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Within the emerging age of lunar exploration, optimizing transfer trajectories is a fundamental measure toward achieving more economical and efficient lunar missions. This study addresses the possibility of reducing the fuel cost of two-impulse Earth–Moon transfers in a four-body model with the Earth, the Moon, and the Sun as primaries. Lawden's primer vector theory is applied within this framework to derive a set of necessary conditions for a fuel-optimal trajectory. These conditions are used to identify which trajectories from an existing database could benefit from the insertion of an additional intermediate impulse. More than 10,000 three-impulse transfers are computed with a direct numerical optimization method. These solutions contribute to enriching the database of impulsive trajectories, useful to perform trade-off analyses. While the majority of trajectories exhibit only marginal improvements, a significant breakthrough emerges for transfers featuring an initial gravity assist at the Moon. Implementing a corrective maneuver after the lunar encounter yields substantial reductions in fuel costs. [ABSTRACT FROM AUTHOR]

High scientific value areas on celestial bodies such as the Moon and Mars are often located in hazardous terrains. To achieve safe landing exploration, a novel planning method is proposed, which can ensure that the planned trajectory maintains a user-specified distance from obstacles, thus reducing potential collision risk induced by factors such as the body size and model uncertainties. Firstly, the basic model of the trajectory optimization problem and its convexification version is given. The obstacles are modeled as polynomial functions, based on which the "if-then" obstacle avoidance logic explicitly considering the safe distance, is described as a sum of squares constraint. This constraint formulation applies to any obstacle described by a finite number of polynomials, independent of the specific expression of the polynomials (reflecting the shape of the obstacle). Subsequently, the convexification process for the obstacle avoidance constraint is given. Finally, the sequential sum of squares programming problem for the obstacle avoidance trajectory is established, which boils down to a series of semidefinite programming problems. Simulation results show that the closest distance between the planned trajectory and obstacles strictly satisfies the specified distance constraint, and the trajectory could avoid non-convex obstacles. With the promising convergence properties of underlying convex optimization algorithms, advanced autonomous obstacle avoidance guidance schemes are expected to be formed based on the proposed trajectory planning method. • Mars/Moon landings cannot ignore unknown and hazardous surface environments. • The trajectory obtained by traditional avoidance methods is too close to obstacles. • The safe distance constraint can be described as a sum of squares constraint. • The proposed planning method applies to any obstacle described by polynomials. [ABSTRACT FROM AUTHOR]

This paper proposes an innovative approach for constrained rendezvous trajectory optimization (CRTO) using stochastic optimal feedback control and unscented transform (UT) uncertainty quantification. The method overcomes limitations of traditional deterministic CRTO solutions that suffer from uncontrolled terminal state error growth and severely deteriorated path constraint satisfaction when initial state uncertainty, dynamics uncertainty, and navigation uncertainty are considered. The approach involves constructing a stochastic optimal feedback control (SOFC) problem with chance constraints and introducing linear feedback control to regulate both the mean and variance of terminal state errors. UT is employed to approximately quantify the state errors and their propagation, transforming the SOFC problem into an unconstrained deterministic optimization (UDO) problem. Differential dynamic programming (DDP) is then used to solve the UDO problem. The obtained stochastic optimal solution provides a robust rendezvous trajectory and a corresponding explicit closed-loop guidance law, improving terminal accuracy and path constraint satisfaction in the presence of various considered uncertainties. The influence of different term weights in the objective function on terminal accuracy and path constraint satisfaction is also studied. The effectiveness of the proposed approach is verified through Monte Carlo simulation, demonstrating the robustness of the closed-loop control strategy. The results highlight the potential of the method in enhancing terminal accuracy and path constraint satisfaction in uncertain rendezvous scenarios. • The uncertainty-resilient rendezvous trajectory optimization algorithm provides not only a nominal trajectory but an explicit optimal state-feedback control policy. • Rendezvous trajectory uncertainty caused by initial state errors, dynamics errors, and navigation errors is propagated using an unscented transformation method. • The proposed method considers common input constraints and path constraints in space rendezvous missions. [ABSTRACT FROM AUTHOR]

Low-thrust trajectory design in the context of the three-body problem, particularly in the vicinity of liberation points, is addressed in this paper with a focus on efficiency and accuracy. The Fourier series, previously successful in approximating near-coplanar low-thrust trajectories within two-body dynamics, is applied to the challenging landscape of the three-body problem, marked by instability and nonlinearity. This paper introduces a novel approach for constructing an initial guess trajectory, that combines coast arcs from periodic orbits and intermediate trajectory arcs from other natural dynamical structures. The resulting initial guess trajectory is instrumental in generating efficient initial estimates for the coefficients of the Fourier series. The study encompasses the analysis of homoclinic and heteroclinic transfers in both two-dimensional and three-dimensional scenarios. The approximated trajectories derived from this methodology serve as invaluable starting points for high-fidelity optimization solvers, offering promise for enhancing the precision and efficiency of low-thrust transfers in cislunar space. • The shape method based on Fourier series is applied to the initial design of the transfer trajectories between periodic orbits in cislunar space. • An innovative approach to construct a continuous initial guess trajectory is introduced to derive initial guess values for the coefficients of Fourier terms. • An accurate approximation of the optimal transfer trajectory can be rapidly obtained. [ABSTRACT FROM AUTHOR]

Comet Interceptor (Comet-I) was selected in June 2019 as the first ESA F-Class mission and aims to explore a pristine comet, which will visit the inner Solar System for the first time. Comet-I will hitch a ride to a Sun-Earth L2 quasi-halo orbit, as a co-passenger in ESA's M4 ARIEL's launch. At the time of writing, this is scheduled for 2029. It will then remain there awaiting the right departure conditions to leave definitively and intercept a newly discovered comet. Comet-I will be the first mission to be designed and launched without an already identified target. Given the latter, the Comet-I Target Identification Working Group is tasked with the continuous follow-up of newly discovered comets, to analyse their characteristics as virtual targets for Comet-I. The goal of the paper is to inform the decision-making process that will eventually be necessary to commit Comet-I spacecraft to its final target. Within this context, this paper presents a trajectory optimization process based on Primer Vector theory that considers intercept trajectories with multiple revolutions and multiple deep space manoeuvres (DSM). The process is applied to 21 long period comets, which were discovered since July 1, 2020 and whose characteristics are of interest for the Comet-I mission. In fact, one of the latest of these recently discovered comets, C/2023H2, is the most accessible virtual target discovered at the time of writing, with delta-V intercept costs well within Comet-I capabilities. A complete launch window analysis is thus presented for C/2023H2, which accounts not only for the most optimal delta-V intercept transfer, but also considers all launch options that satisfy all Comet-I's current boundary conditions and flyby constraints. A systematic launch window analysis is made possible by a grid search approach on which departure and fly-by dates are fixed and the remaining design variables, including the times for manoeuvres and their Δ v impulse vector, are optimized via Primer Vector theory. This allows to understand the latest possible departure date, but also the sensitivity to departure and arrival dates of key encounter characteristics such as relative fly-by speed, the solar aspect angle and the Earth distance. • A Primer Vector Theory-based trajectory optimization tool is shown, tailored for ESA's Comet Interceptor (Comet-I). • The method supports trajectories with multiple revolutions around the Sun and multiple impulses. • The method allows rapid grid search over the search space, finding the lowest cost trajectory and other notable solutions. • A novel analysis of the launch window is presented, contributing to the decision-making process of Comet-I. • A multi-manoeuvre strategy benefits Comet-I's trajectory design, lowering the delta-V cost and improving mission flexibility. [ABSTRACT FROM AUTHOR]

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal. [ABSTRACT FROM AUTHOR]

With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent. [ABSTRACT FROM AUTHOR]

Video stabilization plays a pivotal role in enhancing video quality by eliminating unwanted jitter in shaky videos. This paper introduces a novel video stabilization algorithm that leverages low‐rank constraint and trajectory optimization to effectively eliminate undesirable motion and generate stabilized videos. In the proposed algorithm, a low‐rank constraint regularization term is incorporated to enhance the smoothness of motion trajectories. Additionally, a predictive path smoothness term is integrated to ensure the consistency of motion across neighbouring frames. To address the problem of excessive cropping resulting from aggressive smoothing, a flexible local window strategy that emphasizes local motion relationships within the trajectories is introduced. The experimental results show that, compared to other excellent video stabilization algorithms, the proposed algorithm improves the stability metric by approximately 2.3%. Furthermore, in the stabilized videos generated by the algorithm, an approximate improvement of 2.18 dB in average image temporal fidelity and a 5.7% increase in average structural similarity between adjacent frames are achieved. The code that implements the proposed method is publicly accessible at https://github.com/CZS0319/VS_Low‐Rank_Constraint_Trajectory_Optimization. [ABSTRACT FROM AUTHOR]

Lagrangian particle dispersion models are indispensable tools for the study of atmospheric transport processes. However, Lagrangian transport simulations can become numerically expensive when large numbers of air parcels are involved. To accelerate these simulations, we made considerable efforts to port the Massive-Parallel Trajectory Calculations (MPTRAC) model to graphics processing units (GPUs). Here we discuss performance optimizations of the major bottleneck of the GPU code of MPTRAC, the advection kernel. Timeline, roofline, and memory analyses of the baseline GPU code revealed that the application is memory-bound, and performance suffers from near-random memory access patterns. By changing the data structure of the horizontal wind and vertical velocity fields of the global meteorological data driving the simulations from structure of arrays (SoAs) to array of structures (AoSs) and by introducing a sorting method for better memory alignment of the particle data, performance was greatly improved. We evaluated the performance on NVIDIA A100 GPUs of the Jülich Wizard for European Leadership Science (JUWELS) Booster module at the Jülich Supercomputing Center, Germany. For our largest test case, transport simulations with 108 particles driven by the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 reanalysis, we found that the runtime for the full set of physics computations was reduced by 75 %, including a reduction of 85 % for the advection kernel. In addition to demonstrating the benefits of code optimization for GPUs, we show that the runtime of central processing unit (CPU-)only simulations is also improved. For our largest test case, we found a runtime reduction of 34 % for the physics computations, including a reduction of 65 % for the advection kernel. The code optimizations discussed here bring the MPTRAC model closer to applications on upcoming exascale high-performance computing systems and will also be of interest for optimizing the performance of other models using particle methods. [ABSTRACT FROM AUTHOR]

High vertical jumping motion, which enables a humanoid robot to leap over obstacles, is a direct reflection of its extreme motion capabilities. This article proposes a single sequential kino-dynamic trajectory optimization method to solve the whole-body motion trajectory for high vertical jumping motion. The trajectory optimization process is decomposed into two sequential optimization parts: optimization computation of centroidal dynamics and coherent whole-body kinematics. Both optimization problems converge on the common variables (the center of mass, momentum, and foot position) using cost functions while allowing for some tolerance in the consistency of the foot position. Additionally, complementarity conditions and a pre-defined contact sequence are implemented to constrain the contact force and foot position during the launching and flight phases. The whole-body trajectory, including the launching and flight phases, can be efficiently solved by a single sequential optimization, which is an efficient solution for the vertical jumping motion. Finally, the whole-body trajectory generated by the proposed optimized method is demonstrated on a real humanoid robot platform, and a vertical jumping motion of 0.5 m in height (foot lifting distance) is achieved. [ABSTRACT FROM AUTHOR]

This article develops two metaheuristics optimization techniques, Grey Wolf Optimizer (GWO) and Harris Hawks Optimization (HHO), to handle multi-objective optimal power flow (MOOPF) issues. Multi Objective GWO (MOGWO) and Multi Objective HHO (MOHHO) are the names of the developed techniques. By combining these optimization techniques with Pareto techniques, the non-dominated solution set can be obtained. These developed approaches are characterized by simplicity and have few control parameters. Fuel cost, emissions, real power losses, and voltage deviation were the four objective functions considered. The theories used to determine the best compromise solution and organize the Pareto front options are the fuzzy membership equation and the crowding distance approach, respectively. To validate and evaluate the performance of the presented techniques, two standard IEEE bus systems—30-bus and 57-bus power systems—were proposed. Bi, Tri, and Quad objective functions with 21 case studies are the types of objective functions and the scenarios that were applied in this paper. As compared to the results of the most recent optimization techniques documented in the literature, the comparative analysis results for the proposed methodologies demonstrated the superiority and robustness of MOGWO and MOHHO. [ABSTRACT FROM AUTHOR]

This study presents a novel framework of mission planning for actively removing multiple space debris in low Earth orbit. The associated problem is formulated as a multi-objective optimization problem to minimize the total velocity increments while maximizing the Hazard Criticality Index (HCI) of the candidate target debris. The main idea is based on the Random-Key (RK) encoding scheme that effectively converts a continuous search space into a discretized solution space representing the target debris and visiting sequence. With the RK encoding technique, the proposed framework can obtain the optimal solutions for target debris selection for removal, removal sequences, and other design variables for computing orbital transfer costs simultaneously in a systematic way. In order to demonstrate these favorable characteristics, the proposed framework is integrated with four well-known multi-objective Evolutionary Algorithms (EAs) to solve mission planning problems for the active removal of multiple space debris in low Earth orbit. The framework integrated with NSGA-II, compared with other EAs, shows the best performance in terms of optimality and diversity of non-dominated solutions. Moreover, the optimization results indicate that our integrated framework appropriately selects target debris with similar orbital elements and naturally employs the Right Ascension of the Ascending Node (RAAN) drift originating from J2 perturbation to shift the transfer orbit to reduce total velocity increments. The overall results show that the proposed framework is effective in the preliminary mission planning for multiple space debris removal in low Earth orbit. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative approach for generating re-entry trajectories for a reusable winged spacecraft. This approach utilizes Fourier series parametrized control profiles. The Fourier coefficients are derived through a combination of the Improved Search Space Reduction (ISSR) technique and Sequential Quadratic Programming (SQP) optimization methods, which effectively limit the maximum heat rate experienced by the spacecraft. These re-entry trajectories and control profiles are then used to train artificial neural networks, enabling the controller to provide optimal control inputs based on the spacecraft's current altitude and velocity. To validate this methodology, Hardware-in-loop (HIL) simulations are conducted, integrating the designed neural network-based controller with a Texas Instruments TI Delfino TMSF28335 controller and a real-time simulator, the OPAL-RT OP4510. The results of the HIL simulation demonstrate that the generated re-entry trajectory accurately adheres to heat rate and terminal constraints. Additionally, the Fourier series parametrization of control profiles is applied to a high lift-to-drag ratio CAV-H vehicle, showcasing the method's versatility. Furthermore, resilience of proposed method to uncertainties in aerodynamic coefficients and atmospheric density is also demonstrated. The results show that the proposed method is generic and exhibits robustness to uncertainties. [ABSTRACT FROM AUTHOR]

Biodiesel stands out as a promising contender in the quest for renewable energy solutions, offering a greener alternative to traditional fossil fuels. Derived primarily from the transesterification of vegetable oils or animal fats, biodiesel offers an eco-friendly energy avenue with a minimized carbon footprint. Catalysts are central to the success of this process, which significantly enhance yield rates. Geopolymers, traditionally associated with construction applications due to their inorganic nature, have been derived from aluminosilicate sources activated using alkaline solutions. However, recent advancements spotlight geopolymers in a new light, emphasizing their prospective role as nanocatalytic agents for biodiesel synthesis. This paradigm shift suggests improved production efficiency and an innovative method of repurposing industrial waste. This study centers on the pioneering application of geopolymers, fortified with magnetite, as potent heterogeneous catalysts for biodiesel generation from soybean and safflower oils. By leveraging a meticulously crafted geopolymer matrix—consisting of metakaolin, sodium hydroxide, and magnetite—this research replaced traditional catalysts with this advanced nanostructured geopolymer variant in the biodiesel methylation process. The research delved deep to ascertain the prime synthesis conditions. Furthermore, utilizing cutting-edge machine learning methodologies provided an analytical lens to navigate the extensive experimental data, thereby fine-tuning the optimization trajectory. One of the salient takeaways from this research is the validation that geopolymer catalysts, rooted in kaolinite, can be ingeniously tailored to ensure elevated biodiesel yields across a spectrum of oil sources, underscoring their unparalleled efficiency and versatility in the biofuel domain. [ABSTRACT FROM AUTHOR]

Two problems exist in the study of the trajectory optimization problem of powered hypersonic gliding vehicles (HGVs) due to insufficient consideration of the overall design constraints as well as the strong couplings among relevant disciplines: (1) the engine and thrust models are not compatible with the existing HGV; (2) configuration parameters of the HGV are not included as design variables during trajectory optimization (i.e., propulsion discipline is decoupled in the process of the HGV configuration design), thus failing to fully explore the effect of power to improve the performance of the HGV. Therefore, the application of multidisciplinary design optimization (MDO) in the overall design of powered HGVs should be investigated. First, a MDO task analysis and a multidisciplinary model analysis are carried out for the powered HGV. Second, the multidisciplinary optimization problem is defined, and the couplings between disciplines of the powered HGV are analyzed so that a six-discipline model is established that is suitable for the overall design process, including the parameterized configuration geometry, aerodynamics, propulsion, mass properties, trajectory, and aerodynamic heat/thermal protection system (TPS). Finally, a surrogate model is used to replace the time-consuming accurate model, and numerical optimization examples verify the effectiveness of the method. The optimization results show that the method has a good convergence speed, which increases the gliding range of the optimized vehicle by 8.37%. In addition, by decoupling the propulsion discipline, the validation shows that the coupled propulsion discipline during the overall design can increase the range of the powered HGV by 3.87% compared to the powered HGV optimized with the decoupled propulsion discipline. The work done in this paper provides a new design idea for the overall design of a powered HGV. [ABSTRACT FROM AUTHOR]

This paper presents a joint synthesis algorithm of trajectory and controlled invariant funnel (CIF) for locally Lipschitz nonlinear systems subject to bounded disturbances. The CIF synthesis refers to a procedure of computing controlled invariance sets and corresponding feedback gains. In contrast to existing CIF synthesis methods that compute the CIF with a predefined nominal trajectory, our work aims to optimize the nominal trajectory and the CIF jointly to satisfy feasibility conditions without the relaxation of constraints and obtain a more cost‐optimal nominal trajectory. The proposed work has a recursive scheme that mainly optimize trajectory update and funnel update. The trajectory update step optimizes the nominal trajectory while ensuring the feasibility of the CIF. Then, the funnel update step computes the funnel around the nominal trajectory so that the CIF guarantees an invariance property. As a result, with the optimized trajectory and CIF, any resulting trajectory propagated from an initial set by the control law with the computed feedback gain remains within the feasible region around the nominal trajectory under the presence of bounded disturbances. We validate the proposed method via two applications from robotics. [ABSTRACT FROM AUTHOR]

This work investigates a downlink nonorthogonal multiple access (NOMA) scheme with unmanned aerial vehicle (UAV) aided wireless communication, where a single UAV was regarded as an air base station (ABS) to communicate with multiple ground users. Considering the constraints of velocity and maneuverability, a UAV energy efficiency (EE) model was proposed via collaborative design resource allocation and trajectory optimization. Based on this, an EE maximization problem was formulated to jointly optimize the transmit power of ground users and the trajectory of the UAV. To obtain the optimal solutions, this nonconvex problem was transformed into an equivalent convex optimization problem on the basis of three user clustering algorithms. After several alternating iterations, our proposed algorithms converged quickly. The simulation results show an enhancement in EE with NOMA because our proposed algorithm is nearly 99.6% superior to other OMA schemes. [ABSTRACT FROM AUTHOR]

Metaheuristic techniques are capable of representing optimization frames with their specific theories as well as objective functions owing to their being adjustable and effective in various applications. Through the optimization of deep learning models, metaheuristic algorithms inspired by nature, imitating the behavior of living and non-living beings, have been used for about four decades to solve challenging, complex, and chaotic problems. These algorithms can be categorized as evolution-based, swarm-based, nature-based, human-based, hybrid, or chaos-based. Chaos theory, as a useful approach to understanding neural network optimization, has the basic idea of viewing the neural network optimization as a dynamical system in which the equation schemes are utilized from the space pertaining to learnable parameters, namely optimization trajectory, to itself, which enables the description of the evolution of the system by understanding the training behavior, which is to say the number of iterations over time. The examination of the recent studies reveals the importance of chaos theory, which is sensitive to initial conditions with randomness and dynamical properties that are principally emerging on the complex multimodal landscape. Chaotic optimization, in this regard, accelerates the speed of the algorithm while also enhancing the variety of movement patterns. The significance of hybrid algorithms developed through their applications in different domains concerning real-world phenomena and well-known benchmark problems in the literature is also evident. Metaheuristic optimization algorithms have also been applied to deep learning or deep neural networks (DNNs), a branch of machine learning. In this respect, the basic features of deep learning and DNNs and the extensive use of metaheuristic algorithms are overviewed and explained. Accordingly, the current review aims at providing new insights into the studies that deal with metaheuristic algorithms, hybrid-based metaheuristics, chaos-based metaheuristics as well as deep learning besides presenting recent information on the development of the essence of this branch of science with emerging opportunities, applicability-based optimization aspects and generation of well-informed decisions. [ABSTRACT FROM AUTHOR]

(99942) Apophis is a potentially hazardous asteroid that will closely approach the Earth on April 13, 2029. Although the likelihood of an impact has been ruled out, this close encounter represents a unique opportunity for planetary science and defense. By investigating the physical and dynamical changes induced by this interaction, valuable insights into asteroid cohesion, strength, and internal structure can be obtained. In light of these circumstances, a fast mission to Apophis holds great scientific importance and potential for understanding potentially hazardous asteroids. To this aim, ESA proposed the mission RAMSES (Rapid Apophis Mission for SEcurity and Safety) to reach Apophis before its close encounter. In this context, the paper focuses on the reachability analysis of (99942) Apophis, examining thousands of trajectories departing from Earth and reaching the asteroid before the fly-by, using a low-thrust spacecraft. A two-layer approach combining direct sequential convex programming and an indirect method is employed for fast and reliable trajectory optimization. The results reveal multiple feasible launch windows and provide essential information for mission planning and system design. [ABSTRACT FROM AUTHOR]

• An efficient algorithm for checking the feasibility and evaluating the flight time between two neighboring resonance orbits is given. • A mixed-encoding optimization model is proposed to select the optimal resonance orbits and plan the path through them. • Transfers from the zero-inclination orbit to the polar orbit in the Jupiter system are simulated and the optimal trajectories via tens of gravity assists are analyzed. Trajectory optimization of orbit transfers via multiple gravity assists is studied to generate the time-optimal trajectory for a large-scale velocity change. The optimal trajectory is considered as a combination of transfers between different resonance orbits, and an efficient algorithm for checking the feasibility and evaluating the flight time between two neighboring resonance orbits is given. Then, a mixed-encoding optimization model is proposed to select the optimal resonance orbits and plan the path through them. The transfer from a zero-inclination orbit to a polar orbit in the Jupiter system is simulated and the optimal trajectories via tens of GAs are obtained. The results show the efficiency of the optimization process and the practicability for mission analysis and trajectory design when the third body is small and the velocity change via a single gravity assist is limited. [ABSTRACT FROM AUTHOR]

The irregular satellites within the Jupiter system hold high scientific value due to their potential to contain clues about the early evolution of the solar system. This paper proposes an optimization algorithm for multiple irregular satellites flyby trajectories, which includes the powered gravity assist from Galilean moons. The algorithm is based on beam search and uses virtual trajectories to determine potential flyby targets, solving for trajectories that satisfy constraints on velocity increment, mission duration, and perijove radius. By changing the initial orbital period and optimizing the number of branches with Galilean moon gravity assist during branching, and comparing with the case without Galilean moon gravity assist, the effects of these factors on the number of irregular satellite flybys are summarized. The simulation results show that the algorithm can effectively solve for orbits that flyby multiple irregular satellites for initial orbits of different periods. The three trajectories obtained can serve as references for future missions. [ABSTRACT FROM AUTHOR]

When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms' optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot's movement. These constraints include whole-body dynamics, center of mass constraints, arm's end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

During the process of reverse parking, it is difficult to achieve the ideal reference trajectory while avoiding collision. In this study, with the aim of establishing reference trajectory optimization for automatic reverse parking that smooths and shortens the trajectory length and ensures the berthing inclination angle is small enough, an improved immune moth–flame optimization method based on gene correction is proposed. Specifically, based on the standard automatic parking plane system, a reasonable high-quality reference trajectory optimization model for automatic parking is constructed by combining the cubic spline-fitting method and a boundary-crossing solution based on gene correction integrated into moth–flame optimization. To enhance the model's global optimization performance, nonlinear decline strategies, including crossover and variation probability and weight coefficient, and a high-quality solution-set maintenance mechanism based on fusion distance are also designed. Taking garage No.160 of the Dalian Shell Museum located in Dalian, Xinghai Square, as the experimental site, experiments on automatic parking reference trajectory optimization and tracking control were carried out. The results show that the proposed optimization algorithm provides higher accuracy for reference trajectory optimization and can achieve better tracking control of the reference trajectory. [ABSTRACT FROM AUTHOR]

Trading off the allocation of limited computational resources between front-end path generation and back-end trajectory optimization plays a key role in improving the efficiency of unmanned aerial vehicle (UAV) motion planning. In this paper, a sampling-based kinodynamic planning method that can reduce the computational cost as well as the risks of UAV flight is proposed. Firstly, an initial trajectory connecting the start and end points without considering obstacles is generated. Then, a spherical space is constructed around the topological vertices of the environment, based on the intersections of the trajectory with the obstacles. Next, some unnecessary sampling points, as well as node rewiring, are discarded by the designed position-checking strategy to minimize the computational cost and reduce the risks of UAV flight. Finally, in order to make the planning framework adaptable to complex scenarios, the strategies for selecting different attraction points according to the environment are designed, which further ensures the safe flight of the UAV while improving the success rate of the front-end trajectory. Simulations and real-world experiment comparisons are conducted on a vision-based platform to verify the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Nowadays, the digital development of marine ranching requires a communication system with wide coverage, high transmission rate and stable communication links. It is known that fixed‐wing unmanned aerial vehicles (UAVs) have great advantages in long‐range applications. They have the potential to serve as low‐altitude communication platforms for maritime communication. In this study, with a developmental perspective, considering the intense growth of marine terminals in the future, a new clustering algorithm applied to cluster nonorthogonal multiple access (C‐NOMA) is proposed and its advantages are investigated. In addition, considering the limited energy of marine terminals, combining the wireless power communication (WPC) technology for the UAV to charge terminals, the charging and communication time are optimized with the Lagrange multiplier method and the bisection search method. After completing the above optimization content of charging and communication, combined with the optimization results, it is found the trajectory that maximizes the energy efficiency of the UAV with the convex optimization technique. Experimental results show that the proposed clustering algorithm has good throughput performance, better fairness and lower algorithm complexity, and the proposed trajectory optimization scheme has better energy efficiency. [ABSTRACT FROM AUTHOR]

The deformation of the compliant part is the key factor that affects the production efficiency of the automobile press line. Considering multi-manipulator trajectories and part contours, this paper quantitatively analyzes the deformation of compliant parts when the manipulator transfers sheet metal parts. A multi-objective co-optimization method is proposed to generate smooth motion curves with smaller velocity, acceleration, and jerk. Based on the built-in module of rigid to flex in ADAMS software, some rigid parts such as the end effector and sheet metal part are transformed into compliant ones. And the redundant manipulator in the press line is taken as the representative case to check whether the trajectory performance is conducive to deformation suppression. Simulation result shows that the jerk curve without cusp points is more conducive to suppressing the part deformation than that with cusp points. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present numerical simulations for a series of flight processes for the postlaunching stage of the "balloon-borne UAV system." It includes the balloon further ascent motion after airborne launching. In terms of unmanned aerial vehicles (UAVs), the tailspin state and the charge-out process with an anti-tailspin parachute-assisted suspending are analyzed. Then, the authors conduct trajectory optimization simulations for the long-distance gliding process. Design/methodology/approach: The balloon kinematics model and the parachute Kane multibody dynamic model are established. Using steady-state tailspin to reduced-order analysis and achieving change-out simulation by parachute suspension dynamic model. A reentry optimization control problem is developed and the Radau pseudo-spectral method is used to calculate the glide trajectory. Findings: The established dynamic model and trajectory optimization method can effectively simulate the motion process of balloons and UAVs. The system mass reduction for launching UAVs will not cause damage to the balloon structure. The anti-tailspin parachute can reduce the UAV attack angles effectively. The UAV can glide to the designated target position by adjusting the attack angle and sideslip angle. The farthest flight distance after launching from 20 km height is 94 km and the gliding time is 40 min, which demonstrates the potential application advantage of high-altitude launching. Practical implications: The research content and related conclusions of this article achieve a closed-loop analysis of the flight mission chain for the "balloon-borne UAV system," which provides simulation references for relevant balloon launching experiments. Originality/value: This paper establishes a complete set of numerical simulation models and can effectively analyze various postlaunching behaviors. [ABSTRACT FROM AUTHOR]

This paper introduces a comprehensive motion planning–tracking–safety constraint scheme for a 3D ballbot system. A nonlinear control for the 3D ballbot system is designed based on three separate planes by utilizing extended state observer (ESO) to estimate coupling mechanisms. Three virtual control signals are generated from these distinct planes and can be used for formulating actual control signals. To overcome the complexity of nonlinear motion equations, flatness theory is used to construct the time‐optimal trajectory through an optimization problem, facilitating smooth movement of the ballbot, and obstacle avoidance based on RRT* waypoints. Furthermore, our work manipulates the hierarchical sliding mode controller (HSMC) as the nominal controller to ensure that the ballbot tracks to the optimal trajectory, unifying with the exponential control barrier function (ECBF) to address safety constraints in the body's deflection angle. Through extensive simulations and comparative analysis, the system demonstrates its effectiveness and safe operation in various working conditions. [ABSTRACT FROM AUTHOR]

• A dynamic model for space multi-fingered system with uncertainties and disturbances is formulated. • The dynamic capture domain is designed to envelop space tumbling target for multi-fingered mechanism. • Composite controller based on model predictive control and disturbance observer is proposed. • Proposed method can handle constraints while having better interference rejection. Space debris are usually with diverse geometric shapes and no grappling points. Current capture methods lack the capability to adjust to the target shape dynamically. The capture of space debris remains a significant challenge for operational space missions. This paper presents a multi-finger envelope control strategy based on dynamic capture region in the pre-grasping phase. In the method, the proximal joints of each finger are first determined according to the target shapes. Then, an appropriate joint threshold is selected to generate a set of candidate configurations for the rest of the finger joints, and the effective multi-fingered envelope configuration that resembles the target contour is chosen from candidate finger configurations based on the Hausdorff Distance. Further, the optimal joint trajectories are obtained by minimizing the impact of multi-finger movements on the palm. Considering the model uncertainties, external disturbances and actuator saturation, a composite controller combining disturbance observer and nonlinear model predictive control (NMPC) is proposed for trajectory tracking control of multi-fingered mechanism. The simulation results show that the proposed method can rapidly find an effective envelope capture configuration and has a higher trajectory tracking performance in the presence of uncertainty compared to the Proportional Integral Differential and NMPC controllers. [ABSTRACT FROM AUTHOR]

In this work, we address parametric non–stationary fluid dynamics problems within a model order reduction setting based on domain decomposition. Starting from the optimisation–based domain decomposition approach, we derive an optimal control problem, for which we present a convergence analysis in the case of non–stationary incompressible Navier–Stokes equations. We discretise the problem with the finite element method and we compare different model order reduction techniques: POD–Galerkin and a non–intrusive neural network procedures. We show that the classical POD–Galerkin is more robust and accurate also in transient areas, while the neural network can obtain simulations very quickly though being less precise in the presence of discontinuities in time or parameter domain. We test the proposed methodologies on two fluid dynamics benchmarks with physical parameters and time dependency: the non–stationary backward–facing step and lid–driven cavity flow. • Optimisation-based domain-decomposition algorithm for non-stationary computational fluid dynamics. • Convergence analysis for the domain-decomposition optimal control problem. • Comparison of the intrusive POD-Galerkin and a non-intrusive Neural Networks based approaches. [ABSTRACT FROM AUTHOR]

This work focuses on minimum-time low-thrust orbit transfers from a prescribed low Earth orbit to a specified low lunar orbit. The well-established indirect formulation of minimum-time orbit transfers is extended to a multibody dynamical framework, with initial and final orbits around two distinct primaries. To do this, different representations, useful for describing orbit dynamics, are introduced, i.e., modified equinoctial elements (MEE) and Cartesian coordinates (CC). Use of two sets of MEE, relative to either Earth or Moon, allows simple writing of the boundary conditions about the two celestial bodies, but requires the formulation of a multiple-arc trajectory optimization problem, including two legs: (a) geocentric leg and (b) selenocentric leg. In the numerical solution process, the transition between the two MEE representations uses CC, which play the role of convenient intermediate, matching variables. The multiple-arc formulation at hand leads to identifying a set of intermediate necessary conditions for optimality, at the transition between the two legs. This research proves that a closed-form solution to these intermediate conditions exists, leveraging implicit costate transformation. As a result, the parameter set for an indirect algorithm retains the reduced size of the typical set associated with a single-arc optimization problem. The indirect heuristic technique, based on the joint use of the necessary conditions and a heuristic algorithm (i.e., differential evolution in this study) is proposed as the numerical solution method, together with the definition of a layered fitness function, aimed at facilitating convergence. The minimum-time trajectory of interest is sought in a high-fidelity dynamical framework, with the use of planetary ephemeris and the inclusion of the simultaneous gravitational action of Sun, Earth, and Moon, along the entire transfer path. The numerical results unequivocally prove that the approach developed in this research is effective for determining minimum-time low-thrust Earth–Moon orbit transfers. • Earth–Moon minimum-time low-thrust orbit transfers are investigated. • Problem is formulated as a multiple-arc optimal control problem. • Costate transformations are used, related to different state representations. • Indirect heuristic algorithm with stratified objectives is employed. • Numerical results prove effectiveness of the optimization methodology. [ABSTRACT FROM AUTHOR]

This work considers the trajectory optimization of a kinetic impactor spacecraft, which is sent to collide with a threatening near-Earth asteroid. It is assumed that Earth departure is done impulsively, by the upper stage of a launch vehicle, and is then followed by low-thrust electric propulsion until impact. For a given spacecraft, all of the important decision parameters, such as the date of departure, the direction of the hyperbolic departure, the thrust program, i.e. the history of the low-thrust pointing angles, and the date of impact are all chosen by the optimizer to maximize the perigee radius of the asteroid at its closest approach. Normally a trajectory optimization problem of this type would need to be described by many hundreds of decision parameters, e.g. if formulated to be solved by an optimization package (such as GPOPS) using nonlinear programming. We show that a successful and accurate optimization of the mission is possible using the heuristic method, particle swarm, and only 16 decision parameters, and present results for the deflection of 99942 Apophis at its 2029 close approach. • A particle swarm optimizer finds optimal strategies for asteroid deflection missions. • A novel method uses the system state transition matrix to increase accuracy. • The accuracy of the particle swarm solution is confirmed by using a direct transcription method. • The work shows that deflection by kinetic impact is feasible with present technology. [ABSTRACT FROM AUTHOR]

Accepted by: Konstantinos Nikolopoulos We propose solving non-linear optimization problems by a trajectory method. A parameter is introduced into the optimization problem. For example, a variable in the original formulation is replaced by its squared value. The parameter is the power at which the variable is raised. For a particular value of the parameter (power of 2), the optimal solution is easily obtained. The original optimization problem is defined for another value of the parameter (power of 1). As another example, the means and standard deviations of a function based on a set of variables can be calculated. We multiply the standard deviations by a factor (the parameter) between 0 and 1. Suppose that the problem is easily solvable for zero standard deviations (factor of 0). If we 'slowly' increase the factor, the solution moves to the desired solution for a factor of 1. A trajectory connects the easily obtained solution to the desired solution. We trace the trajectory and the solution for the optimization problem is at the end of the trajectory. The procedure is applied for solving the single facility Weber location problem, and a competitive location problem with good results. [ABSTRACT FROM AUTHOR]

The trajectory optimization of multiple quadcopters for Mars exploration has been a challenging task due to a difficult nonconvex space formed by multiple quadcopters in the flight, the complex dynamics model, and complicated obstacle environments. We propose a distributed optimization algorithm (DiPenOpt) using direct collocation methods to solve the optimization in the nonconvex space. The DiPenOpt algorithm contains a penalty function method to transfer the nonconvex space into a convex one and an iterative optimization strategy employing initial value selection methods to enhance the algorithm's convergence rate. We design a position-tracking controller to ensure that the quadcopters can effectively follow trajectories generated by the DiPenOpt, regardless of initial position deviations and uncertainties. We compare the results of the DiPenOpt with other algorithms and find that DiPenOpt has a faster solution speed and shows superior robustness for trajectory optimization of multiple quadcopters in large and complex environments. The simulation results show that the position-tracking controller can ensure error convergence and stabilize the flight path when the quadcopter has an initial error. Practical Applications: When exploring Mars with multiple quadcopters, ensuring they move efficiently and safely is critical. Think of it like trying to coordinate several quadcopters in a maze-like environment, where every quadcopter needs its own clear path. Our research introduces a new way (DiPenOpt) to help these quadcopters find their best paths, even in complicated surroundings. Our method makes challenging path-finding problems simpler, and we have added tools to make sure quadcopters stick to their paths, even if they start off a little off-course or have uncertain disturbances. Compared to other methods, DiPenOpt is faster and better suited for situations where there are many quadcopters and obstacles. In simple terms, if we were to send a team of quadcopters to explore Mars, our method would make it easier for them to navigate and provide more reliable results, which is crucial for successful space missions. [ABSTRACT FROM AUTHOR]

This paper proposes a trajectory-optimization problem for spacecraft close proximity to a noncooperative target, aiming at the generation of a six-degree-of-freedom (DOF) trajectory with the fuel-optimal objective value and considering multiple constraints on the control magnitude, line-of-sight, and glide-slope. The line-of-sight and glide-slope constraints are coupled between translational and rotational motions. The dual quaternion is an effective method for establishing the translationally and rotationally coupled model, because it can represent the translation and rotation in an integrated manner. Therefore, in this study, the trajectory-optimization problem of spacecraft close proximity coupled with position and attitude is established using dual quaternions. Next, the close-proximity trajectory-optimization problem is converted into a nonlinear programming problem, which can be solved efficiently using well-developed algorithms such as convex optimization. However, the zero-order hold used in the discrete method of convex optimization is an equidistant dispersion, which cannot guarantee the satisfaction of constraints between discrete points. Therefore the pseudospectral convex method is proposed using nonequidistant collocation points to mitigate the problem of constraint violation between discrete points and improve the accuracy and computational efficiency of the algorithm. The proposed algorithm can be applied to tasks such as rendezvous and docking with noncooperative targets and close proximity. Finally, the effectiveness of the proposed method was validated via numerical simulation, and the results were compared with those of the existing approach, GPOPS. The results indicate that the proposed algorithm is superior to GPOPS in computational efficiency and objective values. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to discuss a study aiming to optimize the performance of a DLR-type scramjet engine using computational fluid dynamics (CFD). For this purpose, the response surfaces technique is employed to carry out a multi-objective optimization of the strut geometry used in this engine. In the simulation, strut length and width are considered as design variables. Thrust and combustion efficiency are also constituted as objective parameters. Response surfaces are created using two methods: Kriging and nonparametric regression (NPR). The obtained results indicate a higher accuracy of the Kriging approach. The optimization results show that in the best case the amount of thrust can be increased up to 62.618 N, which is 5.6% more than the base value. Also, the combustion efficiency can be raised to a maximum value of 74.4%, which is 12.37% higher than the base value. In order to determine which design variables have a more significant effect on objective factors, sensitivity analysis has been performed. This analysis revealed that the strut width has more significant effect on the combustion efficiency, while the thrust was more dependent on the strut length. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε , the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data. [ABSTRACT FROM AUTHOR]

Due to the complex aerodynamic effects, the reentry trajectory optimization problem is highly nonlinear. When using sequential convex programming (SCP) methods to solve it, the iteration solution is difficult to converge. To improve this, we propose a hybrid-order soft trust region-based SCP method. We analyze the penalty effect of typical trust regions. Based on the analysis, we develop a hybrid-order soft trust region combining a small-weight first-order component and a higher-order components. To solve the subproblem reliably and effectively, we equivalently reformulate it as a second-order cone programming (SOCP) form through the relaxation technique. Combined with the line search method, we further design an SCP algorithm with guaranteed convergence under some assumptions. In the numerical simulations, the effectiveness and robustness of the proposed method have been verified using a nominal case and 200 Monte Carlo cases. [ABSTRACT FROM AUTHOR]

The problem of a stabilisation system synthesis for the motion of a control object along a given spatial trajectory is considered. The complexity of the problem is that the preset trajectory is defined in the state subspace and not in time. This paper describes a stabilisation system synthesis for motion along a trajectory specified in time and along a trajectory specified in the form of a manifold in a state space. In order to construct a stabilisation system, it is necessary to determine a distance between an object and the given trajectory at each moment in time. For trajectories that are not given in time, the determination of this distance can be ambiguous. An object may be exactly on a trajectory but at a different time. This paper proposes some approaches to solve the problem. One of the approaches is to transform a given trajectory in a state subspace into a trajectory given in time. A description of a universal method to perform this transformation is presented. In order to solve the synthesis problem automatically, without having to analyse the mathematical model of the control object, it is suggested that machine learning control by symbolic regression is used. In computational experiments, examples of stabilisation system syntheses for quadcopter motion along a given spatial trajectory are presented. [ABSTRACT FROM AUTHOR]

We present new stellarator equilibria that have been optimized for reduced turbulent transport using nonlinear gyrokinetic simulations within the optimization loop. The optimization routine involves coupling the pseudo-spectral GPU-native gyrokinetic code GX with the stellarator equilibrium and optimization code DESC. Since using GX allows for fast nonlinear simulations, we directly optimize for reduced nonlinear heat fluxes. To handle the noisy heat flux traces returned by these simulations, we employ the simultaneous perturbation stochastic approximation (SPSA) method that only uses two objective function evaluations for a simple estimate of the gradient. We show several examples that optimize for both reduced heat fluxes and good quasi-symmetry as a proxy for low neoclassical transport. Finally, we run full transport simulations using the T3D stellarator transport code to evaluate the changes in the macroscopic profiles. [ABSTRACT FROM AUTHOR]

The ollie is the base aerial human–board maneuver, foundational to most modern skateboarding tricks. We formulate and solve an optimal control problem of a two-dimensional simplified human model and a rigid body skateboard with the objective of maximizing the height of the ollie. Our solution simultaneously discovers realistic human-applied force trajectories and optimal board geometry. We accomplish this with a direct collocation formulation using a null seed initial guess by carefully modeling the discontinuous aspects of board–ground impact and foot–board friction. This leads to efficient and robust solutions that are 10 times more computationally efficient than prior work on similar problems. The solutions show that ollie height can increase 3% by decreasing the wheelbase and that a smaller board with a back-foot-dominated force strategy can give 12% higher ollies. Our model can be used to inform jump strategy and the effects of changes to the essential board geometry. [ABSTRACT FROM AUTHOR]

Regarding the issue of information freshness in systems that aid in data collection using unmanned aerial vehicles (UAVs), a data collection algorithm that is based on freshness and UAV assistance is proposed. Under the limitations of wireless sensor node communication distance and UAV parameters, the optimization problem of minimizing the average spatial correlation age of information (SCAoI) of all nodes in the area is set up. This problem is solved by optimizing the number of clusters, UAV flight trajectories, and the order of data collection from cluster member nodes. The maximum communication distance of the nodes is used as the cluster formation radius, and the maximum-minimum distance clustering algorithm is used to cluster the nodes in the region to obtain the minimum number of clusters. After it has been proven that the trajectory optimization problem in this study is NP-hard, the ant colony algorithm is applied to obtain the minimum flight time and the corresponding trajectory. By using the greedy algorithm to determine the member nodes in the sequence of data collection for a cluster, the instantaneous SCAoI of the UAV arriving at the cluster head is solved. Simulation results show that the proposed algorithm in this paper can effectively improve the freshness of data and reduce the average SCAoI of the system compared with the algorithm in the comparative literature, reducing the average SCAoI by about 61%. [ABSTRACT FROM AUTHOR]

Assistive aerial teleoperation in unknown environments can be challenging due to the limited sensing range of cameras and the insufficient human–computer interaction. Obstacles occluding the field of view (FOV) of camera reduce the visibility of unknown environments, and it coupled with insufficient pilot instructions directly affect human intention recognition. To address this issue, the planned trajectory generated by assistive teleoperation should align with human intention while considering perception awareness (PA) of the FOV. However, most of the existing works considering PA solely increase the FOV visibility while neglect the human intention constraints. As a result, the PA constraints can cause the heading of the quadrotor to deviate from human expectation, requiring more human–computer interactions to correct it. In this paper, we propose a human-guided motion planner with perception awareness to solve these issues. Specifically, in the path-finding stage, we introduce a spatio-temporal path similarity metric method. This method, combined with motion primitive propagation, finds an initial path that is collision-free and roughly aligned with human intention. In the subsequent trajectory optimization stage, we jointly consider PA with respect to occlusion and human intention constraints, effectively optimizing FOV visibility without deviating the heading of quadrotor from human intention. Extensive experiments and benchmark comparisons demonstrate the effectiveness of our proposed method in solving assistive aerial teleoperation for even novice operators. [ABSTRACT FROM AUTHOR]

UAV has the characteristics of high mobility and easy deployment, which can improve the performance of edge computing system. In order to solve the problems of UAV trajectory optimization, user power allocation and task offloading strategy, this paper proposed a Two-layer Deep Reinforcement Learning (TDRL) algorithm for task offloading. The upper layer used the multi-agent deep reinforcement learning to optimize the trajectories of UAVs, and dynamically allocated the user transmission power to improve the transmission rate of the network. The lower layer used multiple parallel deep neural networks to generate the optimal offloading decision to minimize network latency and energy consumption. The simulation results show that the proposed algorithm enables UAVs to track user movement, significantly reduces system latency and energy consumption, and provides users with better task offloading services. [ABSTRACT FROM AUTHOR]

The problem of effectively controlling the traction of an all-wheel drive wheeled robot after its sharp turn caused by the sudden appearance of an extended obstacle in the robot path has been solved. It is assumed that, in the course of steering, the robot body is parallel to the obstacle and its front wheels are aligned. The task is to ensure acceleration along the obstacle, while avoiding a side collision with it. The solution is based on the linear tangent law adapted to phase restrictions. On a finite time interval, the speed of wheel rotation is obtained in the course of lateral motion in the drift mode and the subsequent acceleration on the verge of slipping along a straight line that is as close as possible to the boundary of the obstacle. The corresponding trajectory is also shown. The dependence of the longitudinal speed developed at the end of the maneuver on the initial distance to the obstacle and the maneuver time is studied. The left-side limits of the wheel angular acceleration and the power at the end of the sliding segment are determined. The found trajectory is compared with some other trajectories consisting of a curved and a straight segment. Numerical calculations show that the former trajectory is more effective. [ABSTRACT FROM AUTHOR]

Building and maintaining a catalog of resident space objects involves several tasks, ranging from observations to data analysis. Once acquired, the knowledge of a space object needs to be updated following a dedicated observing schedule. Dynamics mismodeling and unknown maneuvers can alter the catalog's accuracy, resulting in uncorrelated observations originating from the same object. Starting from two independent orbits, this work presents a convex-optimization based approach to find admissible maneuvers to correlate the two orbits. In case of a feasible maneuver detection, the time profile of the maneuver is estimated, exploiting the conjugate unscented transform to map the orbits' covariance into the maneuver uncertainty. It is also shown that a careful choice of the coordinate system is pivotal to obtain the solution in one iteration, without successive convexification. • Maneuver estimation can be posed as a fuel-optimal second-order cone problem. • If a maneuver is feasible, it is detected in milliseconds. • The conjugate unscented transform transfers the state covariance into a maneuver profile. • Specific coordinates allow polynomial runtime and global convergence. • The maneuver time profile is estimated with knowledge of only the onboard propulsion system. [ABSTRACT FROM AUTHOR]

Human desire to explore Mars has persisted throughout history, and especially, it is required to achieve precise and safe landings on the Martian surface. The atmospheric entry phase plays a crucial role in ensuring mission success, and active research on entry guidance methods continues. This paper presents a comparative study between the two primary types of entry guidance methods: (1) path planning-tracking and (2) predictor–corrector methods by applying them to solve two distinct Mars atmospheric entry guidance problems: (a) robotic entry and (b) human entry missions. Optimization approaches are adopted for the path planning-tracking method. After generating an optimal reference trajectory based on a mission concept-defined cost function, the receding horizon control method is employed to minimize tracking errors. In the predictor–corrector method, two parameterized entry guidance approaches are explored and analyzed. A well-known numerical predictor–corrector method that utilizes a parameterized bank angle profile for entry trajectory generation is tested to demonstrate its robustness against random dispersions. On the other hand, another predictor–corrector type guidance method that assumes a polynomial shape to shape an altitude profile over range-to-go is adopted to show its range control capability. Numerical simulations reveal the pros and cons of each type of entry guidance method, and a suitable guidance method for each Mars entry mission is identified depending on the mission concept and requirements. The analysis results provide future work. • A comparative study between path planning-tracking and predictor-corrector guidance methods. • Mars atmospheric entry problem for robotic and human landing missions. • Optimization approaches for reference trajectory generation and path tracking. • Numerical predictor-corrector based on parameterized bank angle and altitude profiles. [ABSTRACT FROM AUTHOR]

In the past, space trajectory optimisation was limited to the design of space transfers to single destinations. More recent mission concepts however present the challenge to target multiple destinations; be it for science, exploration or even exploitation. This paper focuses on space trajectories that aim to visit multiple main-belt asteroids with one single spacecraft. The trajectory design of these missions is complicated by the fact that the asteroid sequence is not known a priori, but it is the objective of the optimisation problem. Usually, these problems are tackled as global optimisation problems under the formulation of Mixed-Integer Non-Linear Programming, on which the design variables assume both continuous and discrete values. However, beyond the aim of finding the global optimum, mission designers are usually interested in providing a wide range of mission design options reflecting the multi-modality of the problem. The paper describes a multi-fidelity design pipeline that enables a consistent exploration of all feasible asteroid tours with a given set of mission boundary conditions (e.g., launch window, mission duration, Δ v). The consistency of the exploration is ensured by a deterministic search scheme which retains the n -best paths at each stage of a dynamic programming process. Bellman's principle of optimality is made applicable by transcribing the search space into a series of discretized events defined by asteroid index and fly-by epoch, as well as ensuring optimal substructure property with the choice of transfer model and design variables. The method is deployed to explore asteroid tour opportunities visiting 10 or more main belt objects and mission boundary conditions compatible with medium or discovery class missions. 79 million such trajectories are found with launch date in 2037. • A multi-fidelity pipeline is proposed to deal with the challenge of trajectories that fly-by many main belt asteroids. • A novel transcription of the mixed-integer problem allows for an efficient pruning of the search space. • A dynamic programming-based exploration is proposed, keeping the n best solutions for expansion at each unique node. • Novel mission scenarios are presented in the framework of ESA's M-class mission envelope. [ABSTRACT FROM AUTHOR]