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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]

Automated tape placement (ATP) is an important automated process adopted for the fabrication of large composite components. Trajectory planning is the key link of ATP, which directly affects the precision and efficiency of the layup process and the quality of final products. Presently, most existing trajectory optimization methods for ATP focus on smooth surfaces. Nevertheless, as commercial CAD/CAM software generally uses NURBS (Non-uniform Rational B-Splines) for modelling, the difficulty of finding the solution and the low efficiency associated with the calculation process are inevitable. The discrete methods provide alternatives for designing layup trajectories, whereas their accuracy is seldom analysed. Furthermore, a path optimization algorithm for eliminating gap problems while preventing wrinkles on discrete models is rarely reported. In this paper, the adjustment of layup trajectories for ATP is considered on triangular meshes. First, the triangular mesh is reconstructed as a Nagata patch to recover the original geometry with good accuracy. Then, a numerical method for tracing desired paths on the Nagata patch set is provided and the computation efficiency is validated. Next, two optimization methodologies are proposed to improve the layup of composite tapes while avoiding wrinkles. Finally, the presented two strategies are examined on a discrete hyperbolic surface and a discrete freeform surface and some of the results are delivered. [ABSTRACT FROM AUTHOR]

Introduction: We study optimization methods for poses and movements of chained Stewart platforms (SPs) that we call an “Assembler” Robot. These chained SPs are parallel mechanisms that are stronger, stiffer, and more precise, on average, than their serial counterparts at the cost of a smaller range of motion. By linking these units in a series, their individual limitations are overcome while maintaining truss-like rigidity. This opens up potential uses in various applications, especially in complex space missions in conjunction with other robots. Methods: To enhance the efficiency and longevity of the Assembler Robot, we developed algorithms and optimization models. The main goal of these methodologies is to efficiently decide on favorable positions and movements that reduce force loads on the robot, consequently minimizing wear. Results: The optimized maneuvers of the interior plates of the Assembler result in more evenly distributed load forces through the legs of each constituent SP. This optimization allows for a larger workspace and a greater overall payload capacity. Our computations primarily focus on assemblers with four chained SPs. Discussion: Although our study primarily revolves around assemblers with four chained SPs, our methods are versatile and can be applied to an arbitrary number of SPs. Furthermore, these methodologies can be extended to general overactuated truss-like robot architectures. The Assembler, designed to function collaboratively with several other robots, holds promise for a variety of space missions. [ABSTRACT FROM AUTHOR]

Štore Steel Ltd. produces more than 200 different types of steel with a continuous caster installed in 2016. Several defects, mostly related to thermomechanical behaviour in the mould, originate from the continuous casting process. The same casting speed of 1.6 m/min was used for all steel grades. In May 2023, a project was launched to adjust the casting speed according to the casting temperature. This adjustment included the steel grades with the highest number of surface defects and different carbon content: 16MnCrS5, C22, 30MnVS5, and 46MnVS5. For every 10 °C deviation from the prescribed casting temperature, the speed was changed by 0.02 m/min. During the 2-month period, the ratio of rolled bars with detected surface defects (inspected by an automatic control line) decreased for the mentioned steel grades. The decreases were from 11.27 % to 7.93 %, from 12.73 % to 4.11 %, from 16.28 % to 13.40 %, and from 25.52 % to 16.99 % for 16MnCrS5, C22, 30MnVS5, and 46MnVS5, respectively. Based on the collected chemical composition and casting parameters from these two months, models were obtained using linear regression and genetic programming. These models predict the ratio of rolled bars with detected surface defects and the length of detected surface defects. According to the modelling results, the ratio of rolled bars with detected surface defects and the length of detected surface defects could be minimally reduced by 14 % and 189 %, respectively, using casting speed adjustments. A similar result was achieved from July to November 2023 by adjusting the casting speed for the other 27 types of steel. The same was predicted with the already obtained models. Genetic programming outperformed linear regression. [ABSTRACT FROM AUTHOR]

This paper presents an innovative approach to optimize the trajectories of a robotic surveillance system, employing three different optimization methods: genetic algorithm (GA), particle swarm optimization (PSO), and pattern search (PS). The research addresses the challenge of efficiently planning routes for a LiDAR-equipped mobile robot to effectively cover target areas taking into account the capabilities and limitations of sensors and robots. The findings demonstrate the effectiveness of these trajectory optimization approaches, significantly improving detection efficiency and coverage of critical areas. Furthermore, it is observed that, among the three techniques, pattern search quickly obtains feasible solutions in environments with good initial trajectories. On the contrary, in cases where the initial trajectory is suboptimal or the environment is complex, PSO works better. For example, in the high complexity map evaluated, PSO achieves 86.7% spatial coverage, compared to 85% and 84% for PS and GA, respectively. On low- and medium-complexity maps, PS is 15.7 and 18 s faster in trajectory optimization than the second fastest algorithm, which is PSO in both cases. Furthermore, the fitness function of this proposal has been compared with that of previous works, obtaining better results. [ABSTRACT FROM AUTHOR]

Airlines optimize flight trajectories in order to minimize their operational costs, of which fuel consumption is a large contributor. It is known that flight trajectories are not fuel-optimal because of airspace congestion and restrictions, safety regulations, bad weather and other operational constraints. However, the extent to which trajectories are not fuel-optimal (and therefore CO 2 -optimal) is not well known. In this study, we present two methods for optimizing the flight cruising time by taking best advantage of the wind pattern at a given flight level and for constant airspeed. We test these methods against actual flight trajectories recorded under the In-service Aircraft for a Global Observing System (IAGOS) programme. One method is more robust than the other (computationally faster) method, but when successful, the two methods agree very well with each other, with optima generally within the order of 0.1%. The IAGOS actual cruising trajectories are on average 1% longer than the computed optimal for the transatlantic route, which leaves little room for improvement given that by construction the actual trajectory cannot be better than our optimum. The average degree of non-optimality is larger for some other routes and can be up to 10%. On some routes, there are also outlier flights that are not well optimized; however, the reason for this is not known. [ABSTRACT FROM AUTHOR]

Controlled de-orbiting plays a crucial role in any space mission to ensure landing of a satellite or capsule in the desired location and preventing damage to people and property on the ground caused by debris. The necessary orbital energy reduction from the initial conditions to the re-entry interface can be achieved using a de-orbit burn or exploiting drag modulation as a control mechanism. The current work perfectly fits in this scenario, proposing a novel algorithm to generate minimum-time optimal trajectories for a satellite ballistic de-orbiting from a Low Earth Orbit (LEO) to the atmospheric re-entry interface. The formulation is written in terms of modified equinoctial orbital parameters, particularly suitable for trajectory analysis and optimization, even in cases of oscillatory problems with large time scales as in the de-orbiting problem. The optimization problem is solved with the MATLAB software GPOPS-II using a hp adaptive Gaussian quadrature orthogonal collocation method. It is formulated as a single-stage optimization problem considering the exposed surface as a control variable. The cost function to be minimized is the final time, while the imposition of an event constraint on the altitude at the de-orbit point ensures its value is in an acceptable range. A novel class of solutions is defined for the algorithm implementation to guarantee the desired values of latitude and longitude. It has been used to generate high-precision optimal trajectories and corresponding control variable laws in different conditions. The identification of a common trend of solutions along an infinite-shaped pattern allowed the possibility to model a wide range of missions, involving different initial conditions and satellites. A subsequent Monte Carlo analysis showed the algorithm validity and robustness with a successful outcome on 500 cases and an error less of 0.5% for most of them. • Minimum-time optimal trajectories for satellites ballistic de-orbiting from Low Earth Orbit (LEO). • The formulation in terms of modified equinoctial orbital elements managed the oscillatory nature of the problem. • Identification of a common trend of solutions along an infinite-shaped pattern. • A novel optimization algorithm to ensure the desired geolocation in terms of latitude, longitude, and altitude. • A Monte Carlo analysis showed a successful outcome in over 500 cases in different conditions, with high accuracy. [ABSTRACT FROM AUTHOR]

This paper reviews the novel concept of a controllable variational autoencoder (ControlVAE), discusses its parameter tuning to meet application needs, derives its key analytic properties, and offers useful extensions and applications. ControlVAE is a new variational autoencoder (VAE) framework that combines automatic control theory with the basic VAE to stabilize the KL-divergence of VAE models to a specified value. It leverages a non-linear PI controller, a variant of the proportional-integral-derivative (PID) controller, to dynamically tune the weight of the KL-divergence term in the evidence lower bound (ELBO) using the output KL-divergence as feedback. This allows us to precisely control the KL-divergence to a desired value (set point) that is effective in avoiding posterior collapse and learning disentangled representations. While prior work developed alternative techniques for controlling the KL divergence, we show that our PI controller has better stability properties and thus better convergence, thereby producing better disentangled representations from finite training data. In order to improve the ELBO of ControlVAE over that of the regular VAE, we provide a simplified theoretical analysis to inform the choice of set point for the KL-divergence of ControlVAE. We evaluate the proposed method on three tasks: image generation, language modeling, and disentangled representation learning. The results show that ControlVAE can achieve much better reconstruction quality than the other methods for comparable disentanglement. On the language modeling task, our method can avoid posterior collapse (KL vanishing) and improve the diversity of generated text. Moreover, it can change the optimization trajectory, improving the ELBO and the reconstruction quality for image generation. [ABSTRACT FROM AUTHOR]

Robot motion planning methods based on trajectory optimization can efficiently generate feasible and optimal trajectories by minimizing a suitable cost function, even in high-dimensional spaces. However, the main drawback of these methods lies in their proneness to infeasible local minima, especially in complex environments. To mitigate this issue, we propose a novel motion planning method that represents trajectories as samples from a mixture of continuous-time Gaussian processes (MGP) and employs stochastic optimization in order to update the MGP parameters in a cost-minimizing manner. The contributions of the proposed trajectory optimization method arise from the introduced mixture representation and stochastic gradient estimation, dominantly enabling better exploration of the trajectory space and including nondifferentiable optimizing costs. We evaluated the proposed method in multiple simulation benchmarks featuring 7 degree-of-freedom (DOF) robot arms and a 10DOF mobile manipulator. We also conducted a real-world experiment with a 14DOF dual-arm robot. The experimental results demonstrated that the proposed method achieves higher success rate than several state-of-the-art methods, while the advantages stemming from MGPs and stochastic optimization, like trajectory smoothness, support of nondifferentiable cost functions, multiple trajectory solutions, and the ability to tackle high-dimensional planning problems, are inherently kept. [ABSTRACT FROM AUTHOR]

Class solution template trajectories are used clinically for efficiency, safety, and reproducibility. The aim was to develop class solutions for single cranial metastases radiotherapy/radiosurgery based on intracranial target positioning and compare to patient‐specific trajectories in the context of 4π optimization. Template trajectories were constructed based on the open‐source Montreal Neurological Institute (MNI) average brain. The MNI brain was populated with evenly spaced spherical target volumes (2 cm diameter, N = 243) and organs‐at‐risk (OARs) were identified. Template trajectories were generated for six anatomical regions (frontal, medial, and posterior, each with laterality dependence) based on previously published 4π optimization methods. Volumetric modulated arc therapy (VMAT) treatment plans generated using anatomically informed template 4π trajectories and patientspecific 4π trajectories were compared against VMAT plans from a standard four‐arc template. Four‐arc optimization techniques were compared to the standard VMAT template by placing three spherical targets in each of six anatomical regions of a test patient. This yielded 54 plans to compare various plan quality metrics. Increasing plan technique complexity, the total number of OAR maximum dose reductions compared to the standard arc template for the 6 anatomical classes was 4+/−2 (OFIXEDc) and 7+/−2 (OFIXEDi). In 65.6% of all cases, optimized fixed‐couch positions outperformed the standard‐arc template. Of the three comparisons, the most complex (OFIXEDi) showed the greatest statistical significance compared to the least complex (VMATi) across 12 plan quality metrics of maximum dose to each OAR, V12Gy, total plan Monitor Units, conformity index, and gradient index (p < 0.00417). In approximately 70% of all cases, 4π optimization methods outperformed the standard‐arc template in terms of maximum dose reduction to OAR, by exclusively changing the arc geometry. We conclude that a tradeoff exists between complexity of a class solution methodology compared to patient‐specific methods for arc selection, in the context of plan quality improvement. [ABSTRACT FROM AUTHOR]

This paper presents a trajectory determination and optimization method of multirotors equipped with a single-channel radar to obtain 3D Synthetic Aperture Radar imaging. The result is a realistic trajectory that allows to obtain an imaging of the assumed quality in less time than using a multi-pass trajectory. The optimization criteria, in addition to the cross-range resolution, are the Peak Sidelobe Ratio (PSLR), Integrated Sidelobe Ratio (ISLR), and time of flight. The algorithm is based on a realistic motion model of the radar platform. This paper presents all the steps of the algorithm and provides simulation results that show its practical applicability. The advantage of the presented approach over the existing ones is indicated and further research directions are proposed. [ABSTRACT FROM AUTHOR]

Placing robotsoutside controlled conditions requires versatile movement representations that allow robots to learn new tasks and adapt them to environmental changes. The introduction of obstacles or the placement of additional robots in the workspace and the modification of the joint range due to faults or range-of-motion constraints are typical cases, where the adaptation capabilities play a key role for safely performing the robot’s task. Probabilistic movement primitives (ProMPs) have been proposed for representing adaptable movement skills, which are modeled as Gaussian distributions over trajectories. These are analytically tractable and can be learned from a small number of demonstrations. However, both the original ProMP formulation and the subsequent approaches only provide solutions to specific movement adaptation problems, e.g., obstacle avoidance, and a generic, unifying, probabilistic approach to adaptation is missing. In this article, we develop a generic probabilistic framework for adapting ProMPs. We unify previous adaptation techniques, for example, various types of obstacle avoidance, via-points, and mutual avoidance, in one single framework and combine them to solve complex robotic problems. Additionally, we derive novel adaptation techniques such as temporally unbound via-points and mutual avoidance. We formulate adaptation as a constrained optimization problem, where we minimize the Kullback–Leibler divergence between the adapted distribution and the distribution of the original primitive,while we constrain the probability mass associated with undesired trajectories to be low. We demonstrate our approach on several adaptation problems on simulated planar robot arms and seven-degree-of-freedom Franka Emika robots in a dual-robot-arm setting. [ABSTRACT FROM AUTHOR]

An optimal switching heating control strategy based on minimum margin (OS-MM) is proposed for high efficiency, low energy consumption, and high-uniformity fine control in a large-scale vertical quench furnace. This work was stimulated by the need for solving the contradiction between the heating rate and temperature overshoot to achieve a rapid rise in temperature with no overshoot. Based on a three-dimensional (3-D) transient temperature field model of the quenching furnace, the temperature field optimization control problem is transformed into a boundary optimization control problem of rapid heating, whereby it is rigorously proved that the fastest rise in temperature is attained by the full-power heating, which satisfies the bang-bang characteristics. The key parameter that determines the overshoot in the full-power heating mode is introduced and defined as the minimum margin in the temperature-rising process. The analytical expression for solving the minimum margin is calculated by the eigenfunction expansion method and using the strong metal thermal inertia of the internal temperature field, the OS-MM method is designed to stop the heating in advance at a calculated switching point to optimize full-power heating, thus conserving energy by reducing its consumption. The results of simulation and industrial experiments show that this strategy greatly shortens the temperature adjusting time, significantly reduces energy consumption, and effectively improves the control performance indices, such as overshoot and static error in the temperature holding period of the thermal treatment process, as compared to the existing heating methods. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) energy consumption and task completion time are two significant aspects in UAV-enabled Internet of Things (IoT) data acquisition. However, while energy consumption and task completion time are not conflicting constraints, optimizing the former often does not automatically lead to optimization of the latter, and vice-versa. In this paper, we aim to further decrease UAV energy consumption while minimizing UAV task completion time. We propose a novel UAV utility function to describe task completion time for data acquisition and energy consumption for propulsion and receiver's circuits. Then, we formulate a mixed-integer nonconvex utility maximization problem, involving joint optimization of UAV trajectory, velocity, acceleration, task completion time and scheduling of IoT devices for data uploading. To tackle this problem, we decompose the original problem into two sub-problems and propose a two-layer joint task time and trajectory optimization (JTTTO) iterative algorithm, which converges in polynomial time. Accordingly, we apply block coordinate descent (BCD) and successive convex approximation (SCA) algorithms to optimize UAV trajectory, and bisection search algorithm to minimize task completion time, respectively. Simulation results show that the proposed data acquisition scheme significantly decreases the UAV's energy consumption while minimizing task completion time, compared with the other time-minimization schemes. [ABSTRACT FROM AUTHOR]

Firm foot contact is the top priority of climbing robots to avoid catastrophic events, especially when working at height. This study proposes a robust planning and control framework for climbing robots that provides robustness to slippage in unknown environments. The framework includes 1) a center of mass (CoM) trajectory optimization under the estimated contact condition, 2) Kalman filter–like approach for uncertain environment parameter estimation and subsequent CoM trajectory re-planing, and 3) an online weight adaptation approach for whole-body control (WBC) framework that can adjust the ground reaction force (GRF) distribution in real time. Though the friction and adhesion characteristics are often assumed to be known, the presence of several factors that lead to a reduction in adhesion may cause critical problems for climbing robots. To address this issue safely and effectively, this study suggests estimating unknown contact parameters in real time and using the evaluated contact information to optimize climbing motion. Since slippage is a crucial behavior and requires instant recovery, the computation time for motion re-planning is also critical. The proposed CoM trajectory optimization algorithm achieved state-of-art fast computation via trajectory parameterization with several reasonable assumptions and linear algebra tricks. Last, an online weight adaptation approach is presented in the study to stabilize slippery motions within the WBC framework. This can help a robot to manage the slippage at the very last control step by redistributing the desired GRF. In order to verify the effectiveness of our method, we have tested our algorithm and provided benchmarks in simulation using a magnetic-legged climbing robot Manegto.

Differential dynamic programming (DDP) is a well-recognized method for trajectory optimization due to its fast convergence characteristics. However, the original DDP requires a predefined final time and cannot handle nonlinear constraints in optimization. This prohibits the application of DDP for autonomous vehicles due to the heuristic nature of setting a final time beforehand and the existence of inherent physical limits. This article revisits DDP by dynamically optimizing the final time via the first-order optimality condition of the value function and using augmented Lagrangian method to tackle the issue of nonlinear constraints. The resultant algorithm is termed as flexible final time constrained differential dynamic programming (FFT-CDDP). Extensive numerical simulations for a three-dimensional guidance problem are used to demonstrate the working of FFT-CDDP. The results indicate that the proposed FFT-CDDP provides much higher computational efficiency and stronger robustness against the initial solution guess, compared with the off-the-shelf general purpose optimal control software (GPOPS) algorithm. [ABSTRACT FROM AUTHOR]

The control objective of the vertical plane underactuated manipulator (VPUM) is generally to swing up the end point (EP) from the vertical down equilibrium point (VDEP) to the vertical up equilibrium point (VUEP), and finally, to stabilize it at the VUEP. The zone control methods are usually used to achieve this control objective, but the problem of these methods is that they cannot find a unified zone range, and it is easy to appear singularity in the motion process. In this article, for two-link VPUMs, we propose a control strategy based on trajectory planning and optimization, which does not need to divide the zone range, avoids the singularity problem, and can achieve the control objective quickly and smoothly. We design the trajectory by using the time scale method for the active link according to the initial and target states. Then, we obtain the trajectory parameters by employing differential evolution algorithm to ensure that the passive link can also move from the initial states to the target states simultaneously. We design a sliding mode tracking controller to make the active link move along the designed trajectory, so the EP is moved to VUEP. Meantime, we design a sliding mode stabilization controller to ensure that the system is stable at VUEP. Finally, the universality and robustness of the proposed method are proved by simulations. [ABSTRACT FROM AUTHOR]

Joint space trajectory optimization under end-effector task constraints leads to a challenging non-convex problem. Thus, a real-time adaptation of prior computed trajectories to perturbation in task constraints often becomes intractable. Existing works use the so-called warm-starting of trajectory optimization to improve computational performance. We present a fundamentally different approach that relies on deriving analytical gradients of the optimal solution with respect to the task constraint parameters. This gradient map characterizes the direction in which the prior computed joint trajectories need to be deformed to comply with the new task constraints. Subsequently, we develop an iterative line-search algorithm for computing the scale of deformation. Our algorithm provides near real-time adaptation of joint trajectories for a diverse class of task perturbations, such as (i) changes in initial and final joint configurations of end-effector orientation-constrained trajectories and (ii) changes in end-effector goal or way-points under end-effector orientation constraints. We relate each of these examples to real-world applications ranging from learning from demonstration to obstacle avoidance. We also show that our algorithm produces trajectories with quality similar to what one would obtain by solving the trajectory optimization from scratch with warm-start initialization. Most importantly, however, our algorithm achieves a worst-case speed-up of 160x over the latter approach. [ABSTRACT FROM AUTHOR]

Planning high-speed trajectories for UAVs in unknown environments requires algorithmic techniques that enable fast reaction times to guarantee safety as more information about the environment becomes available. The standard approaches that ensure safety by enforcing a “stop” condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown. Moreover, the ad-hoc time and interval allocation scheme usually imposed on the trajectory also leads to conservative and slower trajectories. This work proposes FASTER (Fast and Safe Trajectory Planner) to ensure safety without sacrificing speed. FASTER obtains high-speed trajectories by enabling the local planner to optimize in both the free-known and unknown spaces. Safety is ensured by always having a safe back-up trajectory in the free-known space. The MIQP formulation proposed also allows the solver to choose the trajectory interval allocation. FASTER is tested extensively in simulation and in real hardware, showing flights in unknown cluttered environments with velocities up to 7.8 m/s, and experiments at the maximum speed of a skid-steer ground robot (2 m/s). [ABSTRACT FROM AUTHOR]

This article describes a topological approach to generating families of open- and closed-loop walking gaits for underactuated 2-D and 3-D biped walkers subject to configuration inequality constraints, physical holonomic constraints (e.g., closed-loop linkages), and virtual holonomic constraints (user-defined constraints enforced through feedback control). Our method constructs implicitly defined manifolds of feasible periodic gaits within a state-time-control space that parameterizes the biped’s hybrid trajectories. Since equilibrium configurations of the biped often belong to such manifolds, we use equilibria as “templates” from which to grow the gait families. Equilibria are reliable seeds for the construction of gait families, eliminating the need for random, intuited, or bio-inspired initial guesses at feasible trajectories in an optimization framework. We demonstrate the approach on several 2-D and 3-D biped walkers. [ABSTRACT FROM AUTHOR]

Spacecraft operation in ultra-low Earth orbits (150–250 km) can provide advantages for carrying out traditional tasks. At the same time, the long-term maintenance of spacecraft in such orbits requires the delivery of a propellant supply from the Earth to compensate the aerodynamic drag. An increase in the lifetime can be achieved with air-breathing electric propulsion (ABEP) using outboard atmospheric gases as a propellant. To ensure ABEP operation, it is necessary to provide the required electric power supply and density of ionized gas. At the same time, lowering the orbit altitude or extension of solar arrays is accompanied by a rise in aerodynamic drag, the required thrust to compensate it and, therefore, the required power supply. In this paper the problem of integrated optimization of the trajectories and layout parameters of spacecraft by the criterion of the ABEP power consumption at the fixed spacecraft volume is considered. Variations in the density and composition of the atmosphere and the periods of shading of the spacecraft are taken into account. An analytical solution is obtained depending on generalized parameters that combine such characteristics as the specific power of solar arrays, total thruster efficiency and aerodynamic drag coefficient. The dependencies of the orbit characteristics enabling the long-term maintenance of spacecraft with ABEP on the generalized parameters are determined. • Air-breathing electric propulsion (ABEP), which utilizes the surrounding ionosphere gases, is used to compensate the drag. • The analytical solution is obtained for the optimal parameters of spacecraft with ABEP to minimize the required power. • The minimum allowed number density in the ionization chamber is one of the key parameters. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) is a viable solution to remotely charge several energy receivers (ERs) in areas with intermittent access to a source of energy. However, given the limited available energy in the on-board battery of the UAV, which is mainly required to suffice propulsion energy consumption, the UAV struggles to roam seamlessly for long intervals. A key challenge is thus to efficiently use the limited battery energy, and accordingly prolong the UAV’s endurance, via controlling the underlying associated parameters such as cruising velocity. To further extend the battery lifetime, the UAV can also harness other sources of sustainable energy, such as sunlight, via solar energy harvesting. This paper studies a UAV-enabled WPT system, where a rotary-wing UAV transfers radio frequency (RF) wireless energy to several ERs at known locations. To offer uninterrupted seamless service, the UAV also harvests solar energy using solar panels. Considering the propulsion energy consumption of the UAV, we maximize the minimum average power received among all ERs over a finite cycle duration by jointly optimizing the UAV’s time allocation between harvesting and charging, flight trajectory as well as velocity, and transmit power allocation. Due to the non-convexity of the problem, we utilize the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Numerical results show that the proposed solution outperforms benchmark schemes. [ABSTRACT FROM AUTHOR]

The objective function used in trajectory optimization is often non-convex and can have an infinite set of local optima. In such cases, there are diverse solutions to perform a given task. Although there are a few methods to find multiple solutions for motion planning, they are limited to generating a finite set of solutions. To address this issue, we present an optimization method that learns an infinite set of solutions in trajectory optimization. In our framework, diverse solutions are obtained by learning latent representations of solutions. Our approach can be interpreted as training a deep generative model of collision-free trajectories for motion planning. The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems. [ABSTRACT FROM AUTHOR]

This article presents MADER, a 3-D decentralized and asynchronous trajectory planner for UAVs that generates collision-free trajectories in environments with static obstacles, dynamic obstacles, and other planning agents. Real-time collision avoidance with other dynamic obstacles or agents is done by performing outer polyhedral representations of every interval of the trajectories and then including the plane that separates each pair of polyhedra as a decision variable in the optimization problem. MADER uses our recently developed MINVO basis to obtain outer polyhedral representations with volumes 2.36 and 254.9 times, respectively, smaller than the Bernstein or B-Spline bases used extensively in the planning literature. Our decentralized and asynchronous algorithm guarantees safety with respect to other agents by including their committed trajectories as constraints in the optimization and then executing a collision check-recheck scheme. Finally, extensive simulations in challenging cluttered environments show up to a 33.9% reduction in the flight time, and a 88.8% reduction in the number of stops compared to the Bernstein and B-Spline bases, shorter flight distances than centralized approaches, and shorter total times on average than synchronous decentralized approaches. [ABSTRACT FROM AUTHOR]

Algorithms have been designed for robot motion planning with various adaptability to different problems. However, how to choose the most suitable planner in a scene has always been a problem worthy of research. This paper aims to find the most suitable motion planner for each query under three different scenes and six different queries. The work lies in optimization of sampling-based motion planning algorithms through motion planning pipeline and planning request adapter. The idea is to use the pre-processing of the planning request adapter, to run OMPL as a pre-processer for the optimized CHOMP or STOMP algorithm, and connect through the motion planning pipeline, to realize the optimization of the motion trajectory. The optimized trajectories are compared with original trajectories through benchmarking. The benchmarking determines the most suitable motion planning algorithm for different scenarios and different queries. Experimental results show that after optimization, the planning time of the algorithm is longer, but the efficiency is significantly improved. In the low-complexity scenes, STOMP optimizes the sampling algorithm very well, improves the trajectory quality greatly, and has a higher success rate. CHOMP also has a good optimization of the sampling algorithm, but it reduces the success rate of the original algorithm. However, in more complex scenes, optimization performance of the two optimization methods may not be as good as the original algorithm. In future work, we need to find better algorithms and better optimization algorithms to tackle with complex scenes. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV)-aided two-way relay networks with multiple terrestrial user pairs is investigated, where a fix-wing UAV is served as a two-way relay to assist information transmission. We aim to maximize the total rate of the two-way relay networks while the quality of service (QoS) being guaranteed. A novel relay strategy, namely time-slots pairing, is proposed by exploiting physical-layer network coding (PNC) protocol. Specifically, the UAV receives and buffers the signals from the scheduled user pair when the UAV approaches one of them, then forwards the modulated signal when the UAV is close to the other. As a result, a non-convex total rate maximization problem is formulated by joint trajectory design and resource optimization. To solve this problem, we first decompose it into three sub-problems, and then a three-step iterative algorithm that can effectively handle the non-convex problem with no worse than a polynomial complexity is developed leveraging the block coordinate descent (BCD) technique and successive convex approximation (SCA) method. Simulation results finally illustrate that: 1) Compared with amplify-and-forward (AF) or decode-and-forward (DF) protocols, the proposed design based on PNC protocol can significantly improve the total rate of the two-way relay networks; 2) the time-slots pairing relay strategy always achieves a significant enhancement on performance than the traditional non-time-slots pairing relay strategy; 3) the time-slots pairing relay strategy is more favorable for the mobile PNC relay networks since it can well resist the degradation of the entire networks caused by users’ increased service quality requirements. [ABSTRACT FROM AUTHOR]

In the article, we present an efficient optimization framework that solves trajectory optimization problems by decoupling state variables from timing variables, thereby decomposing a challenging nonlinear programming (NLP) problem into two easier subproblems. With timing fixed, the state variables can be optimized efficiently using convex optimization, and the timing variables can be optimized in a separate NLP, which forms a bilevel optimization problem. The challenge of obtaining the gradient of the timing variables is solved by sensitivity analysis of parametric NLPs. The exact analytic gradient is computed from the dual solution as a by-product, whereas existing finite-difference techniques require additional optimization. The bilevel optimization framework efficiently optimizes both timing and state variables which is demonstrated on generating trajectories for an UAV. Numerical experiments demonstrate that bilevel optimization converges significantly more reliably than a standard NLP solver, and analytical gradients outperform finite differences in terms of computation speed and accuracy. Physical experiments demonstrate its real-time applicability for reactive target tracking tasks. [ABSTRACT FROM AUTHOR]

Recent advances in trajectory replanning have enabled quadrotor to navigate autonomously in unknown environments. However, high-speed navigation still remains a significant challenge. Given very limited time, existing methods have no strong guarantee on the feasibility or quality of the solutions. Moreover, most methods do not consider environment perception, which is the key bottleneck to fast flight. In this article, we present RAPTOR, a robust and perception-aware replanning framework to support fast and safe flight, which addresses these issues systematically. A path-guided optimization approach that incorporates multiple topological paths is devised, to ensure finding feasible and high-quality trajectories in very limited time. We also introduce two perception-aware planning approaches to actively observe and avoid unknown obstacles. A risk-aware trajectory refinement ensures that unknown obstacles which may endanger the quadrotor can be observed earlier and avoid in time. The motion of yaw angle is planned to actively explore the surrounding space that is relevant for safe navigation. The proposed methods are tested extensively through benchmark comparisons and challenging indoor and outdoor aggressive flights. We release our implementation as an open-source package1 for the community. [ABSTRACT FROM AUTHOR]

In maritime wireless communications, unmanned surface vehicles (USVs) can improve coverage and transmission performance due to their agile maneuverability and flexible deployment. This paper considers a USV-enabled maritime wireless network, where a USV is employed to assist the communication between the terrestrial base station and ships. Considering the maritime environment characteristics and earth curvature, we establish the systematic USV kinetics and information transmission models. To guarantee fairness, we aim to maximize the minimum expected throughput overall ships by jointly optimizing the trajectory and communication resource allocation, subject to the constraints of the USV kinetics, safe sailing, breakpoint distances, line-of-sight links, resource allocation, and information-causality. Due to the complexity of the maritime two-ray signal propagation model, we propose a channel approximation method to find an upper bound of the throughput for the original problem. By the problem decomposition, two sub-problems are derived and solved iteratively using successive convex approximation and interior-point methods. Simulation results confirm the effectiveness of the proposed method and show that USV can significantly improve transmission performance in maritime wireless networks. [ABSTRACT FROM AUTHOR]

A tram with an on-board energy storage system is a promising candidate for urban traffic systems. The co-optimization of speed and voltage trajectories for a catenary-supercapacitors hybrid electric tram to minimize energy consumption from traction substations is presented in this article. A source-catenary-load-storage integrated optimization model is proposed to reflect the coupling performance between vehicle dynamics and power sources. Two different methods are designed to address this problem. First, a hierarchical method based on dynamic programming is proposed, and the optimal control problem is recast into two subproblems of speed trajectories optimization and supercapacitors voltage trajectories optimization. Efforts are made to reduce the computation burden. To further investigate the optimality of the hierarchical method, a coupling optimization method is designed using the three-dimensional dynamic programming. The performances of the two methods are evaluated by different track conditions and compared with the only optimizing speed profiles based method, which shows that the proposed methods can improve energy efficiency and reduce the fluctuation of catenary voltage. Meanwhile, the coupling method has lower energy consumption than the hierarchical method, but suffers from more considerable computation time. Finally, the influences of tram weight and dynamic traffic constraints are investigated. [ABSTRACT FROM AUTHOR]

This article presents a novel optimization method that handles collision constraints with complex, non-convex 3D geometries. The optimization problem is cast as a semi-infinite program in which each collision constraint is implicitly treated as an infinite number of numeric constraints. The approach progressively generates some of these constraints for inclusion in a finite nonlinear program. Constraint generation uses an oracle to detect points of deepest penetration, and this oracle is implemented efficiently via signed distance field (SDF) versus point cloud collision detection. This approach is applied to pose optimization and trajectory optimization for both free-flying rigid bodies and articulated robots. Experiments demonstrate performance improvements compared with optimizers that handle only convex polyhedra, and demonstrate efficient collision avoidance between non-convex CAD models and point clouds in a variety of pose and trajectory optimization settings. [ABSTRACT FROM AUTHOR]

IRASSI is an astronomy mission aimed at observing stellar disks and protoplanetary regions using a constellation of five free-flying telescopes operating around the Sun-Earth/Moon L 2 point. In these regions of the cosmos, important chemical/physical phenomena can be observed in the far-infrared, which may advance our current understanding of how pre-biotic conditions in Earth-like planets are formed. The distinguishing aspect of the IRASSI interferometer, however, is its free-flying concept. Rather than relying on a fixed formation configuration and relative position control, IRASSI relies on continuously drifting baselines (e.g., inter-satellite distances) and the knowledge of these baselines during the science observations. However, not all IRASSI targets require the same formation strategy and therefore the planning of the sequence of observation tasks and of the corresponding reconfiguration and science maneuvers has been investigated. The manuscript provides thus an overview of the planning architecture devised for IRASSI, called iSCOUT, composed of different modules: 1) Target Planner Module (TPM), 2) Reconfiguration Module (ReM), 3) Collision- and invisibility Avoidance Module (CAM), 4) Baseline Pattern Module (BPM). The first module (TPM), generates an optimized sequence of targets within a specified window period, taking into account variables such as visibility of the targets at each time step, retargeting time based on the current state, target priority or number of previously observed targets of the same type. The second module, ReM, assigns positions to the telescopes based on the target sequence generated by the TPM. Each translation maneuver is optimized to achieve a specified initial formation configuration, which is both perpendicular to the target direction and three-dimensional. Parameters such as fuel use and fuel-balance as well as ΔV and ΔV-balance are used for optimizing the reconfiguration of the formation. The third module, CAM, ensures that the telescopes following the ReM trajectories do not violate relative proximity restrictions or intervisibility requirements during the reconfiguration. In such an event, the CAM computes a new path in order to avoid collisions and invisibility instances. The last module, BPM, calculates the drifting trajectories that achieve the required patterns in the so-called ' (U, V) -plane' during the science observations. (U, V) -plane projection metrics and several operational constraints are used for the optimization of the trajectories. Each module has been individually optimized and the paper describes the main outcomes thereof. Results concerning and end-to-end iSCOUT simulation for the IRASSI mission are presented and future work goals complete the paper. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) play a significant role in various 5G or Beyond-5G (B5G)-enabled Internet-of-Things (IoT) applications. However, the UAV performance in an air-ground cooperative network is significantly affected by its mobility and air-to-ground (A2G) communication and computation behaviors. In this paper, a UAV-oriented computation offloading system is investigated, where the UAV desires to complete its onboard computation demands with the assistance of a ground edge-computing infrastructure, i.e., a road-side unit (RSU). The objective is to maximize the energy efficiency of the UAV. Specifically, a non-convex constrained optimal control problem is formulated to optimize the overall energy efficiency of UAV by jointly considering the coupled effects of UAV's longitudinal mobility, A2G communication, and computation dynamics. To address the coupled complexity and non-convexity of the original problem, a primal decomposition approach is developed to transform the problem into a convex subproblem and a primary problem, and then a closed-form optimal transmission power control is derived by solving the subproblem, which is dependent on mobility information. By embedding the closed-form optimal power control into the primary problem, a gradient projection-based iterative algorithm is proposed to obtain a joint optimal solution for both the longitudinal acceleration control and the power control, the feasibility and convergence of which is also theoretically proven. Extensive simulations have been conducted to validate the effectiveness of the proposed method in terms of constraint satisfaction and convergence speed, and comparative results also demonstrate that it can outperform other benchmark methods in terms of global energy efficiency. [ABSTRACT FROM AUTHOR]

In principle, the Automatic Identification System (AIS) makes covert rendezvous at sea, such as smuggling and piracy, impossible; in practice, AIS can be spoofed or simply disabled. Previous work showed a means whereby such deviations can be spotted. Here we play the opponent's side, and describe the least-detectable trajectory that the elusive vessel can take. The opponent's route planning problem is formalized as a non-convex optimization problem capitalizing the Kullback-Leibler (KL) divergence between the statistical hypotheses of the nominal and the anomalous trajectories as key performance measure. The velocity of the vessel is modeled with an Ornstein-Uhlenbeck (OU) mean reverting stochastic process, and physical and practical requirements are accounted for by enforcing several constraints at the optimization design stage. To handle the resulting non-convex optimization problem, we propose a globally-optimal and computationally-efficient technique, called the Non-Convex Optimized Stealth Trajectory (N-COST) algorithm. The N-COST algorithm consists amounts to solving multiple convex problems, with the number proportional to the number of segments of the piecewise OU trajectory. The effectiveness of the proposed approach is demonstrated through case studies and a real-world example. [ABSTRACT FROM AUTHOR]

Despite the ongoing advancements in low-thrust propulsion technology and the rise of all-electric satellite platforms, low-thrust spacecraft trajectory optimization remains a complex field of research. Shape-based approximations are predominant in interplanetary applications, but they are generally unsuitable for many-revolution trajectories, common in terrestrial applications. Indirect optimization methods allow for global optimization of many-revolution trajectories, but their mathematical complexity generally requires significant simplifications of the dynamical model, and they must be re-derived for any modification to the system dynamics or constraints. Conversely, direct optimization methods exhibit larger convergence radii and are flexible for application in different problems yet suffer from impractical computational times due to large design vectors. This paper presents a methodology for the optimization of low-thrust many-revolution trajectories, employing a hybrid combination of indirect and direct optimization methods. Similar hybrid approaches have been shown to be highly reliable for minimum-time trajectories. This methodology preserves similar performance while additionally enabling minimum-propellant optimization, through a mechanism that allows for coasting (non-thrusting) arcs, as well as targeting of the final geodetic-longitude. To reduce the propagation load of the methodology, we combine an orbital averaging scheme with a differential evolution algorithm, leading to a global optimization process with a practical computational effort. The analytical nature of the methodology reduces the number of optimization variables and its computational counterpart provides unmatchable flexibility for a configurable force and perturbation model as well as operational constraints fulfilment. The approach is applied to an unperturbed and a J 2 -perturbed GTO-GEO transfer, revealing a 0.03% and a 0.4% error, for time- and propellant-minimization respectively, relative to the reference optimal trajectories. This proves that the method can match the performance of former hybrid approaches while additionally allowing for engine on/off switching. Moreover, the inclusion of the J 2 perturbation shows that, in contrast to indirect methods, it can accommodate modifications to the system dynamics without the need to re-derive the optimal control laws. Furthermore, a superior convergence radius of the optimization problem is demonstrated for the hybrid method, with respect to a reference indirect method, through the simultaneous optimization for minimum-propellant expenditure and final geodetic-longitude targeting. This research constitutes a significant advancement for space mission design and satellite operations, because it simultaneously harnesses the advantages of indirect and direct methods with broader flexibility than the popular indirect approaches and enhanced functionality than the former hybrid methods published in literature. • The hybrid approach is as accurate as indirect methods and as flexible as direct ones. • This hybrid method is able to model coasting arcs and minimize propellant usage. • It simultaneously allows for configurable objectives, constraints, and force models. • It is applicable to different problems without re-deriving the optimal control laws. [ABSTRACT FROM AUTHOR]

This article proposes a computational trajectory optimization framework for solving the problem of multiobjective automatic parking motion planning. Constrained automatic parking maneuver problem is usually difficult to solve because of some practical limitations and requirements. This problem becomes more challenging when multiple objectives are required to be optimized simultaneously. The designed approach employs a swarm intelligent algorithm to produce the tradeoff front along the objective space. In order to enhance the local search ability of the algorithm, a gradient operation is utilized to update the solution. In addition, since the evolutionary process tends to be sensitive with respect to the flight control parameters, a novel adaptive parameter controller is designed and incorporated in the algorithm framework such that the proposed method can dynamically balance the exploitation and exploration. The performance of using the designed multiobjective strategy is validated and analyzed by performing a number of simulation and experimental studies. The results indicate that the present approach can provide reliable solutions and it can outperform other existing approaches investigated in this article. [ABSTRACT FROM AUTHOR]

Dynamic model has broad applications in motion planning, feedforward controller design, and disturbance observer design. Particularly, with the increasing application of model-based control in industrial robots, there has been a resurgence of research interest in accurate identification of dynamic models. However, on the one hand, most existing identification methods directly rely on least squares or weighted least squares (WLS), which suffer from outliers and could lead to physical infeasible solutions. On the other hand, nonlinearity of the friction model is seldom treated in a unified way with linear regression. Moreover, recent researches have shown that proper exciting trajectories are crucial to the identification accuracy, but few of previous works take measurement noise into consideration when optimizing the exciting trajectories. In this article, we propose an iterative approach which integrates WLS, iteratively reweighted least squares with linear matrix inequality constraints, and nonlinear friction models so that the above-mentioned issues can be properly solved altogether. Our research also reveals that performance can be improved by including priori knowledge of measurement noise in the optimization of exciting trajectories. The proposed approach is supported by experimental analysis of four different combinations within the framework on a 6-DoF industrial robot. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the unmanned aerial vehicle (UAV)-aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem’s hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity. [ABSTRACT FROM AUTHOR]

Collision imminent steering is an automotive active safety feature designed to perform an aggressive lane change to avoid a forward collision in the event that the system determines there is insufficient distance to avoid a collision by braking alone.In this paper, a collision imminent steering system is developed using nonlinear model predictive control to perform a lane change at high speed in a highway environment. Two formulations are presented: one formulation to show the theoretical minimum lane change distance within which a safe lane change can be performed, and a second formulation to find the minimum aggressive maneuver for a given lane change distance. The nonlinear model predictive control formulation is posed as a constrained optimization problem, where solutions obey hard safety constraints of the highway environment. Constraint aggregation techniques are analyzed and introduced to monitor the peak tire slip throughout the maneuver. Numerical simulations of a typical luxury sedan in a highway setting show that minimum distance required to change lanes is about half the limit braking distance. Hence, there is a window of feasibility where the vehicle cannot avoid collision by braking alone, but can change lanes safely and maintain a safe trajectory throughout the maneuver. Additional simulations show that an active four wheel steering architecture improves performance over front only steering. [ABSTRACT FROM AUTHOR]

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput. [ABSTRACT FROM AUTHOR]

Periodic cruise has the potential to improve the fuel efficiency of a hypersonic vehicle. However, the optimization of periodic cruise is very difficult and can only be performed inefficiently through trial-and-error due to the parameterized form. In this paper, we systematically optimized the hypersonic periodic cruise using the pseudo-spectral method (PSM) scheme. We specify the main variables as the given forms of periodic functions and parameterize the periodic guidance. The characteristic parameters can then be considered as augmented states to generate augmented dynamics. Therefore, periodic cruise optimization can be directly obtained by using GPOPS (Gauss Pseudo-spectral OPtimization Software). The numerical results demonstrate the effectiveness of the proposed method. The approach in this case study can be generalized to solve similar trajectory optimization problems that can be parameterized in a unified manner. [ABSTRACT FROM AUTHOR]

The optimization of train trajectory and timetable offers two effective methods of energy-efficient operations. Most traditional research optimizes them separately and thus the global optimality cannot be achieved. Previous work has mainly concentrated on minimizing the mechanical energy consumption of trains, not the electrical energy. This paper aims to develop an integrated model to optimize the integrated timetable, which includes both the timetable and the train trajectory achieve the minimum electrical energy consumption. The AC power supply system model is first proposed to calculate the parameters of the AC power supply network. Then, a model is developed to calculate various energy consumptions using power flow calculation. Furthermore, an integrated timetable optimization model for minimum total energy consumption of AC railway system is proposed and the optimal integrated timetable is obtained by the proposed hybrid GA-PSO algorithm. Finally, 5 real-world case studies based on the Lanzhou–Xinjiang high-speed railway are presented. The results show that the proposed integrated model can achieve a reduction in total energy consumption for the entire line up to 14.3% compared with the previous optimization model. The hybrid GA-PSO algorithm achieves the best results compared with the results achieved by the GA and PSO algorithms applied along. [ABSTRACT FROM AUTHOR]

Flattening filter free (FFF) linear accelerators produce a fluence distribution that is forward peaked. Various dosimetric benefits, such as increased dose rate, reduced leakage and out of field dose has led to the growth of FFF technology in the clinic. The literature has suggested the idea of vendors offering dedicated FFF units where the flattening filter (FF) is removed completely and manipulating the beam to deliver conventional flat radiotherapy treatments. This work aims to develop an effective way to deliver modulated flat beam treatments, rather than utilizing a physical FF. This novel optimization model is an extension of the direct leaf trajectory optimization (DLTO) previously developed for volumetric modulated radiation therapy (VMAT) and is capable of accounting for all machine and multileaf collimator (MLC) dynamic delivery constraints, using a combination of linear constraints and a convex objective function. Furthermore, the tongue and groove (T&G) effect was also incorporated directly into our model without introducing nonlinearity to the constraints, nor nonconvexity to the objective function. The overall beam flatness, machine deliverability, and treatment time efficiency were assessed. Regular square fields, including field sizes of 10 × 10 cm2 to 40 × 40 cm2 were analyzed, as well as three clinical fields, and three arbitrary contours with "concave" features. Quantitative flatness was measured for all modulated FFF fields, and the results were comparable or better than their open FF counterparts, with the majority having a quantitative flatness of less than 3.0%. The modulated FFF beams, due to the included efficiency constraint, were able to achieve acceptable delivery time compared to their open FF counterpart. The results indicated that the dose uniformity and flatness for the modulated FFF beams optimized with the DLTO model can successfully match the uniformity and flatness of their conventional FF counterparts, and may even provide further benefit by taking advantage of the unique FFF beam characteristics. [ABSTRACT FROM AUTHOR]

Manifolds are used in almost all robotics applications even if they are not modeled explicitly. We propose a differential geometric approach for optimizing trajectories on a Riemannian manifold with obstacles. The optimization problem depends on a metric and collision function specific to a manifold. We then propose our safe corridor on manifolds (SCM) method of computationally optimizing trajectories for robotics applications via a constrained optimization problem. Our method does not need equality constraints, which eliminates the need to project back to a feasible manifold during optimization. We then demonstrate how this algorithm works on an example problem on SO (3) and a perception-aware planning example for visual–inertially guided robots navigating in three dimensions. Formulating field of view constraints naturally results in modeling with the manifold R 3 × S 2 , which cannot be modeled as a Lie group. We also demonstrate the example of planning trajectories on SE (3) for a formation of quadrotors within an obstacle filled environment. [ABSTRACT FROM AUTHOR]

The objective of professional alpine skiers is to attain the minimal time possible on a slalom course delineated by gates. In recent years, a plethora of scientific research has been carried out to examine this sport, seeking optimal solutions concerning technique, equipment, athlete preparation, and course trajectories, among other factors. Mathematical models describing alpine skiers' movements can be employed to analyze these topics [1]. This study aims to create a model that considers crucial elements associated with athletes' slalom courses. Existing alpine skiing models were analyzed, including: biomechanical models [2][3], aerodynamic models [4][5], trajectory optimization models [6][7]. Based on the literature review, the mathematical model of the alpine skier's run should take into account the following elements: forces acting on the skier (gravity, air resistance, friction force), biomechanical variables (angle of the ski relative to the horizontal plane, angle of the ski edge, position of the skier's body), aerodynamic parameters (skier's body shape, skis, helmet and clothing), route-related variables (terrain profile, slope, type of snow) and individual characteristics of the competitor (weight, height, skill level) and the specificity of the alpine skiing discipline (SL, G, SG, DH ). Based on such a model, strategies for optimizing the alpine skier's ride can be developed. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicles (UAVs) have been widely studied in the communication area to work as aerial base stations, due to the high probability of line of sight (LoS) and high flexibility. However, few works consider fairness for the users, which is one of the most important metrics for a network. In this paper, in order to maximize network capacity with the consideration of fairness, trajectory and scheduling of the mobile UAV aerial base station are jointly optimized. Firstly, the problem of maximizing network capacity with the consideration of fairness is formulated. On account of the coupling relationship of trajectory and scheduling, an alternate iteration approach that contains ant colony algorithm and genetic algorithm are then proposed to solve this intractable problem. Finally, the simulation results demonstrate the fairness enhance of the network and the validity and effectiveness of the proposed optimization approach. [ABSTRACT FROM AUTHOR]

In this paper, a method is presented for antidisturbance constrained control of autonomous underwater vehicles subject to uncertainties and constraints. The uncertainties stem from uncertain hydrodynamic parameters, modeling errors, and unknown forces due to the ocean currents in an underwater environment. An antidisturbance constrained controller is developed by designing a command governor and a disturbance observer. Specifically, the disturbance observer is developed to estimate the lumped disturbance composed of parametric model uncertainties, modeling errors, and unknown environmental forces. The command governor is designed for optimizing command signals in the receding horizon within the state and input constraints. The command governor is formulated as a quadratically constrained quadratic programming problem. To facilitate online implementations, a neurodynamic optimization method based on a one-layer recurrent neural network is employed for solving the quadratic optimization problem subject to inequality constraints with finite-time convergence. The efficacy of the proposed antidisturbance constrained control method for autonomous underwater vehicles is substantiated via simulations and comparisons. [ABSTRACT FROM AUTHOR]