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The ability to change lanes safely, efficiently, and comfortably is an important prerequisite for the application of Connected‐Automated Vehicles (CAVs). Based on the five‐order polynomial trajectory planning for CAVs, the 2D‐Action Asynchronous Lane Change (AALC) trajectory planning model is constructed by further considering the longitudinal and lateral driving action execution time parameters. This is done to improve the applicability of the lane change model and increase the CAV lane change success rate. The continuous collision space algorithm is constructed by determining the continuity condition of collision trajectory parameter solution space through the monotonicity of trajectory curve parameters and collision form classification. AALC trajectory safety judgment is realized through this algorithm. A cooperative lane change trajectory evaluation objective function is constructed, considering multivehicle comfort and efficiency. Finally, the AALC model is solved in the continuous collision space according to the optimal objective function, and the lane change is divided into free, cooperative, and refused according to the optimization. The results indicate that the AALC model achieves the transfer of collision space between lanes through asynchronous process of behavior execution time window, thereby reducing the possibility of vehicle collision. The AALC model reduces the degree of change of cooperative lane change parameters by asynchronous process of behavior, increasing the number of free lane change trajectories by about 17%, effectively reducing the occurrence of lane change refusal, improving the successful rate of lane change, and enhancing the overall evaluation of the lane change. The AALC model realizes the reallocation of collision space between different lanes through asynchronous process, making it more suitable for environments with large differences in vehicle gaps such as ramp merging. The collision‐based trajectory optimization algorithm can quickly obtain the corresponding safety space and optimal trajectory. The maximum calculation time for a single cooperative lane change is 0.073 s, thus enabling real‐time trajectory planning. [ABSTRACT FROM AUTHOR]

Lower-limb exoskeletons (LLEs) can provide rehabilitation training and walking assistance for individuals with lower-limb dysfunction or those in need of functionality enhancement. Adapting and personalizing the LLEs is crucial for them to form an intelligent human–machine system (HMS). However, numerous LLEs lack thorough consideration of individual differences in motion planning, leading to subpar human performance. Prioritizing human physiological response is a critical objective of trajectory optimization for the HMS. This paper proposes a human-in-the-loop (HITL) motion planning method that utilizes surface electromyography signals as biofeedback for the HITL optimization. The proposed method combines offline trajectory optimization with HITL trajectory selection. Based on the derived hybrid dynamical model of the HMS, the offline trajectory is optimized using a direct collocation method, while HITL trajectory selection is based on Thompson sampling. The direct collocation method optimizes various gait trajectories and constructs a gait library according to the energy optimality law, taking into consideration dynamics and walking constraints. Subsequently, an optimal gait trajectory is selected for the wearer using Thompson sampling. The selected gait trajectory is then implemented on the LLE under a hybrid zero dynamics control strategy. Through the HITL optimization and control experiments, the effectiveness and superiority of the proposed method are verified. [ABSTRACT FROM AUTHOR]

Dysregulation of the CXCL12/CXCR4 axis is implicated in autoimmune, inflammatory, and oncogenic diseases, positioning CXCR4 as a pivotal therapeutic target. We evaluated optimized variants of the specific endogenous CXCR4 antagonist, EPI-X4, addressing existing challenges in stability and potency. Our structure-activity relationship study investigates the conjugation of EPI-X4 derivatives with long-chain fatty acids, enhancing serum albumin interaction and receptor affinity. Molecular dynamic simulations revealed that the lipid moieties stabilize the peptide-receptor interaction through hydrophobic contacts at the receptor's N-terminus, anchoring the lipopeptide within the CXCR4 binding pocket and maintaining essential receptor interactions. Accordingly, lipidation resulted in increased receptor affinities and antagonistic activities. Additionally, by interacting with human serum albumin lipidated EPI-X4 derivatives displayed sustained stability in human plasma and extended circulation times in vivo. Selected candidates showed significant therapeutic potential in human retinoblastoma cells in vitro and in ovo , with our lead derivative exhibiting higher efficacies compared to its non-lipidated counterpart. This study not only elucidates the optimization trajectory for EPI-X4 derivatives but also underscores the intricate interplay between stability and efficacy, crucial for delineating their translational potential in clinical applications. [Display omitted] • Lipidated derivatives of EPI-X4 exhibit increased affinity and antagonistic activity towards the CXCR4 receptor. • Lipidated EPI-X4 derivatives exhibit a half-life of >8 h in both blood and plasma. • The lipidated EPI-X4 JM#198 displays a circulation half-life of nearly 4 h in mice. • EPI-X4 JM#198 inhibits retinoblastoma tumor formation in vitro and in ovo. [ABSTRACT FROM AUTHOR]

The Lunar Gateway will represent a primary space system useful for the Artemis program, Earth–Moon transportation, and deep space exploration. It is expected to serve as a staging location and logistic outpost on the way to the lunar surface. This study focuses on low-thrust transfer dynamics, from the Near-Rectilinear Halo Orbit traveled by Gateway to a specified Low-altitude Lunar Orbit (LLO). More specifically, this research addresses two closely-related problems: (i) determination of the minimum-time low-thrust trajectory and (ii) design, implementation, and testing of a guidance and control architecture, for a space vehicle that travels from Gateway to LLO. Orbit dynamics is described in terms of modified equinoctial elements, with the inclusion of all the relevant perturbations, in the context of a high-fidelity multibody ephemeris model. The minimum-time trajectory from Gateway to a specified lunar orbit is detected through an indirect heuristic approach, which uses the analytical conditions arising in optimal control theory in conjunction with a heuristic technique. However, future missions will pursue a growing level of autonomy, and this circumstance implies the mandatory design and implementation of an efficient feedback guidance scheme, capable of compensating for nonnominal flight conditions. This research proposes nonlinear orbit control as a viable and effective option for autonomous explicit guidance of low-thrust transfers from Gateway to LLO. This approach allows defining a feedback law that enjoys quasi-global stability properties without requiring any offline reference trajectory. The overall spacecraft dynamics is modeled and simulated, including attitude control and actuation. The latter is demanded to an array of reaction wheels, arranged in a pyramidal configuration. Guidance, attitude control, and actuation are implemented in an iterative scheme. Monte Carlo simulations demonstrate that the guidance and control architecture at hand is effective in nonnominal flight conditions, i.e. with random starting point from Gateway as well as in case of temporary unavailability of the propulsion system. The numerical results also point out that only a modest propellant penalty is associated with the use of feedback guidance and control in comparison to the minimum-time optimal trajectory. • Minimum-time low-thrust orbit transfer from the Gateway to a low lunar orbit. • The optimal transfer is obtained using the indirect heuristic method. • Feedback guidance is investigated, leveraging nonlinear orbit control. • Nonlinear attitude control and actuation through reaction wheels is designed. • Numerical simulations prove effectiveness of guidance and control architecture. [ABSTRACT FROM AUTHOR]

This study investigates the potential for mitigating the non-CO 2 climate impact induced by air traffic operations at the network scale. Due to the spatiotemporal dependency of non-CO 2 climate impact, aircraft trajectory planning emerges as an operational strategy to mitigate their corresponding effects. However, trajectory planning without considering the interactions between flights is inadequate when studying the actual climate impact mitigation potential. Indeed, meeting climatically oriented aerial traffic requires a holistic view of different aspects of adopting climate-optimal trajectories. In this study, we aim to assess the network-scale effects of full 4D climate-friendly aircraft trajectories. Different indicators are employed to assess air traffic safety, manageability, cost-efficiency, and the environmental impact of optimized routes. Our findings suggest that while optimized trajectories can potentially reduce climate impact, they introduce significant challenges related to air traffic safety, complexity, and demand, especially in sectors in proximity to climate hotspots. These insights highlight the need to develop an advanced mechanism enabling a safe and efficient air traffic management system with minimal climate impact. • Climate-aware flight planning can reduce the climate impact of non-CO 2 emissions. • 4D trajectory optimization is performed for individual flights. • The effects of climate-optimal trajectories on air traffic performance are assessed. • Climate-optimal routes might increase congestion and complexity, jeopardizing safety. [ABSTRACT FROM AUTHOR]

Copyright of Coal Geology & Exploration is the property of Xian Research Institute of China Coal Research Institute and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Headland maneuvering is a crucial part of the field operations performed by autonomous agricultural vehicles (AAVs). While motion planning for headland turning in open fields has been extensively studied and integrated into commercial autoguidance systems, the existing methods primarily address scenarios with ample headland space and thus may not work in more constrained headland geometries. Commercial orchards often contain narrow and irregularly shaped headlands, which may include static obstacles, rendering the task of planning a smooth and collision‐free turning trajectory difficult. To address this challenge, we propose an optimization‐based motion planning algorithm for headland turning under geometrical constraints imposed by headland geometry and obstacles. Our method models the headland and the AAV using convex polytopes as geometric primitives, and calculates optimal and collision‐free turning trajectories in two stages. In the first stage, a coarse path is generated using either a classical pattern‐based turning method or a directional graph‐guided hybrid A* algorithm, depending on the complexity of the headland geometry. The second stage refines this coarse path by feeding it into a numerical optimizer, which considers the vehicle's kinematic, control, and collision‐avoidance constraints to produce a feasible and smooth trajectory. We demonstrate the effectiveness of our algorithm by comparing it to the classical pattern‐based method in various types of headlands. The results show that our optimization‐based planner outperforms the classical planner in generating collision‐free turning trajectories inside constrained headland spaces. Additionally, the trajectories generated by our planner respect the kinematic and control limits of the vehicle and, hence, are easier for a path‐tracking controller to follow. In conclusion, our proposed approach successfully addresses complex motion planning problems in constrained headlands, making it a valuable contribution to the autonomous operation of AAVs, particularly in real‐world orchard environments. [ABSTRACT FROM AUTHOR]

On‐orbit close proximity operations involve robotic spacecraft maneuvering and making decisions for a growing number of mission scenarios demanding autonomy, including on‐orbit assembly, repair, and astronaut assistance. Of these scenarios, on‐orbit assembly is an enabling technology that will allow large space structures to be built in situ, using smaller building block modules. However, like many of these scenarios, robotic on‐orbit assembly involves several technical hurdles, such as changing system models. For instance, grappled modules moved by a free‐flying "assembler" robot can cause significant changes in the combined system inertia, which have cascading impacts on motion planning and control portions of the autonomy stack. Further, on‐orbit assembly and other scenarios require collision‐avoiding motion planning, particularly when operating in a "construction site" scenario of multiple assembler robots and structures. Multiple key technologies that address these complicating factors for autonomous microgravity close proximity operations are detailed in this work, in particular: (1) application of global long‐horizon planning, accomplished using offline and online sampling‐based planner options that consider the system dynamics; (2) adaptation of the recently proposed RATTLE information‐aware planning framework for on‐orbit reconfiguration model learning; and (3) connection with robust control tools to provide low‐level control robustness using current system knowledge. These approaches were demonstrated for an autonomous on‐orbit assembly use case by the RElative Satellite sWarming and Robotic Maneuvering (ReSWARM) experiments using NASA's Astrobee robots on the International Space Station. Results of the ReSWARM experiments are provided along with significant operational and implementation detail discussing the practicalities of hardware implementation and unique aspects of working with the Astrobee free‐flyer robots in microgravity. ReSWARM provides a base set of planning and control tools for robotic close proximity operations, demonstrates them in microgravity, and outlines some of the important hardware aspects that future autonomous free‐flyers will need to consider. [ABSTRACT FROM AUTHOR]

Due to its ability for flexible placement, the Unmanned Aerial Vehicle (UAV) has been widely utilized as an aerial relay to transmit the video streaming data, which is particularly useful in emergency communication networks that lack the communication infrastructure. In this paper, we examine the combined resource optimization and trajectory planning for video services using dynamic adaptive streaming over HTTP (DASH) in the uplink transmission of UAV-based emergency indoor-outdoor communication networks. In detail, a rotary-wing UAV works as an aerial relay, forwarding emergency video collected by indoor users to a remote Base Station (BS). To ensure that the UAV can forward the emergency video data from disaster area with stable video quality, we formulate an optimization problem of maximizing the uplink throughput and video streaming utility for all indoor users, by jointly optimizing the 3D flight trajectory of UAV, playback rate of video, communication time as well as bandwidth allocation, subject to the UAV trajectory constraints, total available bandwidth limitation, video quality variation constraints, as well as information-causality constraints for both UAV relaying and video playing. To tackle the formulated non-convex problem, we present a joint UAV trajectory, bandwidth allocation and video utility (JTBU) algorithm. Specifically, we first decouple the original problem into two sub-issues: 3D UAV trajectory optimization subproblem and resource allocation optimization subproblem. Then the JTBU algorithm alternately optimizes the two subproblems until the convergency is reached. Finally, the numerical results confirm that the proposed algorithm had a higher video streaming utility and system throughput than the baseline methods. [ABSTRACT FROM AUTHOR]

The paper is devoted to trajectory optimization for an inertial object moving in a plane with thrust bounded in magnitude in the presence of external forces. The aim is to maximize the longitudinal terminal velocity with the state constraint satisfied at each time to avoid a lateral collision with an obstacle. The linear tangent law is used as the basis for an algorithm that controls the direction of the thrust. Conditions for the existence of a solution are studied. Constraints on the initial lateral velocity and the time of the motion of the object are obtained. Since the linear tangent law violates the constraint for some motion times, a modified control law is proposed. A transcendental equation is obtained to find a critical value of time above which an undesired collision occurs. The corresponding conjecture is formulated, which allows us to eliminate the ambiguity that arises during the solution process. A method for solving the problem is presented and confirmed by numerical calculations. [ABSTRACT FROM AUTHOR]

A robust trajectory planning method for non-cooperative target rendezvous is investigated based on closed-loop uncertainty analysis. The proposed method addresses the issue that uncertainties affect the optimality of objective functions and the reliability of constraints. Considering multiple uncertainties, a stochastic optimal rendezvous model with a robustness performance index and chance constraints is presented with line-of-sight dynamics. The guidance model, angles-only navigation model and control model are derived, and uncertainty propagation equations for closed-loop GN & C system is proposed by using linear covariance analysis. The stochastic optimal rendezvous model is transformed into a robust optimal rendezvous model by using 3- σ of the random variables. The genetic algorithm is used to solve the robust trajectory planning problem, and a series of examples demonstrate that the robust maneuver solution can be significantly better than the solution corresponding to deterministic trajectory optimization problem. [ABSTRACT FROM AUTHOR]

The recent technology advancements in miniaturizing the primary components of spacecraft allow the classic CubeSats to be considered as a valid option in the design of a deep space scientific mission, not just to support a main typical interplanetary spacecraft. In this context, the proposed ESA M-ARGO mission, whose launch is currently planned in 2026, will use the electric thruster installed onboard of a 12U CubeSat to transfer the small satellite from the Sun–Earth second Lagrangian point to the orbit of a small and rapidly spinning asteroid. Starting from the surrogate model of the M-ARGO propulsion system proposed in the recent literature, this paper analyzes a simplified thrust vector model that can be used to study the heliocentric optimal transfer trajectory with a classical indirect approach. This simplified thrust model is a variation of the surrogate one used to complete the preliminary design of the trajectory of the M-ARGO mission, and it allows to calculate, in an analytical form, the typical Euler–Lagrange equations without singularities. The thrust model is then used to study the performance of a M-ARGO-type CubeSat (MTC) in a different scenario (compared to that of the real mission), in which the small satellite moves along a circular heliocentric orbit in the context of a classic phasing maneuver. In this regard, the work discusses a simplified study of the optimal constrained MTC transfer towards one of the two Sun–Earth triangular Lagrangian points. Therefore, the contributions of this paper are essentially two: the first is the simplified thrust model that can be used to analyze the heliocentric trajectory of a MTC; the second is a novel mission application of a CubeSat, equipped with an electric thruster, moving along a circular heliocentric orbit in a phasing maneuver. [ABSTRACT FROM AUTHOR]

Drone technology has undoubtedly become an integral part of our everyday life in recent years. The business and industrial use of unmanned aerial vehicles (UAVs) can provide advantageous solutions in many areas of life, and they are also optimal for emergency situations and for accessing hard-to-reach places. However, their application poses numerous technological and regulatory challenges to be overcome. One of the weak links in the operation of UAVs is the limited availability of energy. In order to address this issue, the authors developed a novel trajectory planning method for UAVs to optimize energy consumption during flight. First, an "energy map" was created, which was the basis for trajectory planning, i.e., determining the energy consumption of the individual components. This was followed by configuring the 3D environment including partitioning of the work space (WS), i.e., defining the free spaces, occupied spaces (obstacles), and semi-occupied/free spaces. Then, the corresponding graph-like path(s) were generated on the basis of the partitioned space, where a graph search-based heuristic trajectory planning was initiated, taking into account the most important wind conditions including velocity and direction. Finally, in order to test the theoretical results, some sample environments were created to test and analyze the proposed path generations. The method eventually proposed was able to determine the optimal path in terms of energy consumption. [ABSTRACT FROM AUTHOR]

Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes. [ABSTRACT FROM AUTHOR]

We consider a complex control problem: making a monopod accurately reach a target with a single jump. The monopod can jump in any direction at different elevations of the terrain. This is a paradigm for a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimization-based techniques. Reinforcement learning (RL) is an interesting alternative, but an end-to-end approach in which the controller must learn everything from scratch can be non-trivial with a sparse-reward task like jumping. Our solution is to guide the learning process within an RL framework leveraging nature-inspired heuristic knowledge. This expedient brings widespread benefits, such as a drastic reduction of learning time, and the ability to learn and compensate for possible errors in the low-level execution of the motion. Our simulation results reveal a clear advantage of our solution against both optimization-based and end-to-end RL approaches. [ABSTRACT FROM AUTHOR]

The establishment and solution of the inverse kinematic model is the key to improve the efficiency of trajectory optimization. To improve the trajectory smoothness and reduce energy consumption of multi-degree-of-freedom (MDOF) robots, this article presents the time-, jitter-, and energy-optimal trajectory optimization method based on pseudo-attractor and radial basis function neural network. Based on the geometric method, the forward kinematic model of MDOF robots is firstly established. The diversity of inverse kinematic solutions is reduced by determining redundant joints. Combined with the attractor theory, the time-adaptive allocation strategy can automatically endow time information with path points. On this basis, the 7-time polynomial interpolation method is used to fit discrete trajectory points and generate the initial trajectory without singularity points. Affected by the pseudo-attractor, radial basis function neural network is transformed into the improved radial basis function neural network (I-RBFNN) to optimize the initial trajectory. The 2-redundancy planar feeding manipulator (2-RPFM) is introduced to verify the effectiveness of the proposed method. Experiment and simulation results show that the proposed method is available in generating high-performance trajectories, which is beneficial to improve the production efficiency of the auto-body-out-panel stamping line. [ABSTRACT FROM AUTHOR]

This article presents an approach to solve the problem of end-to-end optimization of the low-thrust interplanetary trajectory. At first, the problem of optimizing low-thrust interplanetary trajectory, which passes through the collinear libration points near the planets of departure and arrival is considered. The obtained solutions from this problem can be used as an initial guess to solve the end-to-end optimization problem, i.e., to calculate interplanetary low-thrust trajectories that satisfy the necessary optimality conditions at the match points of the heliocentric and planetocentric segments of trajectory. The minimum-fuel problem is considered. An indirect method based on the maximum principle, and the continuation method is applied to optimize interplanetary spacecraft trajectories. Numerical examples of trajectories from the Earth orbit to the orbit around the destination planet (Mars and Jupiter) are given. The possibility of a significant reduction in the required characteristic velocity is shown in comparison with the estimates obtained by using the zero-radius sphere of influence model. • End-to-end optimization, low-thrust, minimum-fuel trajectory, interplanetary transfer. • Using libration points as an initial guess to figure out the optimal match points of trajectory segments. • Analysing optimality conditions at the match points of the trajectory segments. • Comparison with conventional approach using zero-radius sphere of influence model. • End-to-end trajectory optimization significantly reduces characteristic velocity. [ABSTRACT FROM AUTHOR]

Trajectory optimization is a method of finding the optimal route connecting a start and end point. The suitability of a trajectory depends on not intersecting any obstacles, as well as predefined performance metrics. In the context of unmanned aerial vehicles (UAVs), the goal is to minimize the route cost, in terms of energy or time, while avoiding restricted flight zones. Artificial intelligence techniques, including evolutionary computation, have been applied to trajectory optimization with varying degrees of success. This work explores the use of genetic programming (GP) for 3D trajectory optimization by developing a novel GP algorithm to optimize trajectories in a 3D space by encoding 3D geographic trajectories as function trees. The effects of parameterization are also explored and discussed, demonstrating the advantages and drawbacks of custom parameter settings along with additional evolutionary computational techniques. The results demonstrate the effectiveness of the proposed algorithm, which outperforms existing methods in terms of speed, automaticity, and robustness, highlighting the potential for GP-based algorithms to be applied to other complex optimization problems in science and engineering. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are considered as a promising method to provide service for Internet of Remote Things (IoRT). The incorporation of non-orthogonal multiple access (NOMA) into UAV-assisted networks provides a possibility for massive IoRT devices access. In this paper, we investigate the downlink transmission UAV-assisted wireless communication system based on NOMA in IoRT networks, where UAVs fly vertically to provide services for IoRT devices. In order to transmit as much data as possible, we aim to maximize the energy efficiency (EE) of the UAV by jointly optimizing NOMA user scheduling, UAV trajectory and power allocation. Due to the coupling between variables, the derived problem is mixed integer optimization and non-convex. To solve this challenging problem, we first decouple the variables and split the original problem into three subproblems, i.e., user scheduling, trajectory planning, and power allocation. Then, a double-loop iterative algorithm is proposed to solve the corresponding subproblems based on the Dinkelbach method and successive convex approximation (SCA) techniques. Finally, simulation results give some insights e.g., NOMA outperforms OMA in both spectral efficiency and energy efficiency and the proposed NOMA-based EE-oriented scheme is approximate three times energy efficient than benchmark schemes. [ABSTRACT FROM AUTHOR]

The distribution of internal defects in large-diameter steel pipes can be complicated. Due to the harsh working environments involved, the manual grinding method for correcting such defects is inaccurate, inefficient, and risky. Therefore, in this study, we designed a new internal pipe grinding robot with five degrees of freedom. The grinding trajectory was optimized, and dynamic and kinematic models of the robot were established for the internal space of a pipe with a diameter between 800 and 1,200 mm. The feasibility of the robot was verified by simulation. A trajectory optimization method that focuses on the time, energy, and impact of the trajectory of the grinding robot is proposed. The method uses modified multiobjective particle swarm optimization (MMOPSO) in the robot's trajectory planning module to obtain a Pareto solution set. The analytic hierarchy process is used with the minimum fuzzy entropy (AHP-MFE) method in the decision-making module to select the most appropriate trajectory according to the relevant working conditions. The experimental results validated the space accessibility and trajectory optimization performance. Compared with the original trajectory, operation time was shortened by 15.4%, energy consumption was reduced by 17.5%, and impact was reduced by 12.4%. The experimental error was less than 2.8%. The experimental results showed that the pipe grinding robot is safe and efficient. [ABSTRACT FROM AUTHOR]

When legged robots perform complex tasks in unstructured environments, falls are inevitable due to unknown external disturbances. However, current research mainly focuses on the locomotion control of legged robots without falling. This paper proposes a comprehensive decision-making and control framework to address the falling over of quadruped robots. First, a capturability-based fall prediction algorithm is derived for planar single-contact and 3D multi-contact locomotion with a predefined gait sequence. For safe fall control, a novel contact-implicit trajectory optimization method is proposed to generate both state and input trajectories and contact mode sequences. Specifically, incorporating uncertainty into the system and terrain models enables mitigating the non-smoothness of contact dynamics while improving the robustness of the resulting trajectories. Furthermore, a model-free deep reinforcement learning-based approach is presented to achieve fall recovery after the robot completes a fall. Experimental results demonstrate that the proposed fall prediction algorithm accurately predicts robot falls with up to 95% accuracy approximately 395ms in advance. Compared to classical locomotion controllers, which often struggle to maintain balance under significant pushes or terrain perturbations, the presented framework can autonomously switch to the fall controller approximately 0.06s after the perturbation, effectively preventing falls or achieving recovery with a threefold reduction in touchdown impact velocity. These findings highlight the effectiveness of the proposed framework in enhancing the stability and safety of legged robots in unstructured environments. • The proposed fall prediction approach exhibits significantly enhanced reliability and flexibility. • The proposed controller can autonomously generate contact sequences, reducing damage from falls. • The designed reinforcement learning-method improves efficiency and robustness in fall recovery. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Vibration problems have become one of the most important factors affecting robot performance. To this end, a terminal residual vibration suppression method based on joint trajectory optimization is proposed to improve the accuracy and stability of robot motion. Firstly, based on the characteristics of the friction nonlinearity due to joint coupling and physical feasibility of dynamic parameters, a semidefinite programming method is used to identify dynamic parameters with actual physical meaning, thereby obtaining an accurate dynamic model. Then, based on the result of the residual vibration time domain analysis, a joint trajectory optimization model with the goal of minimizing joint tracking error is established. The Chebyshev collocation method is used to discretize the optimization model. The dynamic model is used as the optimization constraint, and barycentric interpolation is used to obtain the optimized joint motion trajectory. Finally, industrial robot experiments prove that the vibration suppression method proposed in this article can reduce the maximum acceleration amplitude of residual vibration by 62% and the vibration duration by 71%. Compared with the input shaping method, the method proposed in this paper can reduce the terminal residual vibration more effectively and ensure the consistency of running time and trajectory. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive framework for optimising vehicle performance, integrating advanced simulation techniques with optimisation methodologies. The aim is to find the best racing line, as well as the optimal combination of parameters and control inputs to make a car as fast as possible around a given track, with a focus on energy deployment and recovery, active torque distribution and active aerodynamics. The problem known as the Minimum Lap Time Problem is solved using optimal control methods and direct collocation. The solution covers the modelling of the track, vehicle dynamics, active aerodynamics, and a comprehensive representation of the powertrain including motor, engine, transmission, and drivetrain components. This integrated simulator allows for the exploration of different vehicle configurations and track layouts, providing insights into optimising vehicle design and vehicle control simultaneously for improved performance and energy efficiency. Test results demonstrate the effect of active torque distribution on performance under various conditions, enhanced energy efficiency and performance through regenerative braking, and the added value of including parameter optimisation within the optimisation framework. Notably, the simulations revealed interesting behaviours similar to lift-and-coast strategies, depending on the importance of energy saving, thereby highlighting the effectiveness of the proposed control strategies. Also, results demonstrate the positive effect of active torque distribution on performance under various conditions, attributed to the higher utilization of available adherence. Furthermore, unlike a simpler single-track model, the optimal solution required fine control of the active aerodynamic systems, reflecting the complex interactions between different subsystems that the simulation can capture. Finally, the inclusion of parameter optimisation while considering all active systems, further improves performance and provides valuable insights into the impact of design choices. [ABSTRACT FROM AUTHOR]

The benchmarking of CO2 emissions serves as the foundation for the accurate assessment of the environmental impact of air traffic. To calculate the environmental benchmarks of arrival traffic flows with Point Merge System (PMS) patterns, this study proposes a 4D trajectory optimization method that combines data-driven and optimal control models. First, the predominant arrival routes of traffic flows are identified using the trajectory spectral clustering method, which provides the horizontal reference for 4D trajectory optimization. Second, an optimal control model for vertical profiles with point merging topology is established, with the objective of minimizing the fuel–time cost. Finally, considering the complex structure of the PMS, a flexible and adaptable genetic algorithm-based vertical profile nonlinear optimization model is created. The experimental results demonstrate that the proposed method is adaptable to variations in aircraft type and cost index parameters, enabling the generation of different 4D trajectories. The results also indicate an environmental efficiency gap of approximately 10% between the actual CO2 emissions of the arrival traffic flow example and the obtained benchmark. With this benchmark trajectory generation methodology, the environmental performance of PMSs and associated arrival aircraft scheduling designs can be assessed on the basis of reliable data. [ABSTRACT FROM AUTHOR]

Data mining has achieved great success in air traffic management as a technology for learning knowledge from historical data that benefits people. However, data mining can rarely be embedded into the trajectory optimization process since regular optimization algorithms cannot utilize the functional and implicit knowledge extracted from historical data in a general paradigm. To tackle this issue, this research proposes a novel data mining-based trajectory generation method that is compatible with existing optimization algorithms. Firstly, the proposed method generates trajectories by combining various maneuvers learned from operation data instead of reconstructing trajectories with generative models. In such a manner, data mining-based trajectory optimization can be achieved by solving a combinatorial optimization problem. Secondly, the proposed method introduces a majorization–minimization-based adversarial training paradigm to train the generation model with more general loss functions, including non-differentiable flight performance constraints. A case study on Guangzhou Baiyun International Airport was conducted to validate the proposed method. The results illustrate that the trajectory generation model can generate trajectories with high fidelity, diversity, and flyability. [ABSTRACT FROM AUTHOR]

Trajectory optimization is an essential tool for the high-fidelity planning of missions in aerospace engineering in order to increase their safety. Robust optimal control methods are utilized in the present study to address environmental or system uncertainties. To improve robustness, holistic approaches for robust trajectory optimization using sensitivity minimization with system feedback and predicted feedback are presented. Thereby, controller gains to handle uncertainty influences are optimized. The proposed method is demonstrated in an application for UAV trajectories. The resulting trajectories are less prone to unknown factors, which increases mission safety. [ABSTRACT FROM AUTHOR]

This document provides a summary of a book that explores the challenges and advancements in the guidance, navigation, and control (GNC) of autonomous and transport vehicles. It emphasizes the need for vehicles to operate autonomously and efficiently in congested city environments and discusses the development of collaborative GNC for capabilities like platooning. The document also mentions the impact of policies on GNC, such as the authorization of eVTOL and drone operations by the FAA in the United States. It highlights the growing research areas in vehicle navigation and control, including sensor fusion, trajectory optimization, and control challenges. The document concludes by encouraging readers to explore the publications in the Special Issue to gain a deeper understanding of the emerging research priorities in GNC for a diverse range of vehicles. [Extracted from the article]

With the growth of global trade, port shipping is becoming more and more important. In this paper, an analysis of a ship's inbound and outbound track characteristics is conducted using the OD–Hausdorff distance. The accuracy and efficiency of trajectory data analysis have been enhanced through clustering analysis. Trajectories are arranged in a time sequence, and representative port segments are selected. An improved OD–Hausdorff distance method is employed to capture the dynamic characteristics of a ship's movements, such as speed and heading. Additionally, the DBSCAN algorithm is utilized for clustering, allowing for the processing of multidimensional AIS data. Data cleaning and preprocessing have ensured the reliability of the AIS data, and the Douglas–Peucker algorithm is used for trajectory simplification. Significant improvements in the accuracy and efficiency of trajectory clustering have been observed. Therefore, the main channel of the Guan River and the right side of Yanwei Port are usually followed by ships greater than 60 m in length, with a lateral Relative Mean Deviation (RMD) of 7.06%. Vessels shorter than 60 m have been shown to have greater path variability, with a lateral RMD of 7.94%. Additionally, a crossing pattern at Xiangshui Port is exhibited by ships shorter than 60 m due to the extension of berths and their positions at turns. Enhanced clustering accuracy has provided more precise trajectory patterns, which aids in better channel management. [ABSTRACT FROM AUTHOR]

Ship path planning is the core problem of autonomous driving of smart ships and the basis for avoiding obstacles and other ships reasonably. To achieve this goal, this study improved the traditional A* algorithm to propose a new method for ship collision avoidance path planning by combining the multi-target point artificial potential field algorithm (MPAPF). The global planning path was smoothed and segmented into multi-target sequence points with the help of an improved A* algorithm and fewer turning nodes. The improved APF algorithm was used to plan the path of multi-target points locally, and the ship motion constraints were considered to generate a path that was more in line with the ship kinematics. In addition, this method also considered the collision avoidance situation when ships meet, carried out collision avoidance operations according to the International Regulations for Preventing Collisions at Sea (COLREGs), and introduced the collision risk index (CRI) to evaluate the collision risk and obtain a safe and reliable path. Through the simulation of a static environment and ship encounter, the experimental results show that the proposed method not only has good performance in a static environment but can also generate a safe path to avoid collision in more complex encounter scenarios. [ABSTRACT FROM AUTHOR]

This study proposes a receding horizon optimization-based docking control method to address the autonomy and safety challenge of underwater docking between manned submersibles and unmanned vehicles, facilitating the integration of docking trajectory generation and tracking control. A novel approach for optimizing and generating reference trajectory is proposed to construct a docking corridor that satisfies safe collision-free and visual guidance effective regions. It generates dynamically feasible and continuously smooth docking trajectories by rolling optimization. Subsequently, a docking trajectory tracking control method based on nonlinear model predictive control (NMPC) is designed, which is specifically tailored to address thruster saturation and system state constraints while ensuring the feasibility and stability of the control system. The control performance and robustness of underwater docking were validated through simulation experiments. The optimized trajectory generated is continuous, smooth, and complies with the docking constraints. The control system demonstrates superior tracking accuracy than backstepping control, even under conditions where the model has a 40% error and bounded disturbances from currents are present. The research findings presented in this study contribute significantly to enhancing safety and efficiency in deep-sea development. [ABSTRACT FROM AUTHOR]

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm's consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical. [ABSTRACT FROM AUTHOR]

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution. [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]

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Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments. [ABSTRACT FROM AUTHOR]

During terminal guidance, the attack platform is provided with a high‐resolution image of the target area through the application of synthetic aperture radar. Additionally, the stealth trajectory with low observability can significantly impact mission success. This paper considers both the performance of missile‐borne synthetic aperture radar imaging and stealth performance as influencing factors for terminal trajectory optimization, which is modelled as a constrained multi‐objective optimization problem. The application of the pseudospectral method in the solution of optimal control problems has led to the proposal of the hybrid genetic algorithm pseudospectral optimization framework. The problem is decomposed into several single‐objective optimal control problems, which can generate a specific initial population for the genetic algorithm to obtain a set of Pareto‐optimal solutions. Finally, the numerical simulations demonstrate the effectiveness of the proposed optimization approach compared with the benchmark scheme. [ABSTRACT FROM AUTHOR]

The focus of this study was the investigation of how the total pressure of reactants and ammonia flow rate influence the growth morphology of aluminum–gallium nitride layers crystallized by Halide Vapor Phase Epitaxy. It was established how these two critical parameters change the supersaturation levels of gallium and aluminum in the growth zone, and subsequently the morphology of the produced layers. A halide vapor phase epitaxy reactor built in-house was used, allowing for precise control over the growth conditions. Results demonstrate that both total pressure and ammonia flow rate significantly affect the nucleation and crystal growth processes which have an impact on the alloy composition, surface morphology and structural quality of aluminum–gallium nitride layers. Reducing the total pressure and adjusting the ammonia flow rate led to a notable enhancement in the homogeneity and crystallographic quality of the grown layers, along with increased aluminum incorporation. This research contributes to a deeper understanding of the growth mechanisms involved in the halide vapor phase epitaxy of aluminum–gallium nitride, and furthermore it suggests a trajectory for the optimization of growth parameters so as to obtain high-quality materials for advanced optoelectronic and electronic applications. [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]

As a kind of space robot, the two-arm cascade combination system (TACCS) has been applied to perform auxiliary operations at different locations outside space cabins. The motion coupling relation of two arms and complex surrounding obstacles make the collision-free trajectory planning optimization of TACCS more difficult, which has become an urgent problem to be solved. For the above problem, this paper proposed collision-free and time–energy–minimum trajectory planning optimization algorithms, considering the motion coupling of two arms. In this method, the screw-based inverse kinematics (IK) model of TACCS is established to provide the basis for the motion planning in joint space by decoupling the whole IK problem into two IK sub-problems of two arms; the minimum distance calculation model is established based on the hybrid geometric enveloping way and basic distance functions, which can provide the efficient and accurate data basis for the obstacle-avoidance constraint condition of the trajectory optimization. Moreover, the single and bi-layer optimization algorithms are presented by taking motion time and energy consumption as objectives and considering obstacle-avoidance and kinematics constraints. Finally, through example cases, the results indicate that the bi-layer optimization has higher convergence efficiency under the premise of ensuring the optimization effect by separating variables and constraint terms. This work can provide theoretical and methodological support for the efficient and intelligent applications of TACCS in the space arena. [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]

X-ray communication is a kind of space communication technology which uses X-ray as information carrier. In order to improve the information transmission capacity, communication rate and anti-interference ability of X-ray communication, we proposes to design a novel multi-target X-ray source. The source is composed of a fast switching module of light channels based on FPGA technology and four photoelectric X-ray tubes with different target materials: Cr, Fe, Ni, and Cu. Using Geant4 software, we determined the optimal target thickness for each material, which enabled us to fully leverage the characteristic X-rays for multi-channel signal modulation transmission. Moreover, using CST software for particle trajectory simulation and optimization of the electron beam revealed that at a tube voltage of 20 kV, the focus area measures approximately 1.2 mm×1.2 mm. The simulations show that four kinds of spectra with high distinctiveness can be generated from the Cr, Fe, Ni, and Cu targets. Within a single modulation period, these spectra can be combined in various ways to create 16 different X-ray spectra signals, thereby increasing the number of communication elements and enhancing the information transmission rate. [ABSTRACT FROM AUTHOR]

With small size, low cost and high mobility, unmanned aerial vehicles (UAVs) are playing an increasingly important role in all areas of modern society. In this paper, a full-duplex (FD) UAV equipped with a large antenna array is investigated as a relay base station (BS) to provide services to multiple ground users (GUs) using millimeter-wave (mmWave). Specifically, we propose a joint optimization problem based on user scheduling, UAV flight trajectory, beamforming and UAV transmit power to maximize the average transmission rate of the overall system. As the problem described is highly coupled and non-convex, making it difficult to solve, we propose an iterative algorithm based on the block coordinate descent (BCD) technique and the successive convex approximation (SCA) techniques to obtain an approximate optimal solution. Simulation results show that the proposed algorithm is able to converge with a small number of iterations and can significantly improve the system throughput. [ABSTRACT FROM AUTHOR]

The high mobility of unmanned aerial vehicles (UAVs) enables them to improve system throughput by establishing line-of-sight (LoS) links. Nevertheless, in urban environments, these LoS links can be disrupted by complex urban structures, leading to potential interference issues. Reconfigurable intelligent surfaces (RIS) provide an innovative approach to enhance communication performance by intelligently reflecting incident signals. Recent studies suggest that utilizing multi-antenna transmission can increase system efficiency, while single-antenna transmission may be more prone to interference. To address these challenges, this article introduces a RIS-assisted multiple-input single-output (MISO) UAV communication system. Our objective is to optimize the minimum user rate, thereby guaranteeing equitable communication for all users. Nevertheless, the non-convexity inherent in this optimization problem complicates the pursuit of a direct solution. Hence, we decompose the problem into four subproblems: user scheduling optimization, RIS phase-shift optimization, UAV trajectory optimization, and UAV transmit beamforming optimization. To obtain suboptimal solutions, we have developed an alternating iterative optimization algorithm for addressing the four subproblems. Numerical results demonstrate that our algorithm effectively boosts the minimum user rate of the entire system. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight. [ABSTRACT FROM AUTHOR]

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained. [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]