The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-d...The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.展开更多
The automatic and rapid generation of excavation trajectories is the foundation for achieving an intelligent excavator.To obtain high-performance trajectories that enhance operational capacity while avoiding the numer...The automatic and rapid generation of excavation trajectories is the foundation for achieving an intelligent excavator.To obtain high-performance trajectories that enhance operational capacity while avoiding the numerous issues present in existing methods for generating effective excavation paths,this paper proposes a trajectory generation method for excavators based on imitation learning,using the mole as a bionic prototype.Given the high excavation efficiency of moles,this paper first analyzes the structural characteristics of the mole’s forelimbs,its digging principles,morphology,and trajectory patterns.Subsequently,a higher-order polynomial is employed to fit and optimize the mole’s excavation trajectory.Next,imitation learning is conducted on sample trajectories based on Dynamic Movement Primitives,followed by the introduction of an obstacle avoidance algorithm.Simulation experiments and comparisons demonstrate that the mole-inspired trajectory method used in this paper performs well and possesses the ability to generate obstacle avoidance trajectories,as well as the convenience of transferring across different machine models.展开更多
This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated ...This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments.Initially,a collaborative planning cost function for the multi-UAV system is formulated,thereby converting the trajectory planning challenge into an optimization problem.Building on the foundational dung beetle optimization(DBO)algorithm,BFDBO incorporates three significant innovations:a boundary reflection mechanism,an adaptive mixed exploration strategy,and a dynamic multi-scale mutation strategy.These enhancements are intended to optimize the equilibrium between local exploration and global exploitation,facilitating the discovery of globally optimal trajectories thatminimize the cost function.Numerical simulations utilizing the CEC2022 benchmark function indicate that all three enhancements of BFDBOpositively influence its performance,resulting in accelerated convergence and improved optimization accuracy relative to leading optimization algorithms.In two battlefield scenarios of varying complexities,BFDBO achieved a minimum of a 39% reduction in total trajectory planning costs when compared to DBO and three other highperformance variants,while also demonstrating superior average runtime.This evidence underscores the effectiveness and applicability of BFDBO in practical,real-world contexts.展开更多
Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and miss...Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.展开更多
1Introduction To date,in model-based gait-planning methods,the dynamics of the center of mass(COM)of bipedal robots have been analyzed by establishing their linear inverted pendulum model(LIPM)or extended forms(Owaki ...1Introduction To date,in model-based gait-planning methods,the dynamics of the center of mass(COM)of bipedal robots have been analyzed by establishing their linear inverted pendulum model(LIPM)or extended forms(Owaki et al.,2010;Englsberger et al.,2015;Xie et al.,2020).With regard to model-based gait-generation methods for uphill and downhill terrain,Kuo(2007)simulated human gait using an inverted pendulum,which provided a circular trajectory for the COM rather than a horizontal trajectory.He found that a horizontal COM trajectory consumed more muscle energy.Massah et al.(2012)utilized a 3D LIPM and the concept of zero moment point(ZMP).They developed a trajectory planner using the semi-elliptical motion equations of an NAO humanoid robot and simulated walking on various sloped terrains using the Webots platform.展开更多
The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajecto...The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.展开更多
The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.T...The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.To address this demand,a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera’s trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene.A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process.Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction.To ensure safe and effective movement,two spatial constraints,Domain of View Admissible Position(DVAP)and Domain of View Admissible Orientation(DVAO),are implemented to account for robot reachability and collision avoidance.By using diffeomorphism mapping,the orientation path is transformed into 3D,consistent with the position path.Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO.The reconstruction cell is capable of automatic data acquisition and fine scene modeling,using the generated robot C-space trajectory.Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve highprecision 3D reconstruction while optimizing robot motion quality.展开更多
Given the unconstrained characteristics of the multi-robot coordinated towing system,the rope can only provide a unidirectional constraint force to the suspended object,which leads to the weak ability of the system to...Given the unconstrained characteristics of the multi-robot coordinated towing system,the rope can only provide a unidirectional constraint force to the suspended object,which leads to the weak ability of the system to resist external disturbances and makes it difficult to control the trajectory of the suspended object.Based on the kinematics and statics of the multi-robot coordinated towing system with fixed base,the dynamic model of the system is established by using the Newton-Euler equations and the Udwadia-Kalaba equations.To plan the trajectories with high stability and strong control,trajectory planning is performed by combining the dynamics and stability of the towing system.Based on the dynamic stability of the motion trajectory of the suspended object,the stability of the suspended object is effectively improved through online real-time planning and offline manual adjustment.The effectiveness of the proposed method is verified by comparing the motion stability of the suspended object before and after planning.The results provide a foundation for the motion planning and coordinated control of the towing system.展开更多
Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,a...Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,are limited by a high center of gravity,which increases the risk of rollover.Road bulges,sinkholes,and unexpected debris all present additional challenges for autonomous trucks’operational design,which current perception and decisionmaking algorithms often overlook.To mitigate rollover risks and improve adaptability to damaged roads,this paper presents a novel Road Obstacle-Involved Trajectory Planner(ROITP).The planner categorizes road obstacles using a learning-based algorithm.A discrete optimization algorithm selects a multi-objective optimal trajectory while taking into account constraints and objective functions derived from truck dynamics.Validation across various scenarios on a hardware-in-loop platform demonstrates that the proposed planner is effective and feasible for real-time implementation.展开更多
This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in tradition...This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in traditional feedback control,to the domain of motion planning for autonomous vehicles.Thus,closed-loop system uncertainty can be preemptively addressed during vehicle motion planning.This involves selecting collision-free trajectories to minimize the volume of robust invariant tubes.Furthermore,constraints on state and control variables are translated into constraints on the RCI tubes of the closed-loop system,ensuring that motion planning produces a safe and optimal trajectory while maintaining flexibility,rather than solely optimizing for the open-loop nominal model.Additionally,to expedite the solving process,we were inspired by L2gain to parameterize the RCI tubes and developed a parameterized explicit iterative expression for propagating ellipsoidal uncertainty sets within closedloop systems.Furthermore,we applied the pseudospectral orthogonal collocation method to parameterize the optimization problem of transcribing trajectories using high-order Lagrangian polynomials.Finally,under various operating conditions,we incorporate both the kinematic and dynamic models of the vehicle and also conduct simulations and analyses of uncertainties such as heading angle measurement,chassis response,and steering hysteresis.Our proposed robust motion planning framework has been validated to effectively address nearly all bounded uncertainties while anticipating potential tracking errors in control during the planning phase.This ensures fast,closed-loop safety and robustness in vehicle motion planning.展开更多
In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot insp...In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement.The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion.To enhance the robot’s movement efficiency and reduce wear on the adsorption device,a gait mimicking an inchworm’s movement is planned,and foot trajectory planning is performed using a quintic polynomial function.Under velocity constraints,foot trajectory optimization is achieved using an improved Particle Swarm Optimization(PSO)algorithm,determining the quintic polynomial function with the best fitness through simulation.Finally,through comparative experiments,the climbing time of the robot closely matches the simulation results,validating the trajectory planning method’s accuracy.展开更多
As a large-scale mining excavator,the electric shovel(ES)has been extensively employed in open-pit mines for overburden removal and mineral loading.In the development of unmanned operations for ES,dynamic excavation t...As a large-scale mining excavator,the electric shovel(ES)has been extensively employed in open-pit mines for overburden removal and mineral loading.In the development of unmanned operations for ES,dynamic excavation trajectory planning is essential,as it directly influences operational efficiency and energy consumption by guiding the dipper during excavation.However,conventional optimization-based methods for excavation trajectory planning typically start from scratch,resulting in a time-consuming process that fails to meet real-time requirements.To address this challenge,we propose an innovative online trajectory planning framework based on physics-informed neural networks(PINNOTP)that utilizes advanced data-driven techniques.The input to PINNOTP consists of onsite working conditions,including the initial state of the ES and the material surface being excavated.The output is a smooth,polynomial-based curve that serves as the reference trajectory for the dipper.To ensure smooth execution of the generated trajectory,prior domain knowledge-such as physics-based target-oriented constraints,essential system dynamics,and mechanical constraints-is explicitly incorporated into the loss function during training.A case study is presented to validate the proposed method,demonstrating that PINNOTP effectively addresses the challenges of online excavation trajectory planning.展开更多
This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we emplo...This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.展开更多
Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft...Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essential problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft synergetic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multiaircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Substantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56% with an average calculation time of 0.41 s for 3504D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for controllers in real-world ATC systems.展开更多
Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through...Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and driving.However,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works.In this paper,we first design a platoon-based collision avoidance framework for CAVs.In this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme.In addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system.In this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead communication.Moreover,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability.To further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks.Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance.Compared to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.展开更多
Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed bas...Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.展开更多
The reentry trajectory planning for hypersonic vehicles is critical and challenging in the presence of numerous nonlinear equations of motion and path constraints, as well as guaranteed satisfaction of accuracy in mee...The reentry trajectory planning for hypersonic vehicles is critical and challenging in the presence of numerous nonlinear equations of motion and path constraints, as well as guaranteed satisfaction of accuracy in meeting all the specified boundary conditions. In the last ten years, many researchers have investigated various strategies to generate a feasible or optimal constrained reentry trajectory for hypersonic vehicles. This paper briefly reviews the new research efforts to promote the capability of reentry trajectory planning. The progress of the onboard reentry trajectory planning, reentry trajectory optimization, and landing footprint is summarized. The main challenges of reentry trajectory planning for hypersonic vehicles are analyzed, focusing on the rapid reentry trajectory optimization, complex geographic constraints, and coop- erative strategies.展开更多
A 6-degree of freedom （6-DOF） aircraft wing position and pose automatic adjustment method is presented to improve ARJ21 wing-fuselage connection precision and efficiency. Wing position and pose are adjusted by three...A 6-degree of freedom （6-DOF） aircraft wing position and pose automatic adjustment method is presented to improve ARJ21 wing-fuselage connection precision and efficiency. Wing position and pose are adjusted by three pillars which are driven by six high-precision servo motors. During the adjustment process, wing is tracked and positioned by laser tracker. Wing initial position and pose are calibrated by using the measurement coordinates of assembly reference points. Wing target position and pose are calculated according to wing initial, fuselage position and pose, and relative position and pose requirements between wing and fuselage for the connection. Combining Newton-Euler method with quaternion position and pose analyzing method, the inverse kinematics of servo motors, together with the adjustment system dynamics is obtained. Wing quintic polynomial trajectory planning algorithm based on quatemion is proposed; the initial, target position and pose need to be solved and the intermediate moving path is uncertain. Simulation results show that the adjustment method has good dynamic characteristics and satisfies engineering requirements. Preliminary engineering application indicates that ARJ21 wing adjustment efficiency and precision are improved by using the proposed method.展开更多
In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Fir...In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened.The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced.By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths.And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually.Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path.If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV.In most cases, a flyable path can be obtained eventually.Simulation results demonstrate the effectiveness of this method.展开更多
Aimed at capture task for a free-floating space manipulator, a scheme of pre-impact trajectory planning for minimizing base attitude disturbance caused by impact is proposed in this paper.Firstly, base attitude distur...Aimed at capture task for a free-floating space manipulator, a scheme of pre-impact trajectory planning for minimizing base attitude disturbance caused by impact is proposed in this paper.Firstly, base attitude disturbance is established as a function of joint angles, collision direction and relative velocity between robotic hand and the target.Secondly, on the premise of keeping correct capture pose, a novel optimization factor in null space is designed to minimize base attitude disturbance and ensure that the joint angles do not exceed their limits simultaneously.After reaching the balance state, a desired configuration is achieved at the contact point.Thereafter, particle swarm optimization（PSO） algorithm is employed to solve the pre-impact trajectory planning from its initial configuration to the desired configuration to achieve the minimized base attitude disturbance caused by impact and the correct capture pose simultaneously.Finally, the proposed method is applied to a 7-dof free-floating space manipulator and the simulation results verify the effectiveness.展开更多
基金supported by the National Key R&D Program of China(No.2022YFB3104502)the National Natural Science Foundation of China(No.62301251)+2 种基金the Natural Science Foundation of Jiangsu Province of China under Project(No.BK20220883)the open research fund of National Mobile Communications Research Laboratory,Southeast University,China(No.2024D04)the Young Elite Scientists Sponsorship Program by CAST(No.2023QNRC001).
文摘The environment of low-altitude urban airspace is complex and variable due to numerous obstacles,non-cooperative aircraft,and birds.Unmanned Aerial Vehicles(UAVs)leveraging environmental information to achieve three-dimension collision-free trajectory planning is the prerequisite to ensure airspace security.However,the timely information of surrounding situation is difficult to acquire by UAVs,which further brings security risks.As a mature technology leveraged in traditional civil aviation,the Automatic Dependent Surveillance-Broadcast(ADS-B)realizes continuous surveillance of the information of aircraft.Consequently,we leverage ADS-B for surveillance and information broadcasting,and divide the aerial airspace into multiple sub-airspaces to improve flight safety in UAV trajectory planning.In detail,we propose the secure Sub-airSpaces Planning(SSP)algorithm and Particle Swarm Optimization Rapidly-exploring Random Trees(PSO-RRT)algorithm for the UAV trajectory planning in law-altitude airspace.The performance of the proposed algorithm is verified by simulations and the results show that SSP reduces both the maximum number of UAVs in the sub-airspace and the length of the trajectory,and PSO-RRT reduces the cost of UAV trajectory in the sub-airspace.
基金supported by the National Science Foundation of China(Grant No.52375246,No.52372428,No.52105100)Guangxi Science and Technology Program(Grant No.2023AB09014)Jilin Province Science and Technology Development Program,(Grant No.20230201094GX,No.20230201069GX).
文摘The automatic and rapid generation of excavation trajectories is the foundation for achieving an intelligent excavator.To obtain high-performance trajectories that enhance operational capacity while avoiding the numerous issues present in existing methods for generating effective excavation paths,this paper proposes a trajectory generation method for excavators based on imitation learning,using the mole as a bionic prototype.Given the high excavation efficiency of moles,this paper first analyzes the structural characteristics of the mole’s forelimbs,its digging principles,morphology,and trajectory patterns.Subsequently,a higher-order polynomial is employed to fit and optimize the mole’s excavation trajectory.Next,imitation learning is conducted on sample trajectories based on Dynamic Movement Primitives,followed by the introduction of an obstacle avoidance algorithm.Simulation experiments and comparisons demonstrate that the mole-inspired trajectory method used in this paper performs well and possesses the ability to generate obstacle avoidance trajectories,as well as the convenience of transferring across different machine models.
基金funded by the National Defense Science and Technology Innovation project,grant number ZZKY20223103the Basic Frontier InnovationProject at the Engineering University of PAP,grant number WJY202429+2 种基金the Basic Frontier lnnovation Project at the Engineering University of PAP,grant number WJY202408the Graduate Student Funding Priority Project,grant number JYWJ2024B006Key project of National Social Science Foundation,grant number 2023-SKJJ-A-116.
文摘This study introduces a novel algorithm known as the dung beetle optimization algorithm based on bounded reflection optimization andmulti-strategy fusion(BFDBO),which is designed to tackle the complexities associated with multi-UAV collaborative trajectory planning in intricate battlefield environments.Initially,a collaborative planning cost function for the multi-UAV system is formulated,thereby converting the trajectory planning challenge into an optimization problem.Building on the foundational dung beetle optimization(DBO)algorithm,BFDBO incorporates three significant innovations:a boundary reflection mechanism,an adaptive mixed exploration strategy,and a dynamic multi-scale mutation strategy.These enhancements are intended to optimize the equilibrium between local exploration and global exploitation,facilitating the discovery of globally optimal trajectories thatminimize the cost function.Numerical simulations utilizing the CEC2022 benchmark function indicate that all three enhancements of BFDBOpositively influence its performance,resulting in accelerated convergence and improved optimization accuracy relative to leading optimization algorithms.In two battlefield scenarios of varying complexities,BFDBO achieved a minimum of a 39% reduction in total trajectory planning costs when compared to DBO and three other highperformance variants,while also demonstrating superior average runtime.This evidence underscores the effectiveness and applicability of BFDBO in practical,real-world contexts.
文摘Compared with single-domain unmanned swarms,cross-domain unmanned swarms continue to face new challenges in terms of platform performance and constraints.In this paper,a joint unmanned swarm target assignment and mission trajectory planning method is proposed to meet the requirements of cross-domain unmanned swarm mission planning.Firstly,the different performances of cross-domain heterogeneous platforms and mission requirements of targets are characterised by using a collection of operational resources.Secondly,an algorithmic framework for joint target assignment and mission trajectory planning is proposed,in which the initial planning of the trajectory is performed in the target assignment phase,while the trajectory is further optimised afterwards.Next,the estimation of the distribution algorithms is combined with the genetic algorithm to solve the objective function.Finally,the algorithm is numerically simulated by specific cases.Simulation results indicate that the proposed algorithm can perform effective task assignment and trajectory planning for cross-domain unmanned swarms.Furthermore,the solution performance of the hybrid estimation of distribution algorithm(EDA)-genetic algorithm(GA)algorithm is better than that of GA and EDA.
基金supported by the National Natural Science Foundation of China(No.12332023)the Zhejiang Provincial Natural Science Foundation of China(No.LY23E050010).
文摘1Introduction To date,in model-based gait-planning methods,the dynamics of the center of mass(COM)of bipedal robots have been analyzed by establishing their linear inverted pendulum model(LIPM)or extended forms(Owaki et al.,2010;Englsberger et al.,2015;Xie et al.,2020).With regard to model-based gait-generation methods for uphill and downhill terrain,Kuo(2007)simulated human gait using an inverted pendulum,which provided a circular trajectory for the COM rather than a horizontal trajectory.He found that a horizontal COM trajectory consumed more muscle energy.Massah et al.(2012)utilized a 3D LIPM and the concept of zero moment point(ZMP).They developed a trajectory planner using the semi-elliptical motion equations of an NAO humanoid robot and simulated walking on various sloped terrains using the Webots platform.
基金supported by the National Natural Science Foundation of China(51875302)。
文摘The forward design of trajectory planning strategies requires preset trajectory optimization functions,resulting in poor adaptability of the strategy and an inability to accurately generate obstacle avoidance trajectories that conform to real driver behavior habits.In addition,owing to the strong time-varying dynamic characteristics of obstacle avoidance scenarios,it is necessary to design numerous trajectory optimization functions and adjust the corresponding parameters.Therefore,an anthropomorphic obstacle-avoidance trajectory planning strategy for adaptive driving scenarios is proposed.First,numerous expert-demonstrated trajectories are extracted from the HighD natural driving dataset.Subsequently,a trajectory expectation feature-matching algorithm is proposed that uses maximum entropy inverse reinforcement learning theory to learn the extracted expert-demonstrated trajectories and achieve automatic acquisition of the optimization function of the expert-demonstrated trajectory.Furthermore,a mapping model is constructed by combining the key driving scenario information that affects vehicle obstacle avoidance with the weight of the optimization function,and an anthropomorphic obstacle avoidance trajectory planning strategy for adaptive driving scenarios is proposed.Finally,the proposed strategy is verified based on real driving scenarios.The results show that the strategy can adjust the weight distribution of the trajectory optimization function in real time according to the“emergency degree”of obstacle avoidance and the state of the vehicle.Moreover,this strategy can generate anthropomorphic trajectories that are similar to expert-demonstrated trajectories,effectively improving the adaptability and acceptability of trajectories in driving scenarios.
基金supported by the National Key Research and Development Program of China(2019YFB1707505)the National Natural Science Foundation of China(Grant No.52005436)。
文摘The trend towards automation and intelligence in aircraft final assembly testing has led to a new demand for autonomous perception of unknown cockpit operation scenes in robotic collaborative airborne system testing.To address this demand,a robotic automated 3D reconstruction cell which enables to autonomously plan the robot end-camera’s trajectory is developed for image acquisition and 3D modeling of the cockpit operation scene.A continuous viewpoint path planning algorithm is proposed that incorporates both 3D reconstruction quality and robot path quality into optimization process.Smoothness metrics for viewpoint position paths and orientation paths are introduced together for the first time in 3D reconstruction.To ensure safe and effective movement,two spatial constraints,Domain of View Admissible Position(DVAP)and Domain of View Admissible Orientation(DVAO),are implemented to account for robot reachability and collision avoidance.By using diffeomorphism mapping,the orientation path is transformed into 3D,consistent with the position path.Both orientation and position paths can be optimized in a unified framework to maximize the gain of reconstruction quality and path smoothness within DVAP and DVAO.The reconstruction cell is capable of automatic data acquisition and fine scene modeling,using the generated robot C-space trajectory.Simulation and physical scene experiments have confirmed the effectiveness of the proposed method to achieve highprecision 3D reconstruction while optimizing robot motion quality.
基金the National Natural Science Foundation of China(No.51965032)the National Natural Science Foundation of Gansu Province of China(No.22JR5RA319)+1 种基金the Excellent Dectoral Student Foundation of Gansu Province of China(No.23JRRA842)the Science and Technology Foundation of Gansu Province of China(No.21YF5WA060)。
文摘Given the unconstrained characteristics of the multi-robot coordinated towing system,the rope can only provide a unidirectional constraint force to the suspended object,which leads to the weak ability of the system to resist external disturbances and makes it difficult to control the trajectory of the suspended object.Based on the kinematics and statics of the multi-robot coordinated towing system with fixed base,the dynamic model of the system is established by using the Newton-Euler equations and the Udwadia-Kalaba equations.To plan the trajectories with high stability and strong control,trajectory planning is performed by combining the dynamics and stability of the towing system.Based on the dynamic stability of the motion trajectory of the suspended object,the stability of the suspended object is effectively improved through online real-time planning and offline manual adjustment.The effectiveness of the proposed method is verified by comparing the motion stability of the suspended object before and after planning.The results provide a foundation for the motion planning and coordinated control of the towing system.
基金Supported by National Natural Science Foundation of China (Grant Nos. 52072215, 52221005, 52272386)Beijing Municipal Natrual Science Foundation (Grant No. L243025)+2 种基金National Key R&D Program of China (Grant No. 2022YFB2503003)State Key Laboratory of Intelligent Green Vehicle and Mobilityfundamental Research Funds for the Central Universities
文摘Autonomous trucks have the potential to enhance both safety and convenience in intelligent transportation.However,their maximum speed and ability to navigate a variety of driving conditions,particularly uneven roads,are limited by a high center of gravity,which increases the risk of rollover.Road bulges,sinkholes,and unexpected debris all present additional challenges for autonomous trucks’operational design,which current perception and decisionmaking algorithms often overlook.To mitigate rollover risks and improve adaptability to damaged roads,this paper presents a novel Road Obstacle-Involved Trajectory Planner(ROITP).The planner categorizes road obstacles using a learning-based algorithm.A discrete optimization algorithm selects a multi-objective optimal trajectory while taking into account constraints and objective functions derived from truck dynamics.Validation across various scenarios on a hardware-in-loop platform demonstrates that the proposed planner is effective and feasible for real-time implementation.
基金Supported by National Natural Science Foundation of China(Grant Nos.52025121,52394263)National Key R&D Plan of China(Grant No.2023YFD2000301)+2 种基金Foundation of State Key Laboratory of Automobile Safety and Energy Saving of China(Grant No.KFZ2201)the Jiangsu Provincial Scientific Research Center of Applied Mathematics under(Grant No.BK20233002)Special Fund of Jiangsu Province for the Transformation of Scientific and Technological Achievements under(Grant No.BA2021023)。
文摘This paper tackles uncertainties between planning and actual models.It extends the concept of RCI(robust control invariant)tubes,originally a parameterized representation of closed-loop control robustness in traditional feedback control,to the domain of motion planning for autonomous vehicles.Thus,closed-loop system uncertainty can be preemptively addressed during vehicle motion planning.This involves selecting collision-free trajectories to minimize the volume of robust invariant tubes.Furthermore,constraints on state and control variables are translated into constraints on the RCI tubes of the closed-loop system,ensuring that motion planning produces a safe and optimal trajectory while maintaining flexibility,rather than solely optimizing for the open-loop nominal model.Additionally,to expedite the solving process,we were inspired by L2gain to parameterize the RCI tubes and developed a parameterized explicit iterative expression for propagating ellipsoidal uncertainty sets within closedloop systems.Furthermore,we applied the pseudospectral orthogonal collocation method to parameterize the optimization problem of transcribing trajectories using high-order Lagrangian polynomials.Finally,under various operating conditions,we incorporate both the kinematic and dynamic models of the vehicle and also conduct simulations and analyses of uncertainties such as heading angle measurement,chassis response,and steering hysteresis.Our proposed robust motion planning framework has been validated to effectively address nearly all bounded uncertainties while anticipating potential tracking errors in control during the planning phase.This ensures fast,closed-loop safety and robustness in vehicle motion planning.
基金supported by the Guangxi Science and Technology Base and Talent Project(AD23026115)the Special fund for centrally guided local science and technology development(2023JRZ0103)+1 种基金the Guangxi University of Science and Technology Doctoral Fund(2023KY0353)the Guangxi University of Science and Technology Doctoral Fund(22Z39).
文摘In order to reduce the labor intensity of high-altitude workers and realize the cleaning and maintenance of high-rise building exteriors,this paper proposes a design for a 4-DOF bipedal wall-climbing bionic robot inspired by the inchworm’s movement.The robot utilizes vacuum adsorption for vertical wall attachment and legged movement for locomotion.To enhance the robot’s movement efficiency and reduce wear on the adsorption device,a gait mimicking an inchworm’s movement is planned,and foot trajectory planning is performed using a quintic polynomial function.Under velocity constraints,foot trajectory optimization is achieved using an improved Particle Swarm Optimization(PSO)algorithm,determining the quintic polynomial function with the best fitness through simulation.Finally,through comparative experiments,the climbing time of the robot closely matches the simulation results,validating the trajectory planning method’s accuracy.
基金Supported by the National Natural Science Foundation of China(Grant No.52075068)the Shanxi Science and Technology Major Project(Grant No.20191101014).
文摘As a large-scale mining excavator,the electric shovel(ES)has been extensively employed in open-pit mines for overburden removal and mineral loading.In the development of unmanned operations for ES,dynamic excavation trajectory planning is essential,as it directly influences operational efficiency and energy consumption by guiding the dipper during excavation.However,conventional optimization-based methods for excavation trajectory planning typically start from scratch,resulting in a time-consuming process that fails to meet real-time requirements.To address this challenge,we propose an innovative online trajectory planning framework based on physics-informed neural networks(PINNOTP)that utilizes advanced data-driven techniques.The input to PINNOTP consists of onsite working conditions,including the initial state of the ES and the material surface being excavated.The output is a smooth,polynomial-based curve that serves as the reference trajectory for the dipper.To ensure smooth execution of the generated trajectory,prior domain knowledge-such as physics-based target-oriented constraints,essential system dynamics,and mechanical constraints-is explicitly incorporated into the loss function during training.A case study is presented to validate the proposed method,demonstrating that PINNOTP effectively addresses the challenges of online excavation trajectory planning.
基金supported by the National Natural Science Foundation of China(the Key Project,52131201Science Fund for Creative Research Groups,52221005)+1 种基金the China Scholarship Councilthe Joint Laboratory for Internet of Vehicles,Ministry of Education–China MOBILE Communications Corporation。
文摘This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and efficiently.Firstly,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road centerline.Cartesian coordinates are then transformed to achieve the curvature continuity of the generated curve.Considering the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate system.Furthermore,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and adaptability.Extensive simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic participants.Moreover,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.
基金the support of the Chinese Special Research Project for Civil Aircraft(No.MJZ1-7N22)the National Natural Science Foundation of China(No.U2133207).
文摘Adverse weather during aircraft operation generates more complex scenarios for tactical trajectory planning,which requires superior real-time performance and conflict-free reliability of solving methods.Multi-aircraft real-time 4D trajectory planning under adverse weather is an essential problem in Air Traffic Control(ATC)and it is challenging for the existing methods to be applied effectively.A framework of Double Deep Q-value Network under the Critic guidance with heuristic Pairing(DDQNC-P)is proposed to solve this problem.An Agent for two aircraft synergetic trajectory planning is trained by the Deep Reinforcement Learning(DRL)model of DDQNC,which completes two aircraft 4D trajectory planning tasks preliminarily under dynamic weather conditions.Then a heuristic pairing algorithm is designed to convert the multi-aircraft synergetic trajectory planning into multi-time pairwise synergetic trajectory planning,making the multiaircraft trajectory planning problem processable for the trained Agent.This framework compresses the input dimensions of the DRL model while improving its generalization ability significantly.Substantial simulations with various aircraft numbers,weather conditions,and airspace structures were conducted for performance verification and comparison.The success rate of conflict-free trajectory resolution reached 96.56% with an average calculation time of 0.41 s for 3504D trajectory points per aircraft,finally confirming its applicability to make real-time decision-making support for controllers in real-world ATC systems.
基金partly supported by National Key R&D Program of China(No.2018YFE0117500)National Natural Science Foundation of China(No.62171104)+2 种基金EU Horizon2020(824019),EU Horizon2020(101022280)Horizon Europe(101086228)the UK EPSRC(EP/Y027787/1)。
文摘Road obstacles that unexpectedly appear due to vehicle breakdowns and accidents are major causes of fatal road accidents.Connected Autonomous Vehicles(CAVs)can be used to avoid collisions to ensure road safety through cooperative sensing and driving.However,the collision avoidance performance of CAVs with unexpected obstacles has not been studied in the existing works.In this paper,we first design a platoon-based collision avoidance framework for CAVs.In this framework,we deploy a Digital Twin(DT)system at the head vehicle in a platoon to reduce communication overhead and decision-making delay based on a proposed trajectory planning scheme.In addition,a DT-assistant system is deployed on the assistant vehicle to monitor vehicles out of the sensing range of the head vehicle for the maintenance of the DT system.In this case,the transmission frequency of kinetic states of platoon members can be reduced to ensure low-overhead communication.Moreover,we design a variable resource reservation interval that can ensure DT synchronization between DT and the assistant system with high reliability.To further improve road safety,an urgency level-based trajectory planning algorithm is proposed to avoid unexpected obstacles considering different levels of emergency risks.Simulation results show that our DT system-based scheme can achieve significant performance gains in unexpected obstacle avoidance.Compared to the existing schemes,it can reduce collisions by 95%and is faster by about 10%passing by the unexpected obstacle.
基金Supported by National Natural Science Foundation of China(Grant Nos.52222215,52072051)Chongqing Municipal Natural Science Foundation of China(Grant No.CSTB2023NSCQ-JQX0003).
文摘Accurate trajectory prediction of surrounding road users is the fundamental input for motion planning,which enables safe autonomous driving on public roads.In this paper,a safe motion planning approach is proposed based on the deep learning-based trajectory prediction method.To begin with,a trajectory prediction model is established based on the graph neural network(GNN)that is trained utilizing the INTERACTION dataset.Then,the validated trajectory prediction model is used to predict the future trajectories of surrounding road users,including pedestrians and vehicles.In addition,a GNN prediction model-enabled motion planner is developed based on the model predictive control technique.Furthermore,two driving scenarios are extracted from the INTERACTION dataset to validate and evaluate the effectiveness of the proposed motion planning approach,i.e.,merging and roundabout scenarios.The results demonstrate that the proposed method can lower the risk and improve driving safety compared with the baseline method.
基金supported by the National Natural Science Foundation of China(6127334961203223+1 种基金61175109)the Innovation Foundation of BUAA for Ph.D.Graduates(YWF-14-YJSY-013)
文摘The reentry trajectory planning for hypersonic vehicles is critical and challenging in the presence of numerous nonlinear equations of motion and path constraints, as well as guaranteed satisfaction of accuracy in meeting all the specified boundary conditions. In the last ten years, many researchers have investigated various strategies to generate a feasible or optimal constrained reentry trajectory for hypersonic vehicles. This paper briefly reviews the new research efforts to promote the capability of reentry trajectory planning. The progress of the onboard reentry trajectory planning, reentry trajectory optimization, and landing footprint is summarized. The main challenges of reentry trajectory planning for hypersonic vehicles are analyzed, focusing on the rapid reentry trajectory optimization, complex geographic constraints, and coop- erative strategies.
基金Basic Scientific Research Projects of Nanjing University of Aeronautics & Astronautics (NS 2010128)
文摘A 6-degree of freedom （6-DOF） aircraft wing position and pose automatic adjustment method is presented to improve ARJ21 wing-fuselage connection precision and efficiency. Wing position and pose are adjusted by three pillars which are driven by six high-precision servo motors. During the adjustment process, wing is tracked and positioned by laser tracker. Wing initial position and pose are calibrated by using the measurement coordinates of assembly reference points. Wing target position and pose are calculated according to wing initial, fuselage position and pose, and relative position and pose requirements between wing and fuselage for the connection. Combining Newton-Euler method with quaternion position and pose analyzing method, the inverse kinematics of servo motors, together with the adjustment system dynamics is obtained. Wing quintic polynomial trajectory planning algorithm based on quatemion is proposed; the initial, target position and pose need to be solved and the intermediate moving path is uncertain. Simulation results show that the adjustment method has good dynamic characteristics and satisfies engineering requirements. Preliminary engineering application indicates that ARJ21 wing adjustment efficiency and precision are improved by using the proposed method.
基金supported by the National Natural Science Foundation of China (No.61175084)the Program for Changjiang Scholars and Innovative Research Team in University of Ministry of Education of China (No.IRT13004)
文摘In this paper, a novel algorithm based on disturbed fluid and trajectory propagation is developed to solve the three-dimensional（3-D） path planning problem of unmanned aerial vehicle（UAV） in static environment.Firstly, inspired by the phenomenon of streamlines avoiding obstacles, the algorithm based on disturbed fluid is developed and broadened.The effect of obstacles on original fluid field is quantified by the perturbation matrix, where the tangential matrix is first introduced.By modifying the original flow field, the modified one is then obtained, where the streamlines can be regarded as planned paths.And the path proves to avoid all obstacles smoothly and swiftly, follow the shape of obstacles effectively and reach the destination eventually.Then, by considering the kinematics and dynamics equations of UAV, the method called trajectory propagation is adopted to judge the feasibility of the path.If the planned path is unfeasible, repulsive and tangential parameters in the perturbation matrix will be adjusted adaptively based on the resolved state variables of UAV.In most cases, a flyable path can be obtained eventually.Simulation results demonstrate the effectiveness of this method.
基金supported by the National Basic Research Program of China (No.2013CB733000)the National Natural Science Foundation of China (No.61175080)BUPT Excellent Ph.D.Students Foundation of China (No.CX201427)
文摘Aimed at capture task for a free-floating space manipulator, a scheme of pre-impact trajectory planning for minimizing base attitude disturbance caused by impact is proposed in this paper.Firstly, base attitude disturbance is established as a function of joint angles, collision direction and relative velocity between robotic hand and the target.Secondly, on the premise of keeping correct capture pose, a novel optimization factor in null space is designed to minimize base attitude disturbance and ensure that the joint angles do not exceed their limits simultaneously.After reaching the balance state, a desired configuration is achieved at the contact point.Thereafter, particle swarm optimization（PSO） algorithm is employed to solve the pre-impact trajectory planning from its initial configuration to the desired configuration to achieve the minimized base attitude disturbance caused by impact and the correct capture pose simultaneously.Finally, the proposed method is applied to a 7-dof free-floating space manipulator and the simulation results verify the effectiveness.
