The Tethered Space Net Robot(TSNR)is an innovative solution for active space debris capture and removal.Its large envelope and simple capture method make it an attractive option for this task.However,capturing maneuve...The Tethered Space Net Robot(TSNR)is an innovative solution for active space debris capture and removal.Its large envelope and simple capture method make it an attractive option for this task.However,capturing maneuverable debris with the flexible and elastic underactuated net poses significant challenges.To address this,a novel formation control method for the TSNR is proposed through the integration of differential game theory and robust adaptive control in this paper.Specifically,the trajectory of the TSNR is obtained through the solution of a real-time feedback pursuit-evasion game with a dynamic target,where the primary condition is to ensure the stability of the TSNR.Furthermore,to minimize tracking errors and maintain a specific configuration,a robust adaptive formation control scheme with Artificial Potential Field(APF)based on a Finite-Time Convergent Extended State Observer(FTCESO)is investigated.The proposed control method has a key advantage in suppressing complex oscillations by a new adaptive law,thus precisely maintaining the configuration.Finally,numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.展开更多
Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial ...Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.展开更多
Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and pro...Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.展开更多
Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Fir...Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.展开更多
Formation keeping is important for multiple Unmanned Aerial Vehicles(multi-UAV)to fully play their roles in cooperative combats and improve their mission success rate.However,in practical applications,it is difficult ...Formation keeping is important for multiple Unmanned Aerial Vehicles(multi-UAV)to fully play their roles in cooperative combats and improve their mission success rate.However,in practical applications,it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time.This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field(APF)method.Firstly,we build a formation flight model based on the “Leader-Follower”structure and design a robust H∞ controller with three channels X,Y and Z to eliminate dynamic uncertainties,so as to realize high-precision formation keeping.Secondly,to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia,this paper comes up with the improved APF method with deformation factor considered.The judgment criterion is proposed and applied to ensure flight safety.In the end,the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime,it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles.Therefore,the method proposed here boasts good engineering application prospect.展开更多
An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed...An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.展开更多
For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planni...For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.展开更多
A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential fi...A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF),which was a very appropriate method to model a reinforcement learning problem.Secondly,a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept.The performance of this new method was tested by a gridworld problem named as key and door maze.The experimental results show that within 45 trials,good and deterministic policies are found in almost all simulations.In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution,the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning.Therefore,the new method is simple and effective to give an optimal solution to the reinforcement learning problem.展开更多
A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential...A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential field is constructed in Cartesian space,which provides the heuristic information,effective distance to the goal and the motion direction for the motion of the robot joints.Secondly,a genetic algorithm,combined with the heuristic rules,is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained.A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles,but also improve the efficiency and quality of path planning.展开更多
In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields...In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.展开更多
To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method ...To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.展开更多
The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the&...The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the"Integral method"focusing on solving the problem of local minimization.The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole,By changing the parameters of the repulsive force field,robots can quickly get out of the minimum point and move to the target point.This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method,which projects a feasible path successfully and verifies this method.展开更多
Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks.This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollo...Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks.This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk.The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion,lateral motion,yaw motion,sprung mass roll motion,unsprung mass roll motion,and an anti-rollover artificial potential field(APF)is proposed based on this.The motion planning method,which is based on model predictive control(MPC),combines trajectory tracking,anti-rollover APF,and the improved obstacle avoidance APF and considers the truck dynamics constraints,obstacle avoidance,and anti-rollover.Furthermore,by using game theory,the coefficients of the two APF functions are optimised,and an optimal path is planned.The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios.The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.展开更多
In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional R...In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional RRT algorithm used for unmanned aerial vehicle(UAV)path planning in complex environments,an improved bidirectional RRT algorithm was proposed.The algorithm firstly adopted a goal-oriented strategy to guide the sampling points towards the target point,and then the artificial potential field acted on the random tree nodes to avoid collision with obstacles and reduced the length of the search path,and the random tree node growth also combined the UAV’s own flight constraints,and by combining the triangulation method to remove the redundant node strategy and the third-order B-spline curve for the smoothing of the trajectory,the planned path was better.The planned paths were more optimized.Finally,the simulation experiments in complex and dynamic environments showed that the algorithm effectively improved the speed of trajectory planning and shortened the length of the trajectory,and could generate a safe,smooth and fast trajectory in complex environments,which could be applied to online trajectory planning.展开更多
An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and coll...An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.展开更多
In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack stra...In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack strategy for missile swarm is proposed.Based on the distribution of the attackers and defenders,the collision avoidance against the defenders is considered during the attack process.By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders,the collision avoidance constrains of attacking swarm are redefined.The key point is on adjusting the relative velocity vectors to fall outside the collision cone.This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process.By leveraging an innovative repulsion artificial function,a time-efficient cooperative attack strategy for missile swarm is obtained.Through rigorous simulation,the effectiveness of this cooperative attack strategy is substantiated.Furthermore,by employing Monte Carlo simulation,the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.展开更多
Aimed at complex distributed no-fly zones avoidance problems,a novel adaptive lateral reentry guidance algorithm is proposed.Firstly,by introducing the improved attractive and repulsive potential fields,an improved ar...Aimed at complex distributed no-fly zones avoidance problems,a novel adaptive lateral reentry guidance algorithm is proposed.Firstly,by introducing the improved attractive and repulsive potential fields,an improved artificial potential field method is developed.Combined with the proposed judgment criterion for whether a no-fly zone has been avoided,the proposed improved artificial potential field method effectively solves the reference heading angle determination problem under the constraints of complex distributed no-fly zones.Then,based on the proposed no-fly zone’s threat quantitative evaluation method and the reference heading angle determined by the proposed improved artificial potential field method,the heading corridor is improved to increase its sensitivity to the threat changes of the no-fly zones.Finally,for satisfying the requirements of complex distributed no-fly zones avoidance,a novel guidance logic via improved heading corridor is proposed to update the reference heading corridor adaptively in real time according to the threat and constraint changes of the no-fly zones,and the bank reversal logic is employed to control the lateral motion.The simulation results for nominal and dispersed cases indicate that the proposed guidance algorithm has high robustness,stability,and applicability,and is feasible and effective to deal with the complex distributed no-fly zones avoidance problems.展开更多
In this paper, we focus on circle formation control of multi-agent systems （MAS） with a leader. The circle formation is achieved based on the lead-following and the artificial potential field method. A distributed c...In this paper, we focus on circle formation control of multi-agent systems （MAS） with a leader. The circle formation is achieved based on the lead-following and the artificial potential field method. A distributed control law is given to make a group of agents form a circle and consequently achieve an expected angle. Finally, simulation results show that the proposed circle formation strategies are effective.展开更多
An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential fiel...An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential field method,the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets.Secondly,according to the finite-time convergence high-order differentiator,a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory.Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking,high precision,strong stability and avoiding chattering.Finally,a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy.Theoretical analysis and numerical simulation results show that the model has the fast convergence,high control accuracy,strong stability and good robustness.展开更多
To realize automatic manipulation of micro-particles by light-induced dielectrophoresis （LDEP）, a path-planning scheme based on the improved artificial potential field （APF） for micro light pattern movements is pr...To realize automatic manipulation of micro-particles by light-induced dielectrophoresis （LDEP）, a path-planning scheme based on the improved artificial potential field （APF） for micro light pattern movements is proposed. An algorithm combining guided target and point obstacle based on a new local minimum judging criterion is specially designed, which can solve the local minimum problems encountered by the traditional APF. Experiments of real-time particle manipulation based on this algorithm are implemented and the experimental results show that the proposed approach can overcome the local minimum problems of the traditional APF method, and it is validated to be highly stable for intensive particle obstacles during LDEP manipulation. Consequently, this method can realize real-time manipulation of micro-nano particles with safety, decrease the difficulty of manual manipulation, and thus improve the efficiency of manipulation of micro-particles.展开更多
基金supported by the National Natural Science Foundation of China(Nos.62222313,62173275,62327809,62303381,and 62303312)in part by the China Postdoctoral Science Foundation(No.2023M732225).
文摘The Tethered Space Net Robot(TSNR)is an innovative solution for active space debris capture and removal.Its large envelope and simple capture method make it an attractive option for this task.However,capturing maneuverable debris with the flexible and elastic underactuated net poses significant challenges.To address this,a novel formation control method for the TSNR is proposed through the integration of differential game theory and robust adaptive control in this paper.Specifically,the trajectory of the TSNR is obtained through the solution of a real-time feedback pursuit-evasion game with a dynamic target,where the primary condition is to ensure the stability of the TSNR.Furthermore,to minimize tracking errors and maintain a specific configuration,a robust adaptive formation control scheme with Artificial Potential Field(APF)based on a Finite-Time Convergent Extended State Observer(FTCESO)is investigated.The proposed control method has a key advantage in suppressing complex oscillations by a new adaptive law,thus precisely maintaining the configuration.Finally,numerical simulations are performed to demonstrate the effectiveness of the proposed scheme.
基金Supported by the National Natural Science Foundation of China (Grant No. 52071097)Hainan Provincial Natural Science Foundation of China (Grant No. 522MS162)Research Fund from Science and Technology on Underwater Vehicle Technology Laboratory (Grant No. 2021JCJQ-SYSJJ-LB06910)。
文摘Path planning for recovery is studied on the engineering background of double unmanned surface vehicles(USVs)towing oil booms for oil spill recovery.Given the influence of obstacles on the sea,the improved artificial potential field(APF)method is used for path planning.For addressing the two problems of unreachable target and local minimum in the APF,three improved algorithms are proposed by combining the motion performance constraints of the double USV system.These algorithms are then combined as the final APF-123 algorithm for oil spill recovery.Multiple sets of simulation tests are designed according to the flaws of the APF and the process of oil spill recovery.Results show that the proposed algorithms can ensure the system’s safety in tracking oil spills in a complex environment,and the speed is increased by more than 40%compared with the APF method.
基金The National Natural Science Foundation of China(Grant No.72274052)the National Natural Science Foundation of China(Grant No.72174173).
文摘Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways.Emergency response and rescue vessels(ERRVs)play an essential role in minimizing losses and protecting lives and property.However,the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making.This paper proposes an improved artificial potential field(APF)algorithm to optimize the shortest path for ERRVs in the rescue process.To verify the feasibility of the proposed model,eight tests were carried out in two water areas of the Yangtze River.The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds.The improved APF algorithm performed better in the ERRV’s goal achievement,compared with the traditional algorithm.The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief.It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent,mitigate,and relieve flood disasters.
文摘Cooperative path planning is an important area in fixed-wing UAV swarm.However,avoiding multiple timevarying obstacles and avoiding local optimum are two challenges for existing approaches in a dynamic environment.Firstly,a normalized artificial potential field optimization is proposed by reconstructing a novel function with anisotropy in each dimension,which can make the flight speed of a fixed UAV swarm independent of the repulsive/attractive gain coefficient and avoid trapping into local optimization and local oscillation.Then,taking into account minimum velocity and turning angular velocity of fixed-wing UAV swarm,a strategy of decomposing target vector to avoid moving obstacles and pop-up threats is proposed.Finally,several simulations are carried out to illustrate superiority and effectiveness.
基金supported by Funding from the National Key Laboratory of Rotorcraft Aeromechanics,China(No.61422202108)the National Natural Science Foundation of China(No.52176009).
文摘Formation keeping is important for multiple Unmanned Aerial Vehicles(multi-UAV)to fully play their roles in cooperative combats and improve their mission success rate.However,in practical applications,it is difficult to achieve formation keeping precisely and obstacle avoidance autonomously at the same time.This paper proposes a joint control method based on robust H∞ controller and improved Artificial Potential Field(APF)method.Firstly,we build a formation flight model based on the “Leader-Follower”structure and design a robust H∞ controller with three channels X,Y and Z to eliminate dynamic uncertainties,so as to realize high-precision formation keeping.Secondly,to fulfill obstacle avoidance efficiently in complex situations where UAVs fly at high speed with high inertia,this paper comes up with the improved APF method with deformation factor considered.The judgment criterion is proposed and applied to ensure flight safety.In the end,the simulation results show that the designed controller is effective with the formation keeping a high accuracy and in the meantime,it enables UAVs to avoid obstacles autonomously and recover the formation rapidly when coming close to obstacles.Therefore,the method proposed here boasts good engineering application prospect.
基金supported by the National Natural Science Foundation of China (Nos.61973158, 61673209)the Aeronautical Science Foundation (No.2016ZA52009)
文摘An ant colony optimization with artificial potential field(ACOAPF)algorithm is proposed to solve the cooperative search mission planning problem of unmanned aerial vehicle(UAV)swarm.This algorithm adopts a distributed architecture where each UAV is considered as an ant and makes decision autonomously.At each decision step,the ants choose the next gird according to the state transition rule and update its own artificial potential field and pheromone map based on the current search results.Through iterations of this process,the cooperative search of UAV swarm for mission area is realized.The state transition rule is divided into two types.If the artificial potential force is larger than a threshold,the deterministic transition rule is adopted,otherwise a heuristic transition rule is used.The deterministic transition rule can ensure UAVs to avoid the threat or approach the target quickly.And the heuristics transition rule considering the pheromone and heuristic information ensures the continuous search of area with the goal of covering more unknown area and finding more targets.Finally,simulations are carried out to verify the effectiveness of the proposed ACOAPF algorithm for cooperative search mission of UAV swarm.
基金Sponsored by the Science Foundation for Youths of Heilongjiang province (Grant No.QC08C05)
文摘For real-time and distributed features of multi-robot system,the strategy of combining the improved artificial potential field method and the rules based on priority is proposed to study the collision avoidance planning in multi-robot systems. The improved artificial potential field based on simulated annealing algorithm satisfactorily overcomes the drawbacks of traditional artificial potential field method,so that robots can find a local collision-free path in the complex environment. According to the movement vector trail of robots,collisions between robots can be detected,thereby the collision avoidance rules can be obtained. Coordination between robots by the priority based rules improves the real-time property of multi-robot system. The combination of these two methods can help a robot to find a collision-free path from a starting point to the goal quickly in an environment with many obstacles. The feasibility of the proposed method is validated in the VC-based simulated environment.
基金Projects(30270496,60075019,60575012)supported by the National Natural Science Foundation of China
文摘A novel method was designed to solve reinforcement learning problems with artificial potential field.Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field(APF),which was a very appropriate method to model a reinforcement learning problem.Secondly,a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept.The performance of this new method was tested by a gridworld problem named as key and door maze.The experimental results show that within 45 trials,good and deterministic policies are found in almost all simulations.In comparison with WIERING's HQ-learning system which needs 20 000 trials for stable solution,the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning.Therefore,the new method is simple and effective to give an optimal solution to the reinforcement learning problem.
文摘A novel approach for collision-free path planning of a multiple degree-of-freedom(DOF)articulated robot in a complex environment is proposed.Firstly,based on visual neighbor point(VNP),a numerical artificial potential field is constructed in Cartesian space,which provides the heuristic information,effective distance to the goal and the motion direction for the motion of the robot joints.Secondly,a genetic algorithm,combined with the heuristic rules,is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained.A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles,but also improve the efficiency and quality of path planning.
基金This paper was partly supported by the National Natural Science Foundation (No.60131160741,60334010) of China.
文摘In order to overcome the drawbacks of conventional artificial potential fields (APF) based methods for the motion planning problems of mobile robots in dynamic uncertain environments, an artificial coordinating fields (ACF) based method has been proposed recently. This paper deals with the reachability problem of the ACF, that is, how to design and choose the parameters of the ACF and how the environment should be such that the robot can reach its goal without being trapped in local minima. Some sufficient conditions for these purposes are developed theoretically. Theoretical analyses show that, the ACF can effectively remove local minima in dynamic uncertain environments with V-shape or U-shape obstacles, and guide the mobile robot to reach its goal with some necessary environment constraints and based on the methods provided in this paper to properly choose the parameters of the ACF. Comparisons between the ACF and APF, and simulations are provided to illustrate the advantages of the ACF.
基金Supported by the National High Technology Research and Development Programme of China（ No. 2006AA04Z245 ） and China Postdoctoral Science Foundation （ No. 200904500988 ）.
文摘To overcome the shortcomings of the traditional artificial potential field method in mobile robot path planning, an improved artificial potential field model （IAPFM） was established, then a new path planning method combining the IAPFM with optimization algorithm （trust region algorithm） is proposed. Attractive force between the robot and the target location, and repulsive force between the robot and the obstacles are both converted to the potential field intensity; and filled potential field is used to guide the robot to go out of the local minimum points ; on this basis, the effect of dynamic obstacles velocity and the robot＇s velocity is consid thers and the IAPFM is established, then both the expressions of the attractive potential field and the repulsive potential field are obtained. The trust region algorithm is used to search the minimum value of the sum of all the potential field inten- sities within the movement scope which the robot can arrive in a sampling period. Connecting of all the points which hare the minimum intensity in every sampling period constitutes the global optimization path. Experiment result shows that the method can meet the real-time requirement, and is able to execute the mobile robot path planning task effectively in the dynamic environment.
文摘The method of artificial potential field has obvious advantages among the robot path planning methods including simple structure,small amount of calculation and relatively mature in theory.This paper puts forward the"Integral method"focusing on solving the problem of local minimization.The method analyses the distribution of obstructions in a given environment and regards adjacent obstacles as a whole,By changing the parameters of the repulsive force field,robots can quickly get out of the minimum point and move to the target point.This paper uses the Simurosot platform to carry on the simulation experiment on the improved artificial potential field method,which projects a feasible path successfully and verifies this method.
基金Supported by National Natural Science Foundation of China(Grant No.51775269)Jiangsu Provincial Natural Science Foundation of China(Grant No.BK20211190).
文摘Anti-rollover is a critical factor to consider when planning the motion of autonomous heavy trucks.This paper proposed a method for autonomous heavy trucks to generate a path that avoids collisions and minimizes rollover risk.The corresponding rollover index is deduced from a 5-DOF heavy truck dynamic model that includes longitudinal motion,lateral motion,yaw motion,sprung mass roll motion,unsprung mass roll motion,and an anti-rollover artificial potential field(APF)is proposed based on this.The motion planning method,which is based on model predictive control(MPC),combines trajectory tracking,anti-rollover APF,and the improved obstacle avoidance APF and considers the truck dynamics constraints,obstacle avoidance,and anti-rollover.Furthermore,by using game theory,the coefficients of the two APF functions are optimised,and an optimal path is planned.The effectiveness of the optimised motion planning method is demonstrated in a variety of scenarios.The results demonstrate that the optimised motion planning method can effectively and efficiently avoid collisions and prevent rollover.
基金supported by Gansu Provincial Science and Technology Program Project(No.23JRRA868)Lanzhou Municipal Talent Innovation and Entrepreneurship Project(No.2019-RC-103)。
文摘In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional RRT algorithm used for unmanned aerial vehicle(UAV)path planning in complex environments,an improved bidirectional RRT algorithm was proposed.The algorithm firstly adopted a goal-oriented strategy to guide the sampling points towards the target point,and then the artificial potential field acted on the random tree nodes to avoid collision with obstacles and reduced the length of the search path,and the random tree node growth also combined the UAV’s own flight constraints,and by combining the triangulation method to remove the redundant node strategy and the third-order B-spline curve for the smoothing of the trajectory,the planned path was better.The planned paths were more optimized.Finally,the simulation experiments in complex and dynamic environments showed that the algorithm effectively improved the speed of trajectory planning and shortened the length of the trajectory,and could generate a safe,smooth and fast trajectory in complex environments,which could be applied to online trajectory planning.
基金founded by the National Science and Technology Council of the Republic of China under contract NSTC113-2221-E-019-032.
文摘An Interval Type-2(IT-2)fuzzy controller design approach is proposed in this research to simultaneously achievemultiple control objectives inNonlinearMulti-Agent Systems(NMASs),including formation,containment,and collision avoidance.However,inherent nonlinearities and uncertainties present in practical control systems contribute to the challenge of achieving precise control performance.Based on the IT-2 Takagi-Sugeno Fuzzy Model(T-SFM),the fuzzy control approach can offer a more effective solution for NMASs facing uncertainties.Unlike existing control methods for NMASs,the Formation and Containment(F-and-C)control problem with collision avoidance capability under uncertainties based on the IT-2 T-SFM is discussed for the first time.Moreover,an IT-2 fuzzy tracking control approach is proposed to solve the formation task for leaders in NMASs without requiring communication.This control scheme makes the design process of the IT-2 fuzzy Formation Controller(FC)more straightforward and effective.According to the communication interaction protocol,the IT-2 Containment Controller(CC)design approach is proposed for followers to ensure convergence into the region defined by the leaders.Leveraging the IT-2 T-SFM representation,the analysis methods developed for linear Multi-Agent Systems(MASs)are successfully extended to perform containment analysis without requiring the additional assumptions imposed in existing research.Notably,the IT-2 fuzzy tracking controller can also be applied in collision avoidance situations to track the desired trajectories calculated by the avoidance algorithm under the Artificial Potential Field(APF).Benefiting from the combination of vortex and source APFs,the leaders can properly adjust the system dynamics to prevent potential collision risk.Integrating the fuzzy theory and APFs avoidance algorithm,an IT-2 fuzzy controller design approach is proposed to achieve the F-and-C purposewhile ensuring collision avoidance capability.Finally,amulti-ship simulation is conducted to validate the feasibility and effectiveness of the designed IT-2 fuzzy controller.
基金supported by the Intelligent Aerospace System Leading Innovation Team Program of Zhejiang(2022R01003).
文摘In the realm of missile defense systems,the self-sufficient maneuver capacity of missile swarms is pivotal for their survival.Through the analysis of the missile dynamics model,a time-efficient cooperative attack strategy for missile swarm is proposed.Based on the distribution of the attackers and defenders,the collision avoidance against the defenders is considered during the attack process.By analyzing the geometric relationship between the relative velocity vector and relative position vector of the attackers and defenders,the collision avoidance constrains of attacking swarm are redefined.The key point is on adjusting the relative velocity vectors to fall outside the collision cone.This work facilitates high-precision attack toward the target while keeping safe missing distance between other attackers during collision avoidance process.By leveraging an innovative repulsion artificial function,a time-efficient cooperative attack strategy for missile swarm is obtained.Through rigorous simulation,the effectiveness of this cooperative attack strategy is substantiated.Furthermore,by employing Monte Carlo simulation,the success rate of the cooperative attack strategy is assessesed and the optimal configuration for the missile swarm is deduced.
基金supported by the National Natural Science Foundation of China(No.12072090)。
文摘Aimed at complex distributed no-fly zones avoidance problems,a novel adaptive lateral reentry guidance algorithm is proposed.Firstly,by introducing the improved attractive and repulsive potential fields,an improved artificial potential field method is developed.Combined with the proposed judgment criterion for whether a no-fly zone has been avoided,the proposed improved artificial potential field method effectively solves the reference heading angle determination problem under the constraints of complex distributed no-fly zones.Then,based on the proposed no-fly zone’s threat quantitative evaluation method and the reference heading angle determined by the proposed improved artificial potential field method,the heading corridor is improved to increase its sensitivity to the threat changes of the no-fly zones.Finally,for satisfying the requirements of complex distributed no-fly zones avoidance,a novel guidance logic via improved heading corridor is proposed to update the reference heading corridor adaptively in real time according to the threat and constraint changes of the no-fly zones,and the bank reversal logic is employed to control the lateral motion.The simulation results for nominal and dispersed cases indicate that the proposed guidance algorithm has high robustness,stability,and applicability,and is feasible and effective to deal with the complex distributed no-fly zones avoidance problems.
基金supported by the National Natural Science Foundation of China(No.61233002)the Fundamental Research Funds for the Central Universities(No.N120404019)
文摘In this paper, we focus on circle formation control of multi-agent systems （MAS） with a leader. The circle formation is achieved based on the lead-following and the artificial potential field method. A distributed control law is given to make a group of agents form a circle and consequently achieve an expected angle. Finally, simulation results show that the proposed circle formation strategies are effective.
基金This work was supported by the National Natural Science Foundation of China(71462018,71761018)the Science and Technology Program of Education Department of Jiangxi Province in China(GJJ171503).
文摘An optimal control strategy of winner-take-all(WTA)model is proposed for target tracking and cooperative competition of multi-UAVs(unmanned aerial vehicles).In this model,firstly,based on the artificial potential field method,the artificial potential field function is improved and the fuzzy control decision is designed to realize the trajectory tracking of dynamic targets.Secondly,according to the finite-time convergence high-order differentiator,a double closed-loop UAV speed tracking the controller is designed to realize the speed control and tracking of the target tracking trajectory.Numerical simulation results show that the designed speed tracking controller has the advantages of fast tracking,high precision,strong stability and avoiding chattering.Finally,a cooperative competition scheme of multiple UAVs based on WTA is designed to find the minimum control energy from multiple UAVs and realize the optimal control strategy.Theoretical analysis and numerical simulation results show that the model has the fast convergence,high control accuracy,strong stability and good robustness.
基金The National Natural Science Foundation of China(No.91023024,51175083)Scientific Research Foundation of Graduate School of Southeast University(No.YBJJ1020)Jiangsu Graduate Innovative Research Program(No.CX10B_062Z).
文摘To realize automatic manipulation of micro-particles by light-induced dielectrophoresis （LDEP）, a path-planning scheme based on the improved artificial potential field （APF） for micro light pattern movements is proposed. An algorithm combining guided target and point obstacle based on a new local minimum judging criterion is specially designed, which can solve the local minimum problems encountered by the traditional APF. Experiments of real-time particle manipulation based on this algorithm are implemented and the experimental results show that the proposed approach can overcome the local minimum problems of the traditional APF method, and it is validated to be highly stable for intensive particle obstacles during LDEP manipulation. Consequently, this method can realize real-time manipulation of micro-nano particles with safety, decrease the difficulty of manual manipulation, and thus improve the efficiency of manipulation of micro-particles.
