During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive...During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h2/(4√2),where h is the piecewise length.展开更多
This paper considers the tracking problem of a fleet of unmanned surface vehicles(USVs)subject to low state feedback frequencies,disturbances,and communication delays.Influenced by the high computational complexity of...This paper considers the tracking problem of a fleet of unmanned surface vehicles(USVs)subject to low state feedback frequencies,disturbances,and communication delays.Influenced by the high computational complexity of localization algorithms,the low-frequency state feedback brings challenges in fulfilling the high-frequency control requirement for USVs.Therefore,a novel self-triggered distributed model predictive control(ST-DMPC)approach,with a codesign dual-model control strategy,is proposed.By simultaneously optimizing control inputs and triggering intervals,this approach achieves expected control performance comparable to high-frequency control under low state feedback frequencies.Furthermore,sufficient conditions for ensuring recursive feasibility and closed-loop system stability are derived.Finally,a numerical experiment and comparison study are conducted to demonstrate the efficacy of the proposed approach.展开更多
A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirement...A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A*algorithm with the dynamic window approach(DWA).The traditional A*algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A*path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.展开更多
Robotic fingers, which are the key parts of robot hand, are divided into two main kinds: dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fing...Robotic fingers, which are the key parts of robot hand, are divided into two main kinds: dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called "gesture-changeable under-actuated" (GCUA) function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers: a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism. Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.展开更多
This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequentia...This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.展开更多
A new extension of the conventional adaptive fuzzy sliding mode control(AFSMC) scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output(MIMO) systems, is presented. In particular,...A new extension of the conventional adaptive fuzzy sliding mode control(AFSMC) scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output(MIMO) systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.展开更多
Adaptive motion/force tracking control is considered for a class of mobile manipulators with affine constraints and under-actuated joints in the presence of uncertainties in this paper. Dynamic equation of mobile mani...Adaptive motion/force tracking control is considered for a class of mobile manipulators with affine constraints and under-actuated joints in the presence of uncertainties in this paper. Dynamic equation of mobile manipulator is transformed into a controllable form based on dynamic coupling technique. In view of the asymptotic tracking idea and adaptive theory, adaptive controllers are proposed to achieve the desired control objective. Detailed simulation results confirm the validity of the control strategy.展开更多
As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehi...As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.展开更多
This paper investigates fault tolerant attitude control theory and experiment for underactuated spacecraft with one reaction wheel completely broken and two others suffering actuator faults of partial loss of effectiv...This paper investigates fault tolerant attitude control theory and experiment for underactuated spacecraft with one reaction wheel completely broken and two others suffering actuator faults of partial loss of effectiveness or bias.A non-smooth robust adaptive fault tolerant control law is proposed under the zero-momentum and input saturation conditions.It shows that the available reaction wheels need to produce sufficient control torque for the fault tolerance.Such a new control method is implemented in a semi-physical simulation system of an air-bearing platform.Experimental results show the effectiveness of the proposed method in spacecraft practical engineering.展开更多
面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep dete...面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)提出一种全局路径规划算法(TD3-RRT)。结合RRT算法与深度强化学习建立USV路径搜索模型,利用前视探测感知环境以自适应调整扩展步长,通过策略网络输出路径搜索方向,解决RRT算法扩展盲目的问题;改进后见经验回放策略,通过重选虚拟目标、双经验回放池采样等策略以增强复杂环境下路径搜索能力;通过奖励函数提高规划路径质量,加快路径搜索速度。实验结果表明:不同环境下TD3-RRT相比当前主流算法能够有效提高规划成功率,优化转向角度、路径长度和规划时间,证明了改进算法能有效加快路径搜索速度并提高路径质量,且对不同环境具有良好适应性。展开更多
基金supported by the National Natural Sci‐ence Foundation of China(Grant No.62306325)。
摘要During the use of robotics in applications such as antiterrorism or combat,a motion-constrained pursuer vehicle,such as a Dubins unmanned surface vehicle(USV),must get close enough(within a prescribed zero or positive distance)to a moving target as quickly as possible,resulting in the extended minimum-time intercept problem(EMTIP).Existing research has primarily focused on the zero-distance intercept problem,MTIP,establishing the necessary or sufficient conditions for MTIP optimality,and utilizing analytic algorithms,such as root-finding algorithms,to calculate the optimal solutions.However,these approaches depend heavily on the properties of the analytic algorithm,making them inapplicable when problem settings change,such as in the case of a positive effective range or complicated target motions outside uniform rectilinear motion.In this study,an approach employing a high-accuracy and quality-guaranteed mixed-integer piecewise-linear program(QG-PWL)is proposed for the EMTIP.This program can accommodate different effective interception ranges and complicated target motions(variable velocity or complicated trajectories).The high accuracy and quality guarantees of QG-PWL originate from elegant strategies such as piecewise linearization and other developed operation strategies.The approximate error in the intercept path length is proved to be bounded to h2/(4√2),where h is the piecewise length.
基金supported in part by the National Natural Science Foundation of China(NSFC)under Grant Nos.U24B20183,62273281U22B2039.
摘要This paper considers the tracking problem of a fleet of unmanned surface vehicles(USVs)subject to low state feedback frequencies,disturbances,and communication delays.Influenced by the high computational complexity of localization algorithms,the low-frequency state feedback brings challenges in fulfilling the high-frequency control requirement for USVs.Therefore,a novel self-triggered distributed model predictive control(ST-DMPC)approach,with a codesign dual-model control strategy,is proposed.By simultaneously optimizing control inputs and triggering intervals,this approach achieves expected control performance comparable to high-frequency control under low state feedback frequencies.Furthermore,sufficient conditions for ensuring recursive feasibility and closed-loop system stability are derived.Finally,a numerical experiment and comparison study are conducted to demonstrate the efficacy of the proposed approach.
基金supported by the National Nature Science Foundation of China(62203299,62373246,62388101)the Research Fund of State Key Laboratory of Deep-Sea Manned Vehicles(2024SKLDMV04)+1 种基金the Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(SL2023MS007)the Startup Fund for Young Faculty at SJTU(24X010502929)。
摘要A safe and reliable path planning algorithm is fundamental for unmanned surface vehicles(USVs)to perform autonomous navigation tasks.However,a single global or local planning strategy cannot fully meet the requirements of complex maritime environments.Global planning alone cannot effectively handle dynamic obstacles,while local planning alone may fall into local optima.To address these issues,this paper proposes a multi-dynamic-obstacle avoidance path planning method that integrates an improved A*algorithm with the dynamic window approach(DWA).The traditional A*algorithm often generates paths that are too close to obstacle boundaries and contain excessive turning points,whereas the traditional DWA tends to skirt densely clustered obstacles,resulting in longer routes and insufficient dynamic obstacle avoidance.To overcome these limitations,improved versions of both algorithms are developed.Key points extracted from the optimized A*path are used as intermediate start-destination pairs for the improved DWA,and the weights of the DWA evaluation function are adjusted to achieve effective fusion.Furthermore,a multi-dynamic-obstacle avoidance strategy is designed for complex navigation scenarios.Simulation results demonstrate that the USV can adaptively switch between dynamic obstacle avoidance and path tracking based on obstacle distribution,validating the effectiveness of the proposed method.
基金supported by National Natural Science Foundation of China (No. 50905093)National Hi-tech Research and Development Program of China(863 Program,Grant No.2007AA04Z258)
摘要Robotic fingers, which are the key parts of robot hand, are divided into two main kinds: dexterous fingers and under-actuated fingers. Although dexterous fingers are agile, they are too expensive. Under-actuated fingers can grasp objects self-adaptively, which makes them easy to control and low cost, on the contrary, under-actuated function makes fingers feel hard to grasp things agilely enough and make many gestures. For the purpose of designing a new finger which can grasp things dexterously, perform many gestures and feel easy to control and maintain, a concept called "gesture-changeable under-actuated" (GCUA) function is put forward. The GCUA function combines the advantages of dexterous fingers and under-actuated fingers: a pre-bending function is embedded into the under-actuated finger. The GCUA finger can not only perform self-adaptive grasping function, but also actively bend the middle joint of the finger. On the basis of the concept, a GCUA finger with 2 joints is designed, which is realized by the coordination of screw-nut transmission mechanism, flexible drawstring constraint and pulley-belt under-actuated mechanism. Principle analyses of its grasping and the design optimization of the GCUA finger are given. An important problem of how to stably grasp an object which is easy to glide is discussed. The force analysis on gliding object in grasping process is introduced in detail. A GCUA finger with 3 joints is developed. Many experiments of grasping different objects by of the finger were carried out. The experimental results show that the GCUA finger can effectively realize functions of pre-bending and self-adaptive grasping, the grasping processes are stable. The GCUA finger excels under-actuated fingers in dexterity and gesture actions and it is easier to control and cheaper than dexterous hands, becomes the third kinds of finger.
基金supported in part by the Research Grants Council of the Hong Kong Special Administrative Region of China(11202318,11203721)the Australian Research Council(DP200100700)。
摘要This paper addresses a major issue in planning the trajectories of under-actuated autonomous vehicles based on neurodynamic optimization.A receding-horizon vehicle trajectory planning task is formulated as a sequential global optimization problem with weighted quadratic navigation functions and obstacle avoidance constraints based on given vehicle goal configurations.The feasibility of the formulated optimization problem is guaranteed under derived conditions.The optimization problem is sequentially solved via collaborative neurodynamic optimization in a neurodynamics-driven trajectory planning method/procedure.Simulation results with under-actuated unmanned wheeled vehicles and autonomous surface vehicles are elaborated to substantiate the efficacy of the neurodynamics-driven trajectory planning method.
摘要A new extension of the conventional adaptive fuzzy sliding mode control(AFSMC) scheme, for the case of under-actuated and uncertain affine multiple-input multiple-output(MIMO) systems, is presented. In particular, the assumption for non-zero diagonal entries of the input gain matrix of the plant is relaxed. In other words, the control effect of one actuator can propagate from a subgroup of canonical state equations to the rest of equations in an indirect sense. The asymptotic stability of the proposed AFSM control method is proved using a Lyapunov-based methodology. The effectiveness of the proposed method for the case of under-actuated systems is investigated in the presence of plant uncertainties and disturbances, through simulation studies.
基金supported by National Natural Science Foundation of China (Nos.61273091 and 61403228)Project of Taishan Scholar of Shandong Province of ChinaPh.D.Programs Foundation of Ministry of Education of China (No.20123705110002)
摘要Adaptive motion/force tracking control is considered for a class of mobile manipulators with affine constraints and under-actuated joints in the presence of uncertainties in this paper. Dynamic equation of mobile manipulator is transformed into a controllable form based on dynamic coupling technique. In view of the asymptotic tracking idea and adaptive theory, adaptive controllers are proposed to achieve the desired control objective. Detailed simulation results confirm the validity of the control strategy.
基金supported in part by the National Natural Science Foundation of China under Grants 62071189,62201220 and 62171189by the Key Research and Development Program of Hubei Province under Grant 2021BAA026 and 2020BAB120。
摘要As the problem of surface garbage pollution becomes more serious,it is necessary to improve the efficiency of garbage inspection and picking rather than traditional manual methods.Due to lightness,Unmanned Aerial Vehicles(UAVs)can traverse the entire water surface in a short time through their flight field of view.In addition,Unmanned Surface Vessels(USVs)can provide battery replacement and pick up garbage.In this paper,we innovatively establish a system framework for the collaboration between UAV and USVs,and develop an automatic water cleaning strategy.First,on the basis of the partition principle,we propose a collaborative coverage path algorithm based on UAV off-site takeoff and landing to achieve global inspection.Second,we design a task scheduling and assignment algorithm for USVs to balance the garbage loads based on the particle swarm optimization algorithm.Finally,based on the swarm intelligence algorithm,we also design an autonomous obstacle avoidance path planning algorithm for USVs to realize autonomous navigation and collaborative cleaning.The system can simultaneously perform inspection and clearance tasks under certain constraints.The simulation results show that the proposed algorithms have higher generality and flexibility while effectively improving computational efficiency and reducing actual cleaning costs compared with other schemes.
基金supported by the National Natural Science Foundation of China(Nos.62073165 and 62233009)the 111 Project,China(No.B20007).
摘要This paper investigates fault tolerant attitude control theory and experiment for underactuated spacecraft with one reaction wheel completely broken and two others suffering actuator faults of partial loss of effectiveness or bias.A non-smooth robust adaptive fault tolerant control law is proposed under the zero-momentum and input saturation conditions.It shows that the available reaction wheels need to produce sufficient control torque for the fault tolerance.Such a new control method is implemented in a semi-physical simulation system of an air-bearing platform.Experimental results show the effectiveness of the proposed method in spacecraft practical engineering.
摘要面对多障碍、大尺寸障碍、狭窄通道等特殊环境下的USV路径规划问题,快速扩展随机树算法(rapidly-exploring random trees,RRT)存在采样基数大、规划成功率低、规划路径曲折等缺点。基于双延迟深度确定性策略梯度(twin delayed deep deterministic policy gradient,TD3)提出一种全局路径规划算法(TD3-RRT)。结合RRT算法与深度强化学习建立USV路径搜索模型,利用前视探测感知环境以自适应调整扩展步长,通过策略网络输出路径搜索方向,解决RRT算法扩展盲目的问题;改进后见经验回放策略,通过重选虚拟目标、双经验回放池采样等策略以增强复杂环境下路径搜索能力;通过奖励函数提高规划路径质量,加快路径搜索速度。实验结果表明:不同环境下TD3-RRT相比当前主流算法能够有效提高规划成功率,优化转向角度、路径长度和规划时间,证明了改进算法能有效加快路径搜索速度并提高路径质量,且对不同环境具有良好适应性。