This paper introduces a unified planning framework for composite hybrid aerial/terrestrial precision manipulators(CHAT-PM),enabling energy-efficient trimodal navigation in complex environments.The proposed path search...This paper introduces a unified planning framework for composite hybrid aerial/terrestrial precision manipulators(CHAT-PM),enabling energy-efficient trimodal navigation in complex environments.The proposed path search algorithm leverages terrain-specific heuristics and motion primitives to prioritize energy-saving terrestrial/inclined paths while dynamically selecting optimal motion modes.To address the computational complexity of trimodal dynamics,a unified nonlinear model predictive control(NMPC)approach is developed by integrating complementary constraints,thereby eliminating mixed-integer optimization and unifying aerial-terrestrial-inclined dynamics under a single framework.The dynamics model further incorporates disturbances from robotic arm operations,ground/slope contact forces,and aerodynamic effects,enhancing trajectory accuracy.Finally,the effectiveness and superiority of the proposed method are demonstrated by simulation results and real-world experiments.展开更多
In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propag...In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propagation paths with their lengths if faults occur.First,the physical structure system,function behavior,and complex network theory are integrated to define a system structural-action network(SSAN).Second,based on the concept of SSAN,two properties of nodes and edges,i.e.,the topological property and reliability property,are combined to define the failure propagation property.Third,the proposed MPPS model provides all fault propagation paths and possible failure rates of nodes on these paths.Finally,numerical experiments have been implemented to show the accuracy and advancement compared with the methods of Function Space Iteration(FSI)and the algorithm of Ant Colony Optimization(ACO).展开更多
Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned pa...Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.展开更多
Machining quality of clean-up region has a strong influence on the performances of the impeller. In order to plan clean-up tool paths rapidly and obtain good finish surface quality, an efficient and robust tool path g...Machining quality of clean-up region has a strong influence on the performances of the impeller. In order to plan clean-up tool paths rapidly and obtain good finish surface quality, an efficient and robust tool path generation method is presented, which employs an approach based on point-searching. The clean-up machining mentioned in this paper is pencil-cut and multilayer fillet-cut for a free-form model with a ball-end cutter. For pencil-cut, the cutter center position can be determined via judging whether it satisfies the distance requirement. After the searching direction and the tracing direction have been determined, by employing the point-searching algorithm with the idea of dichotomy, all the cutter contact （CC） points and cutter location （CL） points can be found and the clean-up boundaries can also be defined rapidly. Then the tool path is generated. Based on the main concept of pencil-cut, a multilayer fillet-cut method is proposed, which utilizes a ball-end cutter with its radius less than the design radius of clean-up region. Using a sequence of intermediate virtual cutters to divide the clean-up region into several layers and given a cusp-height tolerance for the final layer, then the tool paths for all layers are calculated. Finally, computer implementation is also presented in this paper, and the result shows that the proposed method is feasible.展开更多
Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical ...Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.展开更多
Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （RE...Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （REA＊）, is presented that improves the performance of A＊ significantly. REA＊ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles （instead of individual points within those boundaries） are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A＊. REA＊ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA＂ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA＊ can speed up a highly optimized A＊ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A＊.展开更多
自重构波状爬行(self-reconfiguration wave-like crawling,SWC)机器人具有特殊的串/并联连接状态,其在运动规划过程中更需要生成连续可行的轨迹。传统运动规划算法存在效率低下、生成路径不符合运动学约束的问题。本文提出了一种改进...自重构波状爬行(self-reconfiguration wave-like crawling,SWC)机器人具有特殊的串/并联连接状态,其在运动规划过程中更需要生成连续可行的轨迹。传统运动规划算法存在效率低下、生成路径不符合运动学约束的问题。本文提出了一种改进的信息增强快速探索随机树运动规划方法。首先以RRT-Connect(rapidly-exploring random tree connect)算法为基础,生成初始可行路径,构建椭圆状态空间采样域,实现随机树的快速生长。其次,基于最小化的加加速度目标函数和Hessian矩阵优化多项式轨迹,生成符合SWC机器人运动学特性的平滑轨迹。最后,基于不同的障碍物场景进行路径规划仿真,以验证优化后算法的效果。仿真结果表明,相较于传统算法,所提方法在多种障碍物环境中可显著提升路径规划效率,缩短全局采样时间和规划路径长度,并可有效地避免SWC机器人运动过程中的急加速转弯,消除路径中的尖锐转折,更符合实际作业需求。展开更多
基金supported in part by the National Natural Science Foundation of China(Grant Nos.62225305,62403169,623B2029,52025054,52435001,623B2029)the State Key Laboratory of Robotics and System(HIT)。
文摘This paper introduces a unified planning framework for composite hybrid aerial/terrestrial precision manipulators(CHAT-PM),enabling energy-efficient trimodal navigation in complex environments.The proposed path search algorithm leverages terrain-specific heuristics and motion primitives to prioritize energy-saving terrestrial/inclined paths while dynamically selecting optimal motion modes.To address the computational complexity of trimodal dynamics,a unified nonlinear model predictive control(NMPC)approach is developed by integrating complementary constraints,thereby eliminating mixed-integer optimization and unifying aerial-terrestrial-inclined dynamics under a single framework.The dynamics model further incorporates disturbances from robotic arm operations,ground/slope contact forces,and aerodynamic effects,enhancing trajectory accuracy.Finally,the effectiveness and superiority of the proposed method are demonstrated by simulation results and real-world experiments.
基金Project(2017JBZ103)supported by the Fundamental Research Funds for the Central Universities,China
文摘In view of the structure and action behavior of mechatronic systems,a method of searching fault propagation paths called maximum-probability path search(MPPS)is proposed,aiming to determine all possible failure propagation paths with their lengths if faults occur.First,the physical structure system,function behavior,and complex network theory are integrated to define a system structural-action network(SSAN).Second,based on the concept of SSAN,two properties of nodes and edges,i.e.,the topological property and reliability property,are combined to define the failure propagation property.Third,the proposed MPPS model provides all fault propagation paths and possible failure rates of nodes on these paths.Finally,numerical experiments have been implemented to show the accuracy and advancement compared with the methods of Function Space Iteration(FSI)and the algorithm of Ant Colony Optimization(ACO).
基金supported by the National Natural Science Foundation of China(No.52272382)the Fundamental Research Funds for the Central Universities,China。
文摘Path planning is crucial for autonomous flight of fixed-wing Unmanned Aerial Vehicles(UAVs).However,due to the high-speed flight and complex control of fixed-wing UAVs,ensuring the feasibility and safety of planned paths in complex environments is challenging.This paper proposes a feasible path planning algorithm named Closed-loop Radial Ray A^(*)(CL-RaA^(*)).The core components of the CL-RaA^(*)include an adaptive variable-step-size path search and a just-in-time expansion primitive.The former enables fast path search in complex environments,while the latter ensures the feasibility of the generated paths.By integrating these two components and conducting safety checks on the trajectories to be expanded,the CL-RaA^(*)can rapidly generate safe and feasible paths that satisfy the differential constraints that comprehensively consider the dynamics and control characteristics of six-degree-of-freedom fixed-wing UAVs.The final performance tests and simulation validations demonstrate that the CL-RaA^(*)can generate safe and feasible paths in various environments.Compared to feasible path planning algorithms that use the rapidlyexploring random trees,the CL-RaA^(*)not only ensures deterministic planning results in the same scenarios but also generates smoother feasible paths for fixed-wing UAVs more efficiently.In environments with dense grid obstacles,the feasible paths generated by the CL-RaA^(*)are more conducive to UAV tracking compared to those planned using Dubins curves.
基金National Natural Science Foundation of China （51005183） National Science and Technology Major Project （2011X04016-031）
文摘Machining quality of clean-up region has a strong influence on the performances of the impeller. In order to plan clean-up tool paths rapidly and obtain good finish surface quality, an efficient and robust tool path generation method is presented, which employs an approach based on point-searching. The clean-up machining mentioned in this paper is pencil-cut and multilayer fillet-cut for a free-form model with a ball-end cutter. For pencil-cut, the cutter center position can be determined via judging whether it satisfies the distance requirement. After the searching direction and the tracing direction have been determined, by employing the point-searching algorithm with the idea of dichotomy, all the cutter contact （CC） points and cutter location （CL） points can be found and the clean-up boundaries can also be defined rapidly. Then the tool path is generated. Based on the main concept of pencil-cut, a multilayer fillet-cut method is proposed, which utilizes a ball-end cutter with its radius less than the design radius of clean-up region. Using a sequence of intermediate virtual cutters to divide the clean-up region into several layers and given a cusp-height tolerance for the final layer, then the tool paths for all layers are calculated. Finally, computer implementation is also presented in this paper, and the result shows that the proposed method is feasible.
文摘Aiming at the practical application of Unmanned Underwater Vehicle(UUV)in underwater combat,this paper proposes a battlefield ambush scene with UUV considering ocean current.Firstly,by establishing these mathematical models of ocean current environment,target movement,and sonar detection,the probability calculation methods of single UUV searching target and multiple UUV cooperatively searching target are given respectively.Then,based on the Hybrid Quantum-behaved Particle Swarm Optimization(HQPSO)algorithm,the path with the highest target search probability is found.Finally,through simulation calculations,the influence of different UUV parameters and target parameters on the target search probability is analyzed,and the minimum number of UUVs that need to be deployed to complete the ambush task is demonstrated,and the optimal search path scheme is obtained.The method proposed in this paper provides a theoretical basis for the practical application of UUV in the future combat.
基金supported by the National Natural Science Foundation of China (No.61573283)
文摘Search speed, quality of resulting paths and the cost of pre-processing are the principle evaluation metrics of a pathfinding algorithm. In this paper, a new algorithm for grid-based maps, rectangle expansion A＊ （REA＊）, is presented that improves the performance of A＊ significantly. REA＊ explores maps in units of unblocked rectangles. All unnecessary points inside the rectangles are pruned and boundaries of the rectangles （instead of individual points within those boundaries） are used as search nodes. This makes the algorithm plot fewer points and have a much shorter open list than A＊. REA＊ returns jump and grid-optimal path points, but since the line of sight between jump points is protected by the unblocked rectangles, the resulting path of REA＂ is usually better than grid-optimal. The algorithm is entirely online and requires no offline pre-processing. Experimental results for typical benchmark problem sets show that REA＊ can speed up a highly optimized A＊ by an order of magnitude and more while preserving completeness and optimality. This new algorithm is competitive with other highly successful variants of A＊.
文摘自重构波状爬行(self-reconfiguration wave-like crawling,SWC)机器人具有特殊的串/并联连接状态,其在运动规划过程中更需要生成连续可行的轨迹。传统运动规划算法存在效率低下、生成路径不符合运动学约束的问题。本文提出了一种改进的信息增强快速探索随机树运动规划方法。首先以RRT-Connect(rapidly-exploring random tree connect)算法为基础,生成初始可行路径,构建椭圆状态空间采样域,实现随机树的快速生长。其次,基于最小化的加加速度目标函数和Hessian矩阵优化多项式轨迹,生成符合SWC机器人运动学特性的平滑轨迹。最后,基于不同的障碍物场景进行路径规划仿真,以验证优化后算法的效果。仿真结果表明,相较于传统算法,所提方法在多种障碍物环境中可显著提升路径规划效率,缩短全局采样时间和规划路径长度,并可有效地避免SWC机器人运动过程中的急加速转弯,消除路径中的尖锐转折,更符合实际作业需求。
