Due to the interrelationship between the base placement of the manipulator and its operation object,it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base locat...Due to the interrelationship between the base placement of the manipulator and its operation object,it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base location.A new method is presented to optimize the base placement of manipulators through motion planning optimization and location optimization in the feasible area for manipulators.Firstly,research problems and contents are outlined.And then the feasible area for the manipulator base installation is discussed.Next,index depended on the joint movements and used to evaluate the kinematic performance of manipulators is defined.Although the mentioned indices in last section are regarded as the cost function of the latter,rapidly-exploring random tree(RRT) and rapidly-exploring random tree*(RRT*) algorithms are analyzed.And then,the proposed optimization method of manipulator base placement is studied by means of simulation research based on kinematic performance criteria.Finally,the conclusions could be proved effective from the simulation results.展开更多
Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms ...Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.展开更多
This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*...多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*(rapidly-exploring random trees star)算法(back tracking rapidly-exploring random trees star,BT-RRT*),减少无效父节点,并确保各智能体生成优化的无碰撞路径。在协作避障阶段,智能体依据自身的任务优先级制定局部避障策略,避开动态障碍物和其他智能体。实验结果表明,该算法可成功寻找较优路径,还可降低避障时间。展开更多
针对多自由度机械臂在三维空间中轨迹规划的高复杂性、安全性和可靠性等问题,基于快速扩展随机树(rapidly-exploring random trees,RRT)算法在高维空间中的概率完备性和计算轻量性等优势,提出了一种基于均匀概率的目标启发式RRT(target ...针对多自由度机械臂在三维空间中轨迹规划的高复杂性、安全性和可靠性等问题,基于快速扩展随机树(rapidly-exploring random trees,RRT)算法在高维空间中的概率完备性和计算轻量性等优势,提出了一种基于均匀概率的目标启发式RRT(target heuristic RRT based on uniform probability,PH-RRT)方法.首先,该方法基于均匀概率的分配机制选取概率采样阈值作为节点标准,并与随机采样值进行比较.当随机采样值在设定的阈值范围内时,确定目标点为随机点进行节点扩展.当随机采样值在设定的阈值范围外时,随机生成随机点,在目标重力和随机点重力的目标启发式作用下进行节点扩展.然后,在已规划出的路径的基础上,进一步引入广度优先搜索思想,针对规划出的路径进行优化处理,提高了路径平滑度并减少了路径长度.实验结果表明,该方法能较好地解决传统RRT方法固有的盲目搜索问题,减少路径规划时间和路径长度,提高机械臂的路径规划效率.展开更多
Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance...Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree （RRT） algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.展开更多
In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is intr...In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.展开更多
Due to the unique steering mechanism and driving characteristics of the articulated vehicle,a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and...Due to the unique steering mechanism and driving characteristics of the articulated vehicle,a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and searching the path back and forth of the articulated vehicle.First,Support Vector Machine(SVM)theory is used to obtain the two-dimensional optimal zero potential curve and the maximum margin,and then,several key points are selected from the optimal zero potential curves by using Longest Accessible Path(LAP)method.Next,the Cubic Bezier(CB)curve is adopted to connect the curve that satisfies the curvature constraint of the articulated vehicle between every two key points.Finally,Back and Forth Rapidly-exploring Random Tree with Course Correction(BFRRT-CC)is designed to connect paths that do not meet articulated vehicle curvature requirements.Simulation results show that the proposed hybrid path planning method can search a feasible path with a 90-degree turn,which meets the demand for obstacle avoidance and articulated vehicle back-and-forth movement.展开更多
For motion planning of concrete pump truck( CPT) with end-effector's hosepipe path, this paper sets up the mathematic model,including definition of its motion planning,description of its state in C space( configur...For motion planning of concrete pump truck( CPT) with end-effector's hosepipe path, this paper sets up the mathematic model,including definition of its motion planning,description of its state in C space( configuration space) and its path length. An advanced rapidly-exploring random trees( RRT) algorithm is proposed, in which each tracing point dispersed from the end hosepipe path can map multi-states of CPT so as to make variety of motion path of CPT. For increasing search efficiency and motion path quality,this algorithm generates any random states of CPT in certain probability to trend to the initial state or target state mapped with the end hosepipe path,and to have the least cost between this random state and its parent state. A typical case and two special cases are analyzed in which the end hosepipe paths are reciprocating linear trajectory and planar or spatial sine curves respectively. Their results verify the feasibility and validity of the proposed algorithm.展开更多
机械臂运动规划是机器人研究领域的重点,对机械臂能否顺利执行任务非常重要。目前,机械臂运动规划多使用RRT法,然而该方法是在关节空间进行规划,无法适用于机械臂末端执行器存在约束的任务。为了克服这个不足,该文提出了一种任务自由子...机械臂运动规划是机器人研究领域的重点,对机械臂能否顺利执行任务非常重要。目前,机械臂运动规划多使用RRT法,然而该方法是在关节空间进行规划,无法适用于机械臂末端执行器存在约束的任务。为了克服这个不足,该文提出了一种任务自由子空间RRT(rapidly-exploring random tree)法,在末端执行器任务空间的自由子空间中构建RRT,并对其每步扩张进行逆运动学轨迹优化,求解出相应的关节轨迹。此外,由于末端执行器速度对逆运动规划有重要影响,该文在逆运动轨迹优化阶段采用了最似梯度法,不仅考虑了末端执行器的运动速度,而且通过极小关节自由速度和优化目标负梯度的距离,重新确定了关节自由速度,增强了算法的优化能力。实验结果表明:该算法能有效解决机械臂末端存在约束的问题。展开更多
Anytime sampling-based motion planning algorithms are widely used in practical applications due to limited real-time computing resources.The algorithm quickly finds feasible paths and incrementally improves them to th...Anytime sampling-based motion planning algorithms are widely used in practical applications due to limited real-time computing resources.The algorithm quickly finds feasible paths and incrementally improves them to the optimal ones.However,anytime sampling-based algorithms bring a paradox in convergence speed since finding a better path helps prune useless candidates but also introduces unrecognized useless candidates by sampling.Based on the words of homotopy classes,we propose a Homotopy class Informed Preprocessor(HIP)to break the paradox by providing extra information.By comparing the words of path candidates,HIP can reveal wasteful edges of the sampling-based graph before finding a better path.The experimental results obtained in many test scenarios show that HIP improves the convergence speed of anytime sampling-based algorithms.展开更多
This study aims to solve path planning of ntelligent vehicles in self driving In this study,an improved path planning method com-bining constraints of the environment and vehicle is proposed.The algorithm designs a re...This study aims to solve path planning of ntelligent vehicles in self driving In this study,an improved path planning method com-bining constraints of the environment and vehicle is proposed.The algorithm designs a reasonable path cost function,then uses a heuristic guided search strategy to improve the speed and quality of path planning,and finally generates smooth and continuous cur-vature paths based on the path post-processing method focusing on the requirements of path smoothness.A simulation test shows that compared with the basic rapidly-exploring random tree(RRT),RRT-Connect and RRT*algorithms,the path length of the proposed algorithm can be reduced by 19.7%,29.3%and 1%respectively,and the maximum planned path curvature of the proposed algorithm is 0.0796 mr1 and 0.1512 mi respectively.under the condition of a small amount of planning time.The algorithm can plan the more suitable driving path for intelligent vehicles in a complex environment.展开更多
Autonomous flying vehicles(AFVs)are promising future vehicles,which have high obstacle avoidance ability.To plan a feasible path in a wide range of cross-country environments for the AFV,a triggered forward optimal ra...Autonomous flying vehicles(AFVs)are promising future vehicles,which have high obstacle avoidance ability.To plan a feasible path in a wide range of cross-country environments for the AFV,a triggered forward optimal rapidly-exploring random tree(TF-RRT^(*))method is proposed.Firstly,an improved sampling and tree growth mechanism is built.Sampling and tree growth are allowed only in the forward region close to the target point,which significantly improves the planning speed;Secondly,the driving modes(ground-driving mode or air-driving mode)of the AFV are added to the sampling process as a planned state for uniform planning the driving path and driving mode;Thirdly,according to the dynamics and energy consumption models of the AFV,comprehensive indicators with energy consumption and efficiency are established for path optimal procedures,so as to select driving mode and plan driving path reasonably according to the demand.The proposed method is verified by simulations with an actual cross-country environment.Results show that the computation time is decreased by 71.08%compared with Informed-RRT^(*)algorithm,and the path length of the proposed method decreased by 13.01%compared with RRT^(*)-Connect algorithm.展开更多
To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorith...To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed.The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree,and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency.Then the generated path is reversely optimized to shorten the length of the planned path,and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion.Finally,the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2,a large humanoid service robot.展开更多
Rapid path planner plays an important role in autonomous ground vehicle （AGV） operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring ...Rapid path planner plays an important role in autonomous ground vehicle （AGV） operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree （RRT） can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 mx 170 m area with moderate obstacle complexity.展开更多
Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for p...Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree(HQD-RRT^(*)) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree(RRT^(*)-SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic environment can be achieved in this paper. HQD-RRT^(*) algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT^(*) in avoiding unknown obstacles.展开更多
基金Supported by the National Science and Technology Support Program of China(No.2013BAK03B01)
文摘Due to the interrelationship between the base placement of the manipulator and its operation object,it is significant to analyze the accessibility and workspace of manipulators for the optimization of their base location.A new method is presented to optimize the base placement of manipulators through motion planning optimization and location optimization in the feasible area for manipulators.Firstly,research problems and contents are outlined.And then the feasible area for the manipulator base installation is discussed.Next,index depended on the joint movements and used to evaluate the kinematic performance of manipulators is defined.Although the mentioned indices in last section are regarded as the cost function of the latter,rapidly-exploring random tree(RRT) and rapidly-exploring random tree*(RRT*) algorithms are analyzed.And then,the proposed optimization method of manipulator base placement is studied by means of simulation research based on kinematic performance criteria.Finally,the conclusions could be proved effective from the simulation results.
基金supported in part by the National Science Foundation of China(61976175,91648208)the Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(2019JZ-05)。
文摘Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
文摘多智能体路径规划旨在解决多个智能体在同一工作空间内生成无碰撞路径的问题,是智能体无人化工作的关键支撑技术。基于回溯思想和自适应局部避障策略,提出了一种双阶段多智能体路径规划算法。在全局路径规划阶段,基于回溯思想改进的RRT*(rapidly-exploring random trees star)算法(back tracking rapidly-exploring random trees star,BT-RRT*),减少无效父节点,并确保各智能体生成优化的无碰撞路径。在协作避障阶段,智能体依据自身的任务优先级制定局部避障策略,避开动态障碍物和其他智能体。实验结果表明,该算法可成功寻找较优路径,还可降低避障时间。
文摘针对多自由度机械臂在三维空间中轨迹规划的高复杂性、安全性和可靠性等问题,基于快速扩展随机树(rapidly-exploring random trees,RRT)算法在高维空间中的概率完备性和计算轻量性等优势,提出了一种基于均匀概率的目标启发式RRT(target heuristic RRT based on uniform probability,PH-RRT)方法.首先,该方法基于均匀概率的分配机制选取概率采样阈值作为节点标准,并与随机采样值进行比较.当随机采样值在设定的阈值范围内时,确定目标点为随机点进行节点扩展.当随机采样值在设定的阈值范围外时,随机生成随机点,在目标重力和随机点重力的目标启发式作用下进行节点扩展.然后,在已规划出的路径的基础上,进一步引入广度优先搜索思想,针对规划出的路径进行优化处理,提高了路径平滑度并减少了路径长度.实验结果表明,该方法能较好地解决传统RRT方法固有的盲目搜索问题,减少路径规划时间和路径长度,提高机械臂的路径规划效率.
基金supported by National Natural Science Foundation of China(Grant No. 51275047)Fund of National Engineering and Research Center for Commercial Aircraft Manufacturing of China(Grant No. 07205)Specialized Research Fund for the Doctoral Program of Higher Education of China(Grant No. 20091101110010)
文摘Assembly path planning is a crucial problem in assembly related design and manufacturing processes. Sampling based motion planning algorithms are used for computational assembly path planning. However, the performance of such algorithms may degrade much in environments with complex product structure, narrow passages or other challenging scenarios. A computational path planner for automatic assembly path planning in complex 3D environments is presented. The global planning process is divided into three phases based on the environment and specific algorithms are proposed and utilized in each phase to solve the challenging issues. A novel ray test based stochastic collision detection method is proposed to evaluate the intersection between two polyhedral objects. This method avoids fake collisions in conventional methods and degrades the geometric constraint when a part has to be removed with surface contact with other parts. A refined history based rapidly-exploring random tree （RRT） algorithm which bias the growth of the tree based on its planning history is proposed and employed in the planning phase where the path is simple but the space is highly constrained. A novel adaptive RRT algorithm is developed for the path planning problem with challenging scenarios and uncertain environment. With extending values assigned on each tree node and extending schemes applied, the tree can adapts its growth to explore complex environments more efficiently. Experiments on the key algorithms are carried out and comparisons are made between the conventional path planning algorithms and the presented ones. The comparing results show that based on the proposed algorithms, the path planner can compute assembly path in challenging complex environments more efficiently and with higher success. This research provides the references to the study of computational assembly path planning under complex environments.
基金National Natural Science Foundation of China(No.61903291)。
文摘In order to solve the problem of path planning of mobile robots in a dynamic environment,an improved rapidly-exploring random tree^(*)(RRT^(*))algorithm is proposed in this paper.First,the target bias sampling is introduced to reduce the randomness of the RRT^(*)algorithm,and then the initial path planning is carried out in a static environment.Secondly,apply the path in a dynamic environment,and use the initially planned path as the path cache.When a new obstacle appears in the path,the invalid path is clipped and the path is replanned.At this time,there is a certain probability to select the point in the path cache as the new node,so that the new path maintains the trend of the original path to a greater extent.Finally,MATLAB is used to carry out simulation experiments for the initial planning and replanning algorithms,respectively.More specifically,compared with the original RRT^(*)algorithm,the simulation results show that the number of nodes used by the new improved algorithm is reduced by 43.19%on average.
基金This work was supported by the Jiangsu Natural Science Foundation Project BK20170681National Natural Science Foundation of China 51675281.
文摘Due to the unique steering mechanism and driving characteristics of the articulated vehicle,a hybrid path planning method based on the articulated vehicle model is proposed to meet the demand of obstacle avoidance and searching the path back and forth of the articulated vehicle.First,Support Vector Machine(SVM)theory is used to obtain the two-dimensional optimal zero potential curve and the maximum margin,and then,several key points are selected from the optimal zero potential curves by using Longest Accessible Path(LAP)method.Next,the Cubic Bezier(CB)curve is adopted to connect the curve that satisfies the curvature constraint of the articulated vehicle between every two key points.Finally,Back and Forth Rapidly-exploring Random Tree with Course Correction(BFRRT-CC)is designed to connect paths that do not meet articulated vehicle curvature requirements.Simulation results show that the proposed hybrid path planning method can search a feasible path with a 90-degree turn,which meets the demand for obstacle avoidance and articulated vehicle back-and-forth movement.
基金Nature Science Foundation of Liaoning Province,China(No.201102025)Dalian Science and Technology Plan Project,China(Nos.2012A17GX122,2013A16GX111)Fundamental Research Funds for the Central Universities,China(No.DUT14ZD221)
文摘For motion planning of concrete pump truck( CPT) with end-effector's hosepipe path, this paper sets up the mathematic model,including definition of its motion planning,description of its state in C space( configuration space) and its path length. An advanced rapidly-exploring random trees( RRT) algorithm is proposed, in which each tracing point dispersed from the end hosepipe path can map multi-states of CPT so as to make variety of motion path of CPT. For increasing search efficiency and motion path quality,this algorithm generates any random states of CPT in certain probability to trend to the initial state or target state mapped with the end hosepipe path,and to have the least cost between this random state and its parent state. A typical case and two special cases are analyzed in which the end hosepipe paths are reciprocating linear trajectory and planar or spatial sine curves respectively. Their results verify the feasibility and validity of the proposed algorithm.
文摘机械臂运动规划是机器人研究领域的重点,对机械臂能否顺利执行任务非常重要。目前,机械臂运动规划多使用RRT法,然而该方法是在关节空间进行规划,无法适用于机械臂末端执行器存在约束的任务。为了克服这个不足,该文提出了一种任务自由子空间RRT(rapidly-exploring random tree)法,在末端执行器任务空间的自由子空间中构建RRT,并对其每步扩张进行逆运动学轨迹优化,求解出相应的关节轨迹。此外,由于末端执行器速度对逆运动规划有重要影响,该文在逆运动轨迹优化阶段采用了最似梯度法,不仅考虑了末端执行器的运动速度,而且通过极小关节自由速度和优化目标负梯度的距离,重新确定了关节自由速度,增强了算法的优化能力。实验结果表明:该算法能有效解决机械臂末端存在约束的问题。
基金co-supported by the National Natural Science Foundation of China(Nos.61772055,Grant 61872169)the Technical Foundation Project of Ministry of Industry and Information Technology of China(No.JSZL2016601B003)Equipment Preliminary R&D Project of China(No.41402020102)。
文摘Anytime sampling-based motion planning algorithms are widely used in practical applications due to limited real-time computing resources.The algorithm quickly finds feasible paths and incrementally improves them to the optimal ones.However,anytime sampling-based algorithms bring a paradox in convergence speed since finding a better path helps prune useless candidates but also introduces unrecognized useless candidates by sampling.Based on the words of homotopy classes,we propose a Homotopy class Informed Preprocessor(HIP)to break the paradox by providing extra information.By comparing the words of path candidates,HIP can reveal wasteful edges of the sampling-based graph before finding a better path.The experimental results obtained in many test scenarios show that HIP improves the convergence speed of anytime sampling-based algorithms.
基金Supported by Guangdong Natural Science Foundation(Grant No.2015A030310411)Guangdong University Char acteristic Innovation(Grant No.2018KQNCX207)+1 种基金Shaoguan science and technology plan(Grant No.2018sn043)Shaoguan university scientific research(Grant No.sz2018KJ06).
文摘This study aims to solve path planning of ntelligent vehicles in self driving In this study,an improved path planning method com-bining constraints of the environment and vehicle is proposed.The algorithm designs a reasonable path cost function,then uses a heuristic guided search strategy to improve the speed and quality of path planning,and finally generates smooth and continuous cur-vature paths based on the path post-processing method focusing on the requirements of path smoothness.A simulation test shows that compared with the basic rapidly-exploring random tree(RRT),RRT-Connect and RRT*algorithms,the path length of the proposed algorithm can be reduced by 19.7%,29.3%and 1%respectively,and the maximum planned path curvature of the proposed algorithm is 0.0796 mr1 and 0.1512 mi respectively.under the condition of a small amount of planning time.The algorithm can plan the more suitable driving path for intelligent vehicles in a complex environment.
基金National Natural Science Foundation of China(Grant No.51975048,No.52102449).
文摘Autonomous flying vehicles(AFVs)are promising future vehicles,which have high obstacle avoidance ability.To plan a feasible path in a wide range of cross-country environments for the AFV,a triggered forward optimal rapidly-exploring random tree(TF-RRT^(*))method is proposed.Firstly,an improved sampling and tree growth mechanism is built.Sampling and tree growth are allowed only in the forward region close to the target point,which significantly improves the planning speed;Secondly,the driving modes(ground-driving mode or air-driving mode)of the AFV are added to the sampling process as a planned state for uniform planning the driving path and driving mode;Thirdly,according to the dynamics and energy consumption models of the AFV,comprehensive indicators with energy consumption and efficiency are established for path optimal procedures,so as to select driving mode and plan driving path reasonably according to the demand.The proposed method is verified by simulations with an actual cross-country environment.Results show that the computation time is decreased by 71.08%compared with Informed-RRT^(*)algorithm,and the path length of the proposed method decreased by 13.01%compared with RRT^(*)-Connect algorithm.
基金The Key-Area Research and Development Program of Guangdong Province,China(No.2019B010154003)。
文摘To solve the problems of low efficiency and multi-solvability of humanoid manipulator Cartesian space path planning in physical human-robot interaction,an improved bi-directional rapidly-exploring random tree algorithm based on greedy growth strategy in 3D space is proposed.The workspace of manipulator established based on Monte Carlo method is used as the sampling space of the rapidly-exploring random tree,and the opposite expanding greedy growth strategy is added in the random tree expansion process to improve the path planning efficiency.Then the generated path is reversely optimized to shorten the length of the planned path,and the optimized path is interpolated and pose searched in Cartesian space to form a collision-free optimized path suitable for humanoid manipulator motion.Finally,the validity and reliability of the algorithm are verified in an intelligent elderly care service scenario based on Walker2,a large humanoid service robot.
文摘Rapid path planner plays an important role in autonomous ground vehicle （AGV） operation. Depending on the non-holonomic kinematics constraints of AGV, its path planning problem is discussed. Since rapidly-exploring random tree （RRT） can directly take non-holonomic constraints into consideration, it is selected to solve this problem. By applying extra constraints on the movement, the generation of new configuration in RRT algorithm is simplified and accelerated. With section collision detection method applied, collision detection within the planer becomes more accurate and efficient. Then a new path planner is developed. This method complies with the non-holonomic constraints, avoids obstacles effectively and can be rapidly carried out while the vehicle is running. Simulation shows that this path planner can complete path planning in less than 0.5 s for a 170 mx 170 m area with moderate obstacle complexity.
基金supported by the Program for Youth Innovative Research Team in the University of Shandong Province in China(2019KJN010)。
文摘Mobile robots have been used for many industrial scenarios which can realize automated manufacturing process instead of human workers. To improve the quality of the optimal rapidly-exploring random tree(RRT^(*)) for planning path in dynamic environment, a high-quality dynamic rapidly-exploring random tree(HQD-RRT^(*)) algorithm is proposed in this paper, which generates a high-quality solution with optimal path length in dynamic environment. This method proceeds in two stages: initial path generation and path re-planning. Firstly, the initial path is generated by an improved smart rapidly-exploring random tree(RRT^(*)-SMART) algorithm, and the state tree information is stored as prior knowledge. During the process of path execution, a strategy of obstacle avoidance is proposed to avoid moving obstacles. The cost and smoothness of path are considered to re-plan the initial path to improve the path quality in this strategy. Compared with related work, a higher-quality path in dynamic environment can be achieved in this paper. HQD-RRT^(*) algorithm can obtain an optimal path with better stability. Simulations on the static and dynamic environment are conducted to clarify the efficiency of HQD-RRT^(*) in avoiding unknown obstacles.
