Real-Time Collision-Free Path Planning for Robots in Configuration Space

Collision-free path planning for an industrial robot in configuration space requires mapping obstacles from robot‘s workspace into its configuration space.In this paper,an approach to real-time collision-free path planning for robots in configuration space is presented.Obstacle mapping is carried out by fundamental obstacles defined in the workspace and their images in the configuration space.In order to avoid dealing with unimportant parts of the configuration space that do not affect searching a collision-free path between starting and goal configurations,we construct a free subspace by slice configuration obstacles.In this free subspace,the collision-free path is determined by the A^* algorithm.Finally,graphical simulations show the effectiveness of the proposed approach.     

Neural computation for collision-free path planning

Automatic path planning plays an essential role in planning of assembly or disassembly of products, motions of robot manipulators handling part, and material transfer by mobile robots in an intelligent and flexible manufacturing environment. The conventional methodologies based on geometric reasoning suffer not only from the algorithmic difficulty but also from the excessive time complexity in dealing with 3-D path planning. This paper presents a massively parallel, connectionist algorithm for collision-free path planning. The path planning algorithm is based on representing a path as a series ofvia points or beads connected by elastic strings which are subject to displacement due to a potential field or a collision penalty function generated by polyhedral obstacles. Mathematically, this is equivalent to optimizing a cost function, defined in terms of the total path length and the collision penalty function, by moving thevia points simultaneously but individually in the direction that minimizes the cost function. Massive parallelism comes mainly from: (1) the connectionist model representation of obstacles and (2) the parallel computation of individualvia-point motions with only local information. The algorithm has power to deal effectively with path planning of three-dimensional objects with translational and rotational motions. Finally, the algorithm incorporates simulated annealing to solve a local minimum problem. Simulation results are shown.     

The path and location planning of workpieces by genetic algorithms

This paper presents the planning of a near-optimum path and location of a workpiece by genetic algorithms. The purpose of this planning is to minimize the processing time required for a robot to complete its work on a workpiece. The location of the workpiece can be anywhere by translating it along any direction and by rotating it about the fixedz-axis of the robot coordinate system. Owing to the changeable location of the workpiece and the alterable motion time required for a robot to move between two workpoints, the path and location planning problem is much more complicated than the travelling salesman problem. It is definitely impossible to obtain an optimum path and location within an acceptable time. In this paper, genetic algorithms are applied to solve this problem. The location of the workpiece is defined by three position parameters and one angular parameter, and the path is determined based on the values of the parameters for all workpoints. All the path and location parameters are encoded into a binary string. They are modified simultaneously by genetic algorithms to search for a global solution. As the workpiece can be anywhere, a penalty function is used to prevent the selection of illegal paths. Two experiments are given to show the performance of genetic algorithms: one has 30 workpoints and the other has 50 workpoints. Compared with four human-generated plannings, planning by genetic algorithms has much better performance in minimizing the processing time.     

自治水下机器人的自主启发式生物启发神经网络路径规划算法

针对复杂海流环境下自治水下机器人(autonomous underwater vehicle, AUV)的路径规划问题,本文在栅格地图的基础上给出了一种基于离散的生物启发神经网络(Glasius bio-inspired neural networks, GBNN)模型的新型自主启发式路径规划和安全避障算法,并考虑海流对路径规划的影响.首先建立GBNN模型,利用此模型表示AUV的工作环境,神经网络中的每一个神经元与栅格地图中的位置单元一一对应;其次,根据神经网络中神经元的活性输出值分布情况并结合方向信度算法实现自主规划AUV的运动路径;最后根据矢量合成算法确定AUV实际的航行方向.障碍物环境和海流环境下仿真实验结果表明了生物启发模型在AUV水下环境中路径规划的有效性.     

实现机器人动态路径规划的仿真系统

针对机器人动态路径规划问题,提出了在动态环境中移动机器人的一种路径规划方法,适用于环境中同时存在已知和未知,静止和运动障碍物的复杂情况。采用栅格法建立机器人空间模型,整个系统由全局路径规划和局部避碰规划两部分组成。在全局路径规划中,用快速搜索随机树算法规划出初步全局优化路径,局部避碰规划是在全局优化路径的同时,通过基于滚动窗口的环境探测和碰撞规则,对动态障碍物实施有效的局部避碰策略,从而使机器人安全顺利地到达目的地。仿真实验结果说明该方法具有可行性。     

多搬运任务下考虑碰撞避免的AGV路径规划

为提升自动导引小车在“货到人”仓库中的运行效率,针对AGV-托盘任务分配、单AGV路径规划及多AGV碰撞避免三个子问题的研究,以最小化AGV行驶距离为目标构建数学模型。首先,根据AGV与托盘的双边匹配问题特点设计改进的匈牙利算法求解匹配结果。其次,提出一种二维编码机制的改进遗传算法（improved genetic algorithm,IGA）,采用一种局部搜索算子代替原变异操作,在提高算法搜索性能的基础上使其成功应用于单AGV路径规划问题。然后,利用时空数据设计一种三维网格冲突检测方法,并根据商品SKU数量设定AGV的优先级以降低多AGV执行任务时的碰撞概率。最后,在32 m×22 m的仓库中针对不考虑碰撞与考虑碰撞两种情形进行AGV路径优化分析,给出合理的行驶距离和碰撞次数。IGA与标准遗传算法的对比结果显示,IGA能够在合理的时间内获得更高质量的解,行驶距离减少约1.74%,算法求解时间缩短约37.07%。此外,针对AGV数量灵敏度分析,在不同目标托盘规模下测试不同数量的AGV对行驶距离和碰撞次数的影响,发现14~16台AGV数量是最佳配置,验证了模型的可行性和算法的有效性。     

基于二重势函数法的集群航路规划

人工势场法由于其在构型组织能力上的不足,影响了该方法在集群航路规划上的应用,为此提出基于二重势函数法的集群航路规划法,通过第一重势能场形成集群到目标的可行路径,通过第二重势能场形成构型,从而实现集群航路规划.此外,针对人工势场法存在无谓避碰、陷阱问题等不足,通过引入碰撞危险度来确定障碍物影响距离以及虚拟障碍物,提出改进...     

A configuration deactivation algorithm for boosting probabilistic roadmap planning of robots

We present a method to improve the execution time used to build the roadmap in probabilistic roadmap planners. Our method intelligently deactivates some of the configurations during the learning phase and allows the planner to concentrate on those configurations that are most likely going to be useful when building the roadmap. The method can be used with many of the existing sampling algorithms. We ran tests with four simulated robot problems typical in robotics literature. The sampling methods applied were purely random, using Halton numbers, Gaussian distribution, and bridge test technique. In our tests, the deactivation method clearly improved the execution times. Compared with pure random selections, the deactivation method also significantly decreased the size of the roadmap, which is a useful property to simplify roadmap planning tasks.     

A heuristic approach to robot path planning based on task requirements using a genetic algorithm

In order to enhance integration between CAD and robots, wer propose a scheme to plan kinematically feasible paths in the presence of obstacles based on task requirements. Thus, the feasibility of a planned path from a CAD system is assured before the path is sent for execution. The proposed scheme uses a heuristic approach to deal with a rather complex search space, involving high-dimensional C-space obstacles and task requirements specified in Cartesian space. When the robot is trapped by the local minimum in the potential field related to the heuristic, a genetic algorithm is then used to find a proper intermediate location that will guide it to escape out of the local minimum. For demonstration, simulations based on using a PUMA-typed robot manipulator to perform different tasks in the presence of obstacles were conducted. The proposed scheme can also be used for mobile robot planning. The paper falls into Category (5). Please address correspondence to the second author. This work was supported in part by the National Science Council, Taiwan, R.O.C., under grant NSC 82-0422-E-009-403.     

基于生物启发模型的AUV三维自主路径规划与安全避障算法

针对自治水下机器人(AUV)的路径规划问题,在三维栅格地图的基础上,给出一种基于生物启发模型的三维路径规划和安全避障算法. 首先建立三维生物启发神经网络模型,利用此模型表示AUV的三维工作环境,神经网络中的每一个神经元与栅格地图中的位置单元一一对应;然后,根据神经网络中神经元的活性输出值分布情况自主规划AUV的运动路径.静态环境与动态环境下仿真实验结果表明了生物启发模型在AUV三维水下环境中路径规划和安全避障上的有效性.     

动态环境下改进蚁群算法的多Agent路径规划

针对动态环境下的多Agent路径规划问题,提出了一种改进的蚁群算法与烟花算法相结合的动态路径规划方法。通过自适应信息素强度值及信息素缩减因子来加快算法的迭代速度,并利用烟花算法来解决路径规划过程中的死锁问题,避免陷入局部最优。在多Agent动态避碰过程中,根据动态障碍物与多Agent之间的运行轨迹是否相交制定相应的避碰策略,并利用路径转变函数解决多Agent的正面碰撞问题。仿真实验表明,该方法优于经典蚁群算法,能够有效解决多Agent路径规划中的碰撞问题,从而快速找到最优无碰路径。     

基于粒子滤波的一种全局路径规划方法

提出一种基于粒子滤波的全局路径规划方法, 以多段Ferguson样条曲线表示路径确保所得路径光滑且一阶连续. 将最优路径视为真实状态, 将其他路径视为受噪声污染的状态, 从而将最优路径的搜索过程视为状态空间中对真实状态的滤波过程. 以粒子滤波算法依据路径评价函数对状态空间实施滤波获得最优路径, 仿真结果表明该方法实现简单、收敛迅速、且所得到路径光滑, 易于为机器人直接采用.     

Heuristic collision-free path planning for an autonomous platform

In this paper, a heuristic and learning, algorithmic scheme for collision-free navigation is presented. This scheme determines an optimum collision-free navigation path of an autonomous platform by using a trial and error process, past navigation knowledge and current information extracted from the generated surrounding environment.     

Two-way D algorithm for path planning and replanning

Inspired by the Witkowski’s algorithm, we introduce a novel path planning and replanning algorithm — the two-way D^∗ (TWD^∗) algorithm — based on a two-dimensional occupancy grid map of the environment. Unlike the Witkowski’s algorithm, which finds optimal paths only in binary occupancy grid maps, the TWD^∗ algorithm can find optimal paths in weighted occupancy grid maps. The optimal path found by the TWD^∗ algorithm is the shortest possible path for a given occupancy grid map of the environment. This path is more natural than the path found by the standard D^∗ algorithm as it consists of straight line segments with continuous headings. The TWD^∗ algorithm is tested and compared to the D^∗ and Witkowski’s algorithms by extensive simulations and experimentally on a Pioneer 3DX mobile robot equipped with a laser range finder.     

改进萤火虫算法求解多机器人路径规划

为解决多机器人在静态环境中的路径规划问题,以路径长度为优化目标模型,并针对此模型设计了多机器人萤火虫算法(MR-FA)。首先,考虑到路径安全性对环境中的障碍物采取扩张操作,设计初始化规则以提高生成初始种群的效率;其次,根据算法的连续性原理及特点,设计个体等长策略将维度不一致的个体转变为等维度个体以便于萤火虫的移动更新,并对移动更新后的不可行解采取路径修正策略;然后对规划出的每个机器人的移动路径进行碰撞检测,同时针对机器人不同的碰撞情况设计相应的避碰策略,即暂停—回退策略(PFS)、局部路径重规划策略(LPRS);最后,为验证MR-FA的有效性,在三组环境中进行仿真实验并与其他三种算法进行对比,综合得出MR-FA在解决多机器人路径规划时更有优势。     

海流环境下多AUV多目标生物启发任务分配与路径规划算法

针对多障碍物海流环境下多自治水下机器人(AUV)目标任务分配与路径规划问题, 本文在栅格地图构建的 基础上给出了一种基于生物启发神经网络(BINN)模型的新型自主任务分配与路径规划算法, 并考虑海流对路径规 划的影响. 首先建立BINN模型, 利用此模型表示AUV的工作环境, 神经网络中的每一个神经元与栅格地图中的位 置单元一一对应; 接着, 比较每个目标物在BINN地图中所有AUV的活性值, 并选取活性值最大的AUV作为它的获 胜AUV, 实现多AUV任务分配; 最后, 考虑常值海流影响, 根据矢量合成算法确定AUV实际的航行方向, 实现AUV路 径规划与安全避障. 海流环境下仿真实验结果表明了生物启发模型在多AUV水下任务分配与路径规划中的有效性.     

基于自组织LMBPNN的移动机器人路径规划器

提出一种自组织LMBP神经网络,并将之用于移动机器人免碰路径规划。该算法首先用基于距离传感器的底层局部路径规划器生成初始路径,然后用自组织神经网络将该路径进行样本数据分类,之后将自组织神经网络的权值作为LMBP的输出样本,移动机器人的起始点与目标点作为LMBP神经网络的输入样本进行学习。这样,不但解决了三层LMBP样本若庞大则增加存贮、运行成本,以及数据冗余问题,并且随着机器人对未知环境探索的增多,所构建的地图越趋丰满。仿真结果说明该方法很好效。     

A gradient-based path optimization method for motion planning

Most algorithms in probabilistic sampling-based path planning compute collision-free paths made of straight line segments lying in the configuration space. Due to the randomness of sampling, the paths make detours that need to be optimized. The contribution of this paper is to propose a basic gradient-based algorithm that transforms a polygonal collision-free path into a shorter one. While requiring only collision checking, and not any time-consuming obstacle distance computation nor geometry simplification, we constrain only part of the configuration variables that may cause a collision, and not entire configurations. Thus, parasite motions that are not useful for the problem resolution are reduced without any assumption. Experimental results include navigation and manipulation tasks, eg a manipulator arm-filling boxes and a PR2 robot working in a kitchen environment. Comparisons with a random shortcut optimizer and a partial shortcut have also been studied.     

Robotic path planning using evolutionary momentum-based exploration

In this article, we propose a new algorithm to solve the problem of robotic path planning in static environment where the source and destination are given. A grid-based map has been used to represent the robotic world. The basic algorithm is built on an evolutionary approach, where the path evolves along with generations with each generation adding to the maximum possible complexity of the path. Along with complexity we optimise the total path length as well as the minimum distance from the obstacle in the robotic path. It may be seen that the value of evolutionary parameter number of individuals as well as the maximum complexity is less at start and more at the later stages of the algorithm. We use a Gaussian increase in these values whose parameter may be adjusted to control the time and output. Seven genetic operators have been implemented that include selection, crossover, soft mutation, hard mutation, insert, delete and elite. The phenotype representation consists of the coordinate where the robot is supposed to make a turn. This happens by the traversal of the path using these points by the evolutionary algorithm. Momentum determines the speed of the algorithm in this traversal.     

基于遗传算法的机械臂实时避障路径规划研究

针对模块化机械臂在运行时可能与工作空间中的障碍物发生碰撞的问题, 提出一种基于遗传算法的避障路径规划算法。首先采用D-H(Denavit-Hartenberg)表示法对机械臂进行建模, 并进行运动学和动力学分析, 建立机械臂运动学和动力学方程。在此基础上, 利用遗传算法分别在单/多个障碍物工作环境中, 以运动的时间、移动的空间距离和轨迹长度作为优化指标, 实现机械臂避障路径规划的优化。通过仿真验证了基于遗传算法的机械臂避障路径规划算法的有效性与可行性, 该算法提高了运行中的机械臂有效避开工作空间中障碍物的效率。