Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's tactile sensor. This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.Supported in part by the National Science Foundation Grant DMC-8519542.     

Path-planning strategies for a point mobile automaton moving amidst unknown obstacles of arbitrary shape

The problem of path planning for an automaton moving in a two-dimensional scene filled with unknown obstacles is considered. The automaton is presented as a point; obstacles can be of an arbitrary shape, with continuous boundaries and of finite size; no restriction on the size of the scene is imposed. The information available to the automaton is limited to its own current coordinates and those of the target position. Also, when the automaton hits an obstacle, this fact is detected by the automaton's “tactile sensor.” This information is shown to be sufficient for reaching the target or concluding in finite time that the target cannot be reached. A worst-case lower bound on the length of paths generated by any algorithm operating within the framework of the accepted model is developed; the bound is expressed in terms of the perimeters of the obstacles met by the automaton in the scene. Algorithms that guarantee reaching the target (if the target is reachable), and tests for target reachability are presented. The efficiency of the algorithms is studied, and worst-case upper bounds on the length of generated paths are produced.     

部分未知环境中移动机器人动态路径规划方法

针对部分未知环境,提出一种基于粒子滤波的动态路径规划方法.将全局最优路径视为受机器人运动及环境影响的变化量,采用粒子滤波算法,利用机器人运动信息预测路径,并利用实时环境信息更新路径,通过在线跟踪全局最优路径获得不断更新的全局优化路径.将传统全局路径规划先规划后执行的模式改为边规划边执行的模式,既减少了等待时间,又为机器人的移动误差及部分未知环境提供了较强的适应能力.仿真及实验验证,该方法的有效性.     

Dynamic optimal payload path planning of mobile manipulators among moving obstacles

This paper is dealt with dynamic analysis of the wheeled mobile manipulators in the presence of moving obstacles considering optimal payload criterion. General dynamic formulation of the system was derived, and the moving obstacle avoidance strategy was proposed in terms of dynamic potential functions. The problem of dynamic motion planning and payload maximization was formulated using open-loop optimal control theory. Then, the indirect solution based on Pontryagin’s minimum principle was employed to solve the problem. Using the proposed method, complete nonlinear states and control constraints were treated without any simplifications such as linearizing the dynamics equations, discretizing the robot’s workspace, or parameterizing the solution. The proposed method will be useful for the system design and in the situation where the trajectories of obstacles are predefined. Finally, capability and applicability of the proposed method were investigated by the number of simulations on a two-link mobile manipulator.     

面向未知地图的六足机器人路径规划算法

针对移动机器人路径规划中无法准确得知全局地图的问题,提出了一种基于模糊规则和人工势场法的局部路径规划算法。首先,利用测距组与模糊规则,进行障碍物的形状分类,构建局部地图;其次,在人工势场法中引入了一种修正的斥力函数,基于局部地图,利用人工势场法进行局部路径规划;最后,随着机器人的运动,设置时间断点,以减少路径震荡。针对随机障碍物和凹凸障碍物的地图,分别采用传统人工势场法和改进的人工势场法进行仿真,其结果表明：在遇到随机障碍物时,相比传统人工势场法,改进的人工势场法能够显著减少与障碍物的碰撞;在遇到凹凸障碍物时,改进的人工势场法能够很好地完成路径规划的目标。所提算法对地形变化适应能力强,能够实现在未知地图下的六足机器人路径规划。     

Automatic path planning for a mobile robot among obstacles ofarbitrary shape

A novel and fast path planning method for a mobile robot (MR) among objects of arbitrary shape is described. It comprises two phases. During the first phase, the graph including all possible collision-free paths from a top view of the environment is first obtained. During the second phase, the optimal path for the MR is then selected. For this, the proposed method uses both the fast distance transformation (FDT) and variations of some topological methods as thinning and skeletonization, to obtain the free space skeleton. Unlike conventional methods, the proposed approach is capable to include the MR and the target intrinsically in the path and, at the same time it obtains the collision-free path's graph, taking advantage of the topological concept of hole. We propose to use a logical operator over the FDT instead of the classical morphologic operators over the discrete array (erosion and dilation), to obtain a much faster algorithm. The optimal path (in terms of length) is next selected and smoothed by conventional algorithms. The resultant path is finally used as a reference by the mobile robot     

Velocity potential approach to path planning for avoiding moving obstacles

This paper describes the theory and an experiment of a velocity potential approach to path planning and avoiding moving obstacles for an autonomous mobile robot by use of the Laplace potential. This new navigation function for path planning is feasible for guiding a mobile robot avoiding arbitrarily moving obstacles and reaching the goal in real time. The essential feature of the navigation function comes from the introduction of fluid flow dynamics into the path planning. The experiment is conducted to verify the effectiveness of the navigation function for obstacle avoidance in a real world. Two examples of the experiment are presented; first, the avoidance of a moving obstacle in parallel line-bounded space, and second, the avoidance of one moving obstacle and another standing obstacle. The robot can reach the goal after successfully avoiding the obstacles in these cases.     

Multi-agent based path planning for first responders among moving obstacles

Natural or man-made disasters can cause different kinds of moving obstacles (e.g., fires, plumes, floods), which make some parts of the road network temporarily unavailable. After such incidents occur, responders have to go to different destinations to perform their tasks in the environment affected by the disaster. Therefore they need a path planner that is capable of dealing with such moving obstacles, as well as generating and coordinating their routes quickly and efficiently. In this paper, we present a novel approach for using a multi-agent system for navigating one or multiple responders to one or multiple destinations in the presence of moving obstacles. Our navigation system supports information collection from hazard simulations, spatio-temporal data processing and analysis, connection with a geo-database, and route generation in dynamic environments affected by disasters. We design and develop a set of software geospatial agents that assist emergency actors in dealing with the spatio-temporal data required for emergency navigation, based on their roles in the disaster response. One of the key components of the system is the path planning module, which combines the modified A* algorithm, insertion heuristics, and auction algorithm to calculate obstacle-avoiding routes for multiple responders with multiple destinations. A spatial data model is designed to support the storage of information about the tasks and routes produced during the disaster response. Our system has been validated using four navigation cases. Some preliminary results are presented in this paper and show the potential of the system for solving more navigation cases.     

Cooperative neural field for the path planning of a robot arm

This paper deals with the problem of finding a good trajectory, from an initial position to a prescribed target point, for the end effector of a robot arm moving on a two-dimensional work field and avoiding obstacles lying on the work field. Two algorithms based on cooperative neural fields are proposed: the former is suited for the case where the location of obstacles is known, the latter doesn't require any a priori knowledge and is based on a very crude collision detector.     

未知环境下移动机器人实时路径规划

针对在未知环境下实现移动机器人实时的路径规划问题,提出了一种将快速扩展随机树（RRT）算法与视野域自适应的滚动窗口相结合的路径规划算法。该方法实时获取滚动窗口内的局部环境信息,根据环境的变化,滚动窗口视野域进行自适应调整,通过分析滚动窗口内传感器获取的信息,结合改进后的RRT算法筛选出可行的路径,控制移动机器人到达子目标点,在此过程中动态监测规划好的路径,确保路径合理,并重复上述过程,直至到达目标区域。实验对比分析表明,该方法能实时并有效实现未知环境下移动机器人的路径规划。     

完全未知环境下机器人路径规划的RPC算法

提出一种适合存在多种类型障碍的完全未知环境的机器人路径规划方法：RPC算法（全称为实时规划－选择算法）。即将BUG规划算法与所提出的两级路径选择策略相结合实时决策机器人的行为。其中两级路径选择策略模拟人类探索路径的思维模式实时决定机器人绕行障碍的方向,能够减小机器人避障中的盲目性和降低路径冗余度。此算法提高了机器人在未知环境中的智能性,增强了对较复杂环境的适应性。仿真实验表明了该算法的可行性。     

未知环境下单目视觉移动机器人路径规划

针对室内未知环境下的避障和局部路径规划,提出了一种单目移动机器人路径规划算法,该算法通过对环境图像的自适应阈值分割,获取障碍物与地面交线轮廓点集。通过对现有几种单目测距方法的分析比较,提出一种改进的空间几何约束单目视觉测距计算方法,并依据单目测距的几何关系建立了图像坐标系与机器人坐标系的映射,绘建了一定比例的局部地图。在局部地图上通过改进的人工势场算法为机器人规划路径,改进的人工势场算法解决了传统算法目标点不可到达的问题。通过MATLAB进行仿真实验,结果表明该方法可以规划出有效合理的路径。     

动态未知环境下一种新的机器人路径规划方法

针对机器人动态路径规划问题,提出了一种机器人在复杂动态环境中实时路径规划方法.该方法基于滚动窗口的路径规划和避障策略,通过设定可视点子目标、绕行障碍物和对动态障碍物的分析预测,实现机器人在复杂动态环境下的路径规划.针对障碍物分布情况,合理设计可视点法和绕行算法之间转换,有效地解决了局部路径规划的死循环与极小值问题.该方...     

未知环境下机器人路径规划的粒子群优化

研究了全局静态环境未知时机器人的路径规划问题,提出了一种新颖的基于粒子群算法的滚动规划算法。该方法在机器人视野域内产生若干个同心圆进行环境建模,然后利用粒子群优化算法规划出一条导航路径,机器人每前进一步,都由粒子群优化算法重新规划导航路径,因此,机器人前进路径不断动态修改,从而能使机器人沿一条全局优化的路径接近终点。仿真实验结果表明,即使在障碍物非常复杂的地理环境,用该算法也能迅速规划出一条优化路径,且能安全避碰,效果令人满意。     

面向自动驾驶的动态路径规划避障算法

针对自动驾驶中避障的动态路径规划问题,提出一种在已知车辆的初始位置、速度、方向和障碍物位置情况下,实时避开障碍物的动态规划算法。首先,利用三次样条曲线的二阶连续性,结合已知的车道信息产生道路基准线;其次,以车辆的位置方向和道路的曲率构建s-q坐标系,并在s-q坐标系内产生从车辆当前位置到目的位置的一簇平滑曲线,作为候选路径;最后,综合考虑车辆行驶的安全性、平滑性和连贯性准则,设计一种新的代价函数,并且通过使代价函数最小化的方法从候选路径中选择最佳路径。在实验过程中,通过设计多种不同的模拟道路来检验算法的性能。实验结果表明,该方法在多种地形的单车道和多车道道路上都能够规划出安全、平滑的路径,有效避开障碍物,并且具有较好的实时性。     

Polynomial joint angle arm robot motion planning in complex geometrical obstacles

This paper addresses a point-to-point of an arm robot motion planning in complex geometrical obstacle. It will govern a two-layer optimization strategy utilizing sixth degree polynomial as joint angle path. At the beginning of the motion planning process, the path planning starts with the optimization objective to minimize the joint angle travelling distance under collision detection rules as constraint. After the best path has been met, the associated time will be searched with the optimization objective to minimize the total travelling time and the torque under the maximum velocity, the maximum acceleration, the maximum jerk, and the maximum torque constraints. The performance of a Genetic Algorithm (GA) and a Particle Swarm Optimization (PSO) will be investigated in searching the feasible sixth degree polynomial joint angle path and the total travelling time that gives the optimal trajectories under kinodynamic constraints. A 3-Degree-Of-Freedom (3-DOF) planar robot will be utilized to simulate the proposed scenario.     

Time-minimal paths amidst moving obstacles in three dimensions

A path-planning problem is considered in the presence of moving polygonal obstacles in three dimensions. A particle is to be moved from a given initial position to a destination position amidst polygonal disjoint barriers moving along known linear trajectories. The particle can move in any direction in space with a single constraint that it cannot move faster than a given speed bound. All obstacles are slowly moving, i.e., their speeds are strictly slower than the maximum speed of the particle. The destination point is also permitted to move along a known trajectory and is assumed to be collision-free at all times. Three properties are stated and proved for a time-minimal path amidst moving polygonal barriers. A few extensions are considered, including piecewise linear motions of the obstacles.     

An augmented Voronoi roadmap for 3D translational motion planning for a convex polyhedron moving amidst convex polyhedral obstacles

This paper concerns the development of a piecewise linear Voronoi roadmap for translating a convex polyhedron in a three-dimensional (3-D) polyhedral world. In general the Voronoi roadmap is incomplete for motion planning, i.e., it can have several disjoint components in one connected component of free space. An analysis of the roadmap shows that incompleteness is caused by the occurrence of the following simple geometric structure: a polygon in the Voronoi surface containing one or more polygons inside it. We formally bring out the details of this geometric structure and give an efficient augmentation of the roadmap that makes it complete. We show that the roadmap has size e = O(n²Q²l²), where n is the total number of faces on the obstacles, Q is the total number of obstacles and l is the number of faces on the moving object. We also present an algorithm to construct the roadmap in O((n + Ql)e + Q²log Q) time.     

基于改进PRM的采摘机器人机械臂避障路径规划

针对采摘机器人机械臂在不确定的环境中进行采摘作业的要求,提出了一种基于改进概率地图(PRM)算法的机械臂避障路径规划方法。将机械臂工作空间分割成离散单元集合,通过遍历的方法,获得机械臂工作空间中任意离散单元与机械臂有撞位姿之间的映射关系。将空间障碍物分割成离散单元,并通过索引映射关系获得与障碍物有撞的所有机械臂位姿信息,并以此建立关节构形空间。通过PRM算法在关节构形空间中快速搜索机械臂避障路径。仿真结果表明:相比传统PRM算法,改进算法速度提高22. 2%,能够有效地实现机械臂无碰撞路径规划。