A TD-RRT* Based Real-Time Path Planning of a Nonholonomic Mobile Robot and Path Smoothening Technique Using Catmull-Rom Interpolation

Jyotish, Mei-Yung Chen
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引用次数: 1

Abstract

It is inevitable for a mobile robot to competently plan an optimal path from its starting, or current, location to a desired goal location. This is an insignificant task when the environment is unvarying. However, the practicable environment for the robot is hardly static, and it often has many moving obstacles. The robot may encounter one, or many, of these unknown and unforeseeable dynamic obstacles. The robot will now opt to proceed, when one of these obstacles is obstructing its path. The objective of this paper is to find a reasonable relation between parameters used in the path planning algorithm in a platform which a robot will be able to move from the start point in a dynamic environment with map and plan an optimal path to specified goal without any collision with moving and static obstacles. For this purpose, an asymptotically optimal version of Rapidly-exploring Random Tree (RRT algorithm), named RRT* is used. The algorithm is based on an incremental sampling which covers the whole space and acts fast. Moreover, this algorithm is computationally efficient, therefore it can be used in multidimensional environments.A method of dynamic replanning using TD-RRT* is presented. The robot will rectify or modify its path when unknown random moving or static snag obstructs the path. Various experimental results show the effectiveness of the proposed method which is faster than the basic RRT*, and the smooth path with the shortest distance can be obtained which satisfies the nonholonomic constraint of mobile robots.
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基于TD-RRT*的非完整移动机器人实时路径规划及Catmull-Rom插值路径平滑技术
对于移动机器人来说,从其起始位置或当前位置到期望目标位置的最优路径是不可避免的。当环境不变时,这是一个微不足道的任务。然而,机器人的实际环境并不是静态的,它经常有许多移动的障碍物。机器人可能会遇到这些未知和不可预见的动态障碍中的一个或多个。现在,当其中一个障碍物挡住了机器人的去路时,机器人会选择继续前进。本文的目标是在一个平台中找到路径规划算法中使用的参数之间的合理关系,使机器人能够在具有地图的动态环境中从起点移动,并规划出到达指定目标的最优路径,而不会与移动和静态障碍物发生碰撞。为此,使用了快速探索随机树(RRT算法)的渐近最优版本,称为RRT*。该算法基于增量采样,覆盖整个空间,速度快。此外,该算法计算效率高,可用于多维环境。提出了一种基于TD-RRT*的动态重规划方法。当未知的随机移动或静态障碍物阻碍其路径时,机器人将纠正或修改其路径。各种实验结果表明,该方法比基本的RRT*算法更快,能够得到距离最短的满足移动机器人非完整约束的光滑路径。
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