基于改进rrt *算法的自主移动机器人路径规划研究与设计

IF 0.6 Q4 ENGINEERING, MECHANICAL
THAI-VIET DANG
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引用次数: 0

摘要

路径规划被视为一个多目标优化问题(MOOP),即最短路径和到无碰撞障碍物的最小距离。移动机器人的位置是基于使用SLAM构建地图或从相机帧提取数据得出的数据确定的。由于环境的复杂性,避障仍然需要复杂的传感器系统,对处理效率有很高的要求。研究提供了一种改进的RRT*算法,用于给定环境下的移动机器人路径规划。RRT*将根据搜索环境中节点的最优代价函数来优化网格节点的数量。改进的RRT*算法以一定的安全代价提高了避障效率。因此,在传统RRT算法的基础上,改进的RRT*算法可以去除冗余路径节点。此外,三次b样条曲线在保证MOOPs的同时使路径平滑。最后,通过仿真和实验验证了所提策略的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RESEARCH AND DESIGN OF A PATH PLANNING USING AN IMPROVED RRT* ALGORITHM FOR AN AUTONOMOUS MOBILE ROBOT
Path planning is innately viewed as a multi-objective optimization problem (MOOP) of the shortest path and the smallest distance to collision-free obstacles. The position of the mobile robot is definitively identified based on data derived from constructing maps with SLAM or extracting data from camera frames. Due to the complexity of the surroundings, obstacle avoidance still requires a complex sensor system with a high processing efficiency demand. The study provides an improved RRT* algorithm for mobile robot path planning in a given environment. RRT* will optimize the number of grid nodes based on the optimal cost functions of nodes in the searching environment. The improved RRT* algorithm with a safety cost increases the efficacy of obstacle avoidance. Hence, an improved RRT* algorithm based on the traditional RRT algorithm will remove redundant path nodes. Furthermore, the third-degree B-spline curve will smooth the path while ensuring MOOPs. Last but not least, simulations and experiments are shown to demonstrate the effectiveness of the suggested strategy.
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来源期刊
MM Science Journal
MM Science Journal ENGINEERING, MECHANICAL-
CiteScore
1.30
自引率
42.90%
发文量
96
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