移动机器人避障运动研究

Obstacle Avoidance Motion in Mobile Robotics

随着人工智能技术的飞速进展,移动机器人在工业、航天及农业等领域的作用逐渐凸显,其自主避障能力直接关系到在不同环境中作业的安全性与效率,而路径规划作为避障的核心技术之一,在决定避障性能方面起着至关重要的作用。对移动机器人路径规划技术进行了综述。基于作业需求将算法分为全局规划和局部避障两类。详述了以采样、图搜索和仿生学为基础的全局规划方法,分析了其收敛速度、内存需求及计算效率,并探讨了其改进策略。对局部避障算法进行了探讨,概述了其原理与特点,并明确其最佳应用场景。对当前的自主避障技术进行了总结,强调了传统算法的智能化程度仍需提升,以及集成不同的算法以提高规划性能将是未来的发展大势。

The advancement of artificial intelligence technology has significantly enhanced the utilization of mobile robots in various fields such as industry,aerospace,and agriculture.The autonomous obstacle avoidance capability of these robots is crucial to the safety and efficiency of their operations in diverse settings.Path planning,a key technology in obstacle avoidance,plays an essential role in the overall performance of these systems.This paper presents a comprehensive review of path planning technology for mobile robots,categorizing the algorithms into global planning and local obstacle avoidance according to their operational requirements.Specific focus is given to the global planning methods involving sampling,graph search,and biomimetics,assessing their convergence rate,memory demands,and computational efficiency,along with strategies for improvement.The paper then explores local obstacle avoidance algorithms,explicating their foundational principles,characteristics,and ideal use cases.In conclusion,the paper synthesizes the state-of-the-art in autonomous obstacle avoidance,noting that the strategic integration of various algorithms to refine planning performance,and the enhancement of traditional algorithms'intelligence is projected to be a leading trend in future research.