Obstacle Avoidance Motion in Mobile Robotics

doi:10.16182/j.issn1004731x.joss.23-1297E

Abstract

Abstract:

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.

Key words: mobile robot, obstacle avoidance motion, global path planning, local obstacle avoidance

CLC Number:

TP242.6

Tang Yunchao, Qi Shaojun, Zhu Lixue, Zhuo Xianrong, Zhang Yunqi, Meng Fan. Obstacle Avoidance Motion in Mobile Robotics[J]. Journal of System Simulation, 2024, 36(1): 1-26.

Figures/Tables 12

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Table 1

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