A novel navigation system for an autonomous mobile robot in an uncertain environment

• Published in: Robotica Vol. 40; no. 3; pp. 421 - 446
• Main Authors: Chen, Meng-Yuan, Wu, Yong-Jian, He, Hongmei
• Format: Journal Article
• Language: English
• Published: Cambridge, UK Cambridge University Press 01.03.2022
• Subjects: Adaptive algorithms; Algorithms; Autonomous navigation; Clustering; Decision trees; Modular design; Navigation systems; Obstacle avoidance; Particle swarm optimization; Robots; Shortest-path problems; adaptive threshold clustering; obstacle recognition; sliding window; Morphin path planning; collision prediction
• ISSN: 0263-5747; 1469-8668
• DOI: 10.1017/S0263574721000497

In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.