A New Robot Navigation Algorithm Based on a Double-Layer Ant Algorithm and Trajectory Optimization

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 11; pp. 8557 - 8566
• Main Authors: Yang, Hui, Qi, Jie, Miao, Yongchun, Sun, Haixin, Li, Jianghui
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Ant colony optimization; Autonomous navigation; Collision avoidance; Computer simulation; Double-layer ant colony optimization (DL-ACO); Heuristic algorithms; Indoor environments; Mobile robots; Optimization algorithms; path planning; piecewise B-spline curve; Robot control; Robots; Smoothness; Splines (mathematics); Tracking control; Trajectory optimization; Trajectory planning
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2018.2886798

This paper presents an efficient double-layer ant colony optimization algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms that run independently and successively. First, a parallel elite ant colony optimization method is proposed to generate an initial collision-free path in a complex map, and then, we apply a path improvement algorithm called turning point optimization algorithm, in which the initial path is optimized in terms of length, smoothness, and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments.