Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

• Published in: Industrial robot 2017-01, Vol.44 (1), p.64-74
• Main Authors: Wu, Peng, Xie, Shaorong, Liu, Hengli, Li, Ming, Li, Hengyu, Peng, Yan, Li, Xiaomao, Luo, Jun
• Format: Article
• Language: English
• Subjects: Algorithms; Heuristic; Lasers; Pheromones; Robots; Trails; Unmanned aerial vehicles
• ISSN: 0143-991X; 1758-5791
• DOI: 10.1108/IR-04-2016-0127

Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.