Local Path Planning with Multiple Constraints for USV Based on Improved Bacterial Foraging Optimization Algorithm

The quality of unmanned surface vehicle (USV) local path planning directly affects its safety and autonomy performance. The USV local path planning might easily be trapped into local optima. The swarm intelligence optimization algorithm is a novel and effective method to solve the path-planning problem. Aiming to address this problem, a hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed. The proposed algorithm preserves a three-layer nested structure, and a simulated annealing mechanism is incorporated into the outermost nested dispersal operator. The proposed algorithm can effectively escape the local optima. Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) rules and dynamic obstacles are considered as the constraints for the proposed algorithm to design different obstacle avoidance strategies for USVs. The coastal port is selected as the working environment of the USV in the visual test platform. The experimental results show the USV can successfully avoid the various obstacles in the coastal port, and efficiently plan collision-free paths.

Automated summary

The quality of local path planning for unmanned surface vehicles (USVs) is crucial for their safety and autonomy. A hybrid bacterial foraging optimization algorithm with a simulated annealing mechanism is proposed to address the problem of being trapped in local optima. The algorithm successfully avoids local optima and effectively plans collision-free paths for USVs in a coastal port environment.