An energy-efficient hierarchical algorithm of dynamic obstacle avoidance for unmanned surface vehicle
Jiaqi Wang, Shixin Li, Boyang Li, Chenyu Zhao, Ying Cui
Abstract
Most of the existing studies developed and improved local path planning algorithms independently of global planning, i.e., ignoring the global optimal constrains. To meet the requirements of practical applications, this paper presented an energy-efficient hierarchical collision avoidance algorithm for unmanned surface vehicle operating in clustered dynamic environments. For the global level, genetic algorithm was modified by strategies of greedy-inspired population initialization, penalty-based multi-objective fitness function, and joint crossover. For the local level, velocity obstacle was combined with dynamic window approach to provide the kinematic constraints of the vehicle to its admissible velocities and simplified collision avoidance rules to guide the evasive maneuvers. Simulations showed that the proposed global algorithm was superior to three other algorithms in terms of path length, path smoothness, and convergence speed regardless of the environment size. The performance of the local algorithm was also verified for various encounter scenarios and speed ratios. In addition, the combination of the global and local planning can effectively solve the path optimization and dynamic obstacle avoidance in a designed offshore environment of fish cage culture.