An Optimization-Based Path Planning Method for Unmanned Systems in Multi-obstacle Environments

To address the global path planning problem for unmanned systems in environments with multiple obstacles, an Enhanced Quantum Particle Swarm Optimization (EQPSO) algorithm is proposed. With this algorithm, globally optimal paths will be planned with a novel adaptive law designed to achieve adaptive coefficient contraction and expansion. Moreover, the length, curvature, and obstacle avoidance costs are simultaneously considered during the path updating process to ensure that safe routes are designed. The simulation conclusively illustrates the effectiveness of the algorithm in solving the global path planning problems.