A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments
提出一种融合领域知识的遗传算法，通过五个专用算子和局部搜索，在静态和动态环境中生成无碰撞的机器人路径，仿真和实验验证了其有效性。
This article presents a novel knowledge-based genetic algorithm (GA) to generate a collision-free path in complex environments. The proposed algorithm infuses specific domain knowledge into robot path planning through the development of five problem-specific operators that integrate a local search technique to improve efficiency. In addition, the proposed algorithm introduces a unique and straightforward representation of the robot path and an effective method for evaluating the path quality and accurately detecting collisions. The proposed algorithm is capable of finding optimal or suboptimal robot paths in both static and dynamic environments. Simulation and experimental studies are conducted to showcase the effectiveness and efficiency of the proposed algorithm. Furthermore, a comparative study is performed to highlight the indispensable role of specialized genetic operators within the proposed algorithm in solving the path planning problem.