A Novel Knowledge-Based Genetic Algorithm for Robot Path Planning in Complex Environments
This addresses path planning for mobile robots in unstructured environments, representing an incremental improvement through domain-specific operator integration.
The paper tackled robot path planning in complex environments by proposing a knowledge-based genetic algorithm with specialized operators, achieving near-optimal paths in static and dynamic settings as demonstrated through simulation studies.
In this paper, a novel knowledge-based genetic algorithm for path planning of a mobile robot in unstructured complex environments is proposed, where five problem-specific operators are developed for efficient robot path planning. The proposed genetic algorithm incorporates the domain knowledge of robot path planning into its specialized operators, some of which also combine a local search technique. A unique and simple representation of the robot path is proposed and a simple but effective path evaluation method is developed, where the collisions can be accurately detected and the quality of a robot path is well reflected. The proposed algorithm is capable of finding a near-optimal robot path in both static and dynamic complex environments. The effectiveness and efficiency of the proposed algorithm are demonstrated by simulation studies. The irreplaceable role of the specialized genetic operators in the proposed genetic algorithm for solving the robot path planning problem is demonstrated through a comparison study.