Multi-objective mobile robot path planning problem through learnable evolution model

A new multi-objective non-Darwinian-type evolutionary computation approach based on learnable evolution model (LEM) is proposed for solving the robot path planning problem. The multi-objective property of this approach is governed by a robust strength Pareto evolutionary algorithm (SPEA) incorporated in the LEM algorithm presented here. Learnable evolution model includes a machine learning method, like the decision trees, that can detect the right directions of the evolution and leads to large improvements in the fitness of the individuals. Several new refiner operators are proposed to improve the objectives of the individuals in the evolutionary process. These objectives are: the path length, the path safety and the path smoothness. A modified integer coding path representation scheme is proposed where the edge-fixing and top-row fixing procedures are performed implicitly. This proposed robot path planning problem solving approach is assessed on eight realistic scenarios in order to verify the performance thereof. Computer simulations reveal that this proposed approach exhibits much higher hypervolume and set coverage in comparison with other similar approaches. The experimental results confirm that the proposed approach performs in the workspaces with a dense set of obstacles in a significant manner.