Multi-objective multi-robot path planning in continuous environment using an enhanced genetic algorithm

Abstract

Abstract This paper presents a hybrid approach for path planning of multiple mobile robots in continuous environments. For this purpose, first, an innovative Artificial Potential Field (APF) algorithm is presented to find all feasible paths between the start and destination locations in a discrete gridded environment. Next, an enhanced Genetic Algorithm (EGA) is developed to improve the initial paths in continuous space and find the optimal path between start and destination locations. The proposed APF works based on a time-efficient deterministic scheme to find a set of feasible initial paths and is guaranteed to find a feasible path if one exists. The EGA utilizes five customized crossover and mutation operators to improve the initial paths. In this paper, path length, smoothness, and safety are combined to form a multi-objective path planning problem. In addition, the proposed method is extended to deal with multiple mobile robot path planning problem. For this purpose, a new term is added to the objective function which measures the distance between robots and a collision removal operator is added to the EGA to remove possible collision between paths. To assess the efficiency of the proposed algorithm, 12 planar environments with different sizes and complexities were examined. Evaluations showed that the control parameters of the proposed algorithm do not affect the performance of the EGA considerably. Moreover, a comparative study has been made between the proposed algorithm, A*, PRM, B-RRT and Particle Swarm Optimization (PSO). The comparative study showed that the proposed algorithm outperforms PSO as well as well-recognized deterministic (A*) and probabilistic (PRM and B-RRT) path planning algorithms in terms of path length, run time, and success rate. Finally, simulations proved the efficiency of the proposed algorithm for a four-robot path planning problem. In this case, not only the proposed algorithm determined collision-free paths, but also it found near optimal solution for all robots.