A hybrid artificial bee colony algorithm with genetic augmented exploration mechanism toward safe and smooth path planning for mobile robot

Abstract

Path planning is important for mobile robot to ensure safe and efficient navigation. This paper proposes a hybrid artificial bee colony with genetic augmented exploration mechanism (HABC-GA) that enables mobile robot to achieve safe and smooth path planning. Considering the characteristics of path planning problem, a mathematical model is constructed to balance three objectives: path length, path safety, and path smoothness. In the employed bee phase, a genetic augmented exploration mechanism is designed, which encompasses redesigned path crossover, adaptive obstacle-aware mutation, and dynamic elite selection operators. In the onlooker bee phase, an objective-guided optimization strategy is investigated to improve local search ability. In the scout bee phase, a dual exploration restart strategy is developed to increase the activity of individuals in the population, in which stagnant individuals in the evolution are replaced by more promising ones. Finally, the proposed HABC-GA is compared with five efficient algorithms in 24 instances of six representative environments. Simulation results demonstrate the effectiveness and high performance of HABC-GA in obtaining safe and smooth paths.