A Multilevel Adaptive Path-Planning Model in Off-Road Environments

Abstract

Most existing path-planning algorithms are applied in either trafficable environments or non-trafficable environments. Off-road vehicles (ORVs) are often faced with a mix of trafficable and non-trafficable environments. Therefore, trafficability should be considered in path planning for ORVs. Conventional ant colony algorithms (ACAs) are prone to stagnation and often fail to reach the optimal path. To address these problems, an improved ACA that considers trafficability was proposed in this study, which improved the pheromone distribution rules and adaptively adjusted the pheromone volatility coefficient. Based on this improved ACA, a multilevel adaptive path-planning model was proposed to solve path-planning problems with various scales of area. Experiments and comparative studies revealed that the improved ACA was applicable to path-planning problems in complex environments and achieved better performance and a higher computing efficiency than conventional counterparts.