A New Algorithm for Path Planning in Dynamic Environments based on Modified Artificial Potential Field Model and Modified Ant-Q Algorithm
Jinghao Zhang · 2024
Artificial potential field and Ant-Q are effective methods for path planning problems. In this paper, several improvements are made for these two methods. First, a new model of repulsive field is proposed to improve the safety of paths. Second, the risk on a path is defined and quantified as a measurement of path quality to further ensure security. Finally, a new algorithm is designed based on these two modified methods. It extends the application field of the ant-Q algorithm to dynamic path planning and meanwhile overcomes the disadvantages of the artificial potential field method. Experiments show that the new algorithm achieved up to an 86% reduction in computational time and a 5.5% reduction in time costs of paths while maintaining safety in environments with dynamic obstacles and targets.