Dynamic path planning of mobile robots by combining improved informed-RRT* and VFH+ algorithms

Addressing the issues of long planning time, low iteration efficiency, and inability to be applied in dynamic scenarios of the Informed-RRT* algorithm, a dynamic path planning method is proposed by integrating the improved Informed- RRT* algorithm with the VFH+ algorithm. Firstly, the Informed-RRT* algorithm is optimized by adopting bidirectional expansion of target node biasing and adaptive step size strategy, enhancing its capability in global path planning. The VFH+ algorithm with dynamic threshold optimization is proposed for local path planning, effectively improving the algorithm's obstacle avoidance capability and path smoothness in local environments. By determining the optimal passage direction, dynamic threshold optimization, and setting local sub-target points. Finally, the integration of the improved Informed-RRT* algorithm and VFH+ algorithm achieves dynamic path planning based on local sub-target points. Simulation results demonstrate that the improved Informed-RRT* algorithm reduces the average path length by 9.4% and 5.8% compared to RRT* and standard Informed-RRT*, respectively, with an average increase in planning time of 35.2% and 23.5%. The integrated algorithm exhibits good dynamic path planning capabilities in complex environments, balancing global optimality with safe obstacle avoidance, and facilitating stable and efficient robot operation.