基于拓扑路网的多挡无人履带平台路径重规划

To solve the problem of poor robustness and weak scene adaptability of replanning for unmanned tracked hybrid platforms in the off-road environment, a method that incorporates a multi-objective function model and uses a three-order spiral of multi-stage sampling optimization to reconstruct the planned path is proposed. This method regenerates a reference trajectory that can reach the target when no local path is found in dynamic driving. It focuses on solving the cost optimization problem during the back-off road driving of a unmanned tracked platform. The platform is also equipped with a two-speed planetary automatic mechanical transmission. The unmanned platform can switch to different gears according to the driving speed during the traveling process to meet the speed requirements under different road conditions, and thus it has high adaptability. The real vehicle platform verifies that the method proposed in this study can be used for different off-road scenarios. According to the objective cost function model of the three parameters of travel time, energy consumption, and number of shifts, the best replanning strategy can be obtained, as well as an optimal and passable route. The results show that the path replanning method reduces the time and energy costs of the planning process. The appropriate gear is selected while both time and energy, different turning radiuses and speeds are considered. Thus, the longitudinal movement performance, effectiveness, and economy of the platform are guaranteed.