Heterogeneous Vehicle Motion Planning Considering Multiple Differentiated Characteristic Constraints

Revealing differences in vehicle characteristics is critical to enhancing the accessibility of heterogeneous vehicles in off-road environments. Decomposing complex motions into primitives facilitates the maintenance of the algorithm's solution efficiency while considering various constraints. Therefore, this paper proposes a heterogeneous vehicle motion planning method for off-road scenarios based on the generation, extension, and selection of driving behavior primitives. Based on the library of heterogeneous vehicle driving behavior primitives (HDBPs) extracted from driving data in our previous study, this paper proposes a primitive offline optimization generation method that integrates driving behavior constraints, vehicle kinematics constraints, reserved power constraints, and ground adhesion constraints. The generation of spatiotemporal coupled planning results is accomplished by HDBP extension and selection using the optimized HDBP library as the source. In particular, the extension and selection cost considers the interaction with the ground under the constraints of the suspension system, as well as the capacity of the drive system. This paper demonstrates that the proposed HDBP-based planning method can generate highly adaptable and differentiated primitive sequences based on diverse terrain conditions and heterogeneous vehicle characteristic constraints. Moreover, benefiting from the suspension-based pose estimation and drive system characteristic limitations, the method proposed in this paper has a significant advantage over the comparison methods in terms of terrain traversability in real-scene motion planning experiments.