A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption

Global path planning in complex off-road environments is one of the key technologies for realizing the autonomous driving of unmanned ground vehicles (UGVs). However, there is limited research on energy-efficient path planning for tracked vehicles in the industry, and currently commonly used path planning algorithms are unable to balance both the solution quality and the computational efficiency, making them impractical for real-time energy-optimal global path planning of tracked vehicles. To address this issue, this paper proposes an optimal energy-fast probabilistic roadmap algorithm (OPRA) for tracked vehicles, An off-road energy consumption cost model considering the characteristics of tracked vehicles is established to quantify their energy consumption under off-road conditions, The sampling method of the probabilistic roadmap algorithm in off-road environments is directionally improved by creating specific vectors, enhancing the running speed of the algorithm while reducing the energy consumption of tracked vehicles. Meanwhile, a method for increasing the density of path node ia used to prevent the interference between paths and the environment. Compared with traditional algorithms, the proposed algorithm can reduce the energy consumption and the planning time by up to 32% and 89. 2%, respectively, in off-road environments, achieving the comprehensive optimization of both travel energy consumption and algorithm runtime for global path planning of tracked vehicles under off-road conditions.