Reliable Global Path Planning of Off-Road Autonomous Ground Vehicles Under Uncertain Terrain Conditions

• Published in: IEEE transactions on intelligent vehicles Vol. 9; no. 1; pp. 1161 - 1174
• Main Authors: Yin, Jianhua, Li, Lingxi, Mourelatos, Zissimos P., Liu, Yixuan, Gorsich, David, Singh, Amandeep, Tau, Seth, Hu, Zhen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acceleration; Algorithms; Autonomous vehicles; Computational modeling; Failure modes; Formability; Mission mobility reliability; Mission planning; Path planning; Predictive models; Reliability; Roads & highways; Rough terrain; RRT; Shortest-path problems; Surface roughness; Surface soil; surrogate modeling; Travel time; Uncertainty; Unmanned ground vehicles; Vehicles
• ISSN: 2379-8858; 2379-8904
• DOI: 10.1109/TIV.2023.3317833

Path planning plays a vital role in ensuring the efficient and safe operation of off-road autonomous ground vehicles (AGVs). Current methods mostly focus on minimizing the travel time or path length of AGVs and often overlook the fact that the AGVs could fail in many ways during the operation due to stochastic and rough terrain conditions. The objective of this article is to not only generate a proper path for the vehicle but also ensure that the planned path is overall reliable and safe for the vehicle with complex terrain conditions. To achieve this goal, this article develops a reliability-based mission planning method for off-road AGVs subject to two failure modes in terms of mobility (the maximum attainable speed and vehicle vertical acceleration) induced by the uncertain ground properties of the terrain. A physics-based vehicle dynamics simulation model is first employed to predict vehicle mobility for any given terrain conditions of a path. Mobility reliability of an AGV is then analyzed using surrogate modeling methods considering uncertainty sources in the off-road terrain conditions. After that, the reliability constraints for the two failure modes are integrated with the Rapidly-exploring Random Tree Star (RRT*) algorithm to identify an optimal path, which is the shortest path while satisfying the reliability requirements of the two considered failure modes. Results of a case study demonstrated the effectiveness of the proposed methods for path planning with the consideration of uncertainty in the deformable terrain.