DEM-AIA: Asymmetric inclination-aware trajectory planner for off-road vehicles with digital elevation models

• Published in: Engineering applications of artificial intelligence Vol. 121; p. 105976
• Main Authors: Toscano-Moreno, Manuel, Mandow, Anthony, Martínez, María Alcázar, García-Cerezo, Alfonso
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.05.2023
• Subjects: Digital elevation model; Inclination awareness; Path planning; Unmanned ground vehicle; Unmanned ground vehicle; Digital elevation model; Path planning; Inclination awareness
• ISSN: 0952-1976; 1873-6769
• DOI: 10.1016/j.engappai.2023.105976

Planning safe and effective trajectories for off-road unmanned ground vehicles (UGV) is a critical Artificial Intelligence (AI) challenge that can benefit from recent advances in digital elevation models (DEM) for readily capturing accurate terrain geometry. Considering path slopes is crucial to preserve stability and assess terrain traversability at feasible speeds to optimize travel time, which is highly dependent on direction (i.e., pitch and roll). In this article, we propose a new DEM-based asymmetric inclination-aware (DEM-AIA) trajectory planner for ground vehicles. The planner is an any-angle variant of the A⋆ algorithm that computes pitch and roll estimations for each segment crossing cell triangles in the line-of-sight. Furthermore, we define a non-linear velocity constraints function that integrates information about tip-over safety limitations, maximum uphill and downhill slopes for the vehicle, and asymmetric modulation of nominal flat-ground velocity for all pitch and roll combinations. The planner produces a time sub-optimal trajectory with feasible speed references for each segment crossing a cell triangle. Moreover, we provide an extensive experimental analysis of inclination-aware performance on simulated and real-world DEMs as well as a comparison with state-of-the-art path planners adapted to travel-time optimization. An executable version of the planner with parameterizable variations is publicly available.