RT Journal Article
T1 DEM-AIA: Asymmetric inclination-aware trajectory planner for off-road vehicles with digital elevation models
A1 Toscano-Moreno, Manuel
A1 Mandow, Anthony
A1 Martínez-Sánchez, María Alcázar
A1 García-Cerezo, Alfonso José
K1 Vehículos autodirigidos
K1 Aprendizaje automático (Inteligencia artificial)
AB 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.
PB Elsevier
YR 2023
FD 2023
LK https://hdl.handle.net/10630/26534
UL https://hdl.handle.net/10630/26534
LA eng
NO Toscano-Moreno, Mandow, A., Martínez, M. A., & García-Cerezo, A. (2023). DEM-AIA: Asymmetric inclination-aware trajectory planner for off-road vehicles with digital elevation models. Engineering Applications of Artificial Intelligence, 121. https://doi.org/10.1016/j.engappai.2023.105976
NO This work has been partially funded by the Spanish projects RTI2018-093421-B-I00 and PID2021-122944OB-I00. The first authoris partially supported by predoctoral grant BES-2016-077022 (Spanish Ministry of Science and Innovation, co-financed by the European Social Fund). Funding for open access charge: Universidad de Málaga / CBUA.The authors are grateful to ATyges Ingeniería (Málaga, Spain) for providing aerial photogrammetry. Finally, we acknowledge the support from our colleagues of the UMA Robotics and Mechatronics Group.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 23 ene 2026