High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2026
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| author | Baxter, James R. Epureanu, Bogdan I. Jayakumar, Paramsothy Ersal, Tulga |
| author_facet | Baxter, James R. Epureanu, Bogdan I. Jayakumar, Paramsothy Ersal, Tulga |
| contents | A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off-road vehicles on rough, non-planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy-based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state-of-the-art methods, and real-time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state-of-the-art baseline across several challenging scenarios that push the vehicle to its mobility limits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_20525 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility Baxter, James R. Epureanu, Bogdan I. Jayakumar, Paramsothy Ersal, Tulga Robotics Systems and Control A novel local trajectory planner, capable of controlling an autonomous off-road vehicle on rugged terrain at high-speed is presented. Autonomous vehicles are currently unable to safely operate off-road at high-speed, as current approaches either fail to predict and mitigate rollovers induced by rough terrain or are not real-time feasible. To address this challenge, a novel model predictive control (MPC) formulation is developed for local trajectory planning. A new dynamics model for off-road vehicles on rough, non-planar terrain is derived and used for prediction. Extreme mobility, including tire liftoff without rollover, is safely enabled through a new energy-based constraint. The formulation is analytically shown to mitigate rollover types ignored by many state-of-the-art methods, and real-time feasibility is achieved through parallelized GPGPU computation. The planner's ability to provide safe, extreme trajectories is studied through both simulated trials and full-scale physical experiments. The results demonstrate fewer rollovers and more successes compared to a state-of-the-art baseline across several challenging scenarios that push the vehicle to its mobility limits. |
| title | High-Speed, All-Terrain Autonomy: Ensuring Safety at the Limits of Mobility |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2603.20525 |