Terrain Adaptive Autonomous Vehicles for Uncertain Off-Road Environments

Project began August 2020 and completed in Q3 2023.

Four considerations about military autonomous ground vehicles motivate this proposal: (1) Military vehicles are often required to operate on off-road deformable terrains where the vehicle’s mobility is highly dependent on the highly nonlinear tire forces generated at the tire-terrain interface. (2) Terramechanics models are often parameterized by many terrain properties that are unknown or varying during vehicle operation. (3) Increasing the mobility of military autonomous ground vehicles is a critical need. (4) State-of-the-art approaches to navigate such vehicles typically rely on model dependent architectures, such as Model Predictive Control (MPC), and require efficient, dynamic, and twice continuously differentiable mathematical models

The project is based on the vision that autonomous vehicles that are aware of and adaptive to changes in the terrain conditions will offer increased safety and higher performance. Accordingly, the overarching goal of this work is to develop an off-road trajectory planning algorithm that is capable of learning terrain conditions online and modifying decisions accordingly. This constitutes basic research, because while the project focuses on the application of off-road autonomous navigation, the techniques developed can impact other applications where real time decision-making in autonomous systems are influenced by an unknown environment. Furthermore, the techniques developed can in general improve the robustness of MPC and be applied to other autonomous vehicles, for example commercial vehicles operating in on-road winter conditions. Towards this end, the following basic research questions will be answered.

• Can a dynamic, accurate, efficient, and twice continuously differentiable terramechanics model be achieved to enable trajectory planning in off-road environments through MPC?
• Can such terramechanics models be used in estimators to determine terrain properties with sufficient accuracy for mobility prediction purposes?
• To what extent can such terrain estimation improve safety and robustness of off-road autonomous vehicles on uncertain terrains?

Prior related publications:

1. J. Dallas, K. Jain, Z. Dong, M. P. Cole, P. Jayakumar, and T. Ersal, “Online terrain estimation for autonomous vehicles on deformable terrains,” Journal of Terramechanics, vol. 91, pp. 11-22, 2020.
2. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “Improving the Robustness of an MPC-Based Obstacle Avoidance Algorithm to Parametric Uncertainty Using Worst-Case Scenarios,” Vehicle System Dynamics, vol. 57, no. 6, pp. 874-913, 2019.
3. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “A nonlinear model predictive control formulation for obstacle avoidance in high-speed autonomous ground vehicles in unstructured environments,” Vehicle System Dynamics, vol. 56, no. 6, pp. 853-882, 2018.
4. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “A Double-Worst-Case Formulation for Improving the Robustness of an MPC-Based Obstacle Avoidance Algorithm to Parametric Uncertainty,” American Control Conference, Seattle, WA, 2017.
5. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “Combined Speed and Steering Control in High Speed Autonomous Ground Vehicles for Obstacle Avoidance Using Model Predictive Control,” IEEE Transactions on Vehicular Technology, vol. 66, no. 10, pp. 8746-8763, 2017.
6. H. Febbo, J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “Moving Obstacle Avoidance for Large, High-Speed Autonomous Ground Vehicles,” American Control Conference, 2017.
7. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “A study on model fidelity for Model Predictive Control based obstacle avoidance in high speed autonomous ground vehicles,” Vehicle System Dynamics, vol. 54, no. 11, pp. 1629-1650, 2016.
8. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “An MPC algorithm with combined speed and steering control for obstacle avoidance in autonomous ground vehicles,” Dynamic Systems and Control Conference, 2015.
9. J. Liu, P. Jayakumar, J. L. Stein, and T. Ersal, “A multi-stage optimization formulation for MPC-based obstacle avoidance in autonomous vehicles using a LIDAR sensor,” Dynamic Systems and Control Conference, San Antonio, TX, 2014.
10. J. Liu, P. Jayakumar, J. L. Overholt, J. L. Stein, and T. Ersal, “The role of model fidelity in model predictive control based hazard avoidance in unmanned ground vehicles using LIDAR sensors,” Dynamic Systems and Control Conference, Palo Alto, CA, 2013.

Publications:

1. S. Yu, C. Shen, J. Dallas, B. I. Epureanu, P. Jayakumar, and T. Ersal, “A real-time terrain-adaptive local trajectory planner for high-speed autonomous off-road navigation on deformable terrains,” IEEE Transactions of Intelligent Transportation Systems, submitted.
2. J. Dallas, M. Cole, P. Jayakumar, and T. Ersal, “Terrain adaptive trajectory planning and tracking on deformable terrains,” IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 11255 - 11268, 2021.
3. J. Dallas, K. Jain, Z. Dong, M. Cole, P. Jayakumar, and T. Ersal. “Online Terrain Estimation for Autonomous Vehicles Operating on Deformable Terrains,” Journal of Terramechanics, vol 91, pp. 11-22, 2020.
4. J. Dallas, Y. Weng, and T. Ersal. “Combined Trajectory Planning and Tracking for Autonomous Vehicles on Deformable Terrains,” ASME 2020 Dynamic Systems and Control Conference.

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