Vehicle-Dynamics-Conscious Real-Time Hazard Avoidance in Autonomous Ground Vehicles

This project began in 2012 and was completed in 2016.

Unmanned ground vehicles (UGVs) are gaining importance and finding increased utility in both military and commercial applications. They hold a great potential for increasing mission performance, combat effectiveness, and personnel safety. UGVs span a large spectrum in terms of both platform size and mode of operation. In terms of platform size, a UGV can be a vehicle from a small ground robot to a heavy, high speed vehicle; whereas the mode of operation can range from teleoperation to full autonomy.

This scope of this work was large, fully autonomous UGVs. Within this scope, the specific problem that the work aimed to address was ensuring dynamic safety during hazard avoidance maneuvers. The term “dynamic safety” refers to not only preventing the vehicle from running into obstacles, but also ensuring that the maneuver does not induce any stability or handling issues such as excessive side slip, tire lift off, or rollover.

Existing hazard avoidance algorithms developed for small robots do not deliver the desired performance in larger UGVs. The overarching objective of this project was to incorporate high-fidelity vehicle models into real-time hazard avoidance for a large autonomous vehicle to generate dynamically safe obstacle avoidance maneuvers while minimizing the impact on travel time.

This project delivered a model predictive control based framework, which handles the path planning and path tracking problems simultaneously to push a large UGV to its limits while navigating unstructured environments without a priori knowledge about the environment. Robustness of the framework was improved with a novel double-worst-case formulation that rendered the algorithm robust to parametric uncertainties that can lead to either collisions or tire lift-off.

Select publications:

• 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.
• 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.
• 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.
• 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.
• 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.

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