Obstacle Avoidance Using Model Predictive Control: An Implementation and Validation Study Using Scaled Vehicles 2020-01-0109
Over the last decade, tremendous amount of research and progress has been made towards developing smart technologies for autonomous vehicles such as adaptive cruise control, lane keeping assist, lane following algorithms, and decision-making algorithms. One of the fundamental objectives for the development of such technologies is to enable autonomous vehicles with the capability to avoid obstacles and maintain safety. Automobiles are real-world dynamical systems - possessing inertia, operating at varying speeds, with finite accelerations/decelerations during operations. Deployment of autonomy in vehicles increases in complexity multi-fold especially when high DOF vehicle models need to be considered for robust control. Model Predictive Control (MPC) is a powerful tool that is used extensively to control the behavior of complex, dynamic systems. As a model-based approach, the fidelity of the model and selection of model-parameters plays a role in ultimate performance. Hardware-in-the-loop testing of such algorithms can often prove to be complex in its design as well as in its implementation. Therefore, in this paper, we explore a less-used deployment toolchain that combines the power of ROS (Robot Operating System) for intra-robot communication with motors and sensors with the rich library of controller models in Simulink Real-Time. In particular we explore this rapid-control-prototyping in real-time to deploy Model Predictive Control for Obstacle Avoidance on a ROS-based scaled-vehicle. We found that this framework is user-friendly and contains great potential for educational and research-bed deployments - with a short development and deployment time that can fit neatly in one semester.
Citation: Bulsara, A., Raman, A., Kamarajugadda, S., Schmid, M. et al., "Obstacle Avoidance Using Model Predictive Control: An Implementation and Validation Study Using Scaled Vehicles," SAE Technical Paper 2020-01-0109, 2020, https://doi.org/10.4271/2020-01-0109. Download Citation