The dynamics of an autonomous unmanned ground vehicle (UGV) that is at least the size of a passenger vehicle are critical to consider during obstacle avoidance maneuvers to ensure vehicle safety. Methods developed so far do not take vehicle dynamics and sensor limitations into account simultaneously and systematically to guarantee the vehicle’s dynamical safety during avoidance maneuvers. To address this gap, this paper presents a model predictive control (MPC) based obstacle avoidance algorithm for high-speed, large-size UGVs that perceives the environment only through the information provided by a sensor, takes into account the sensing and control delays and the dynamic limitations of the vehicle, and provides smooth and continuous optimal solutions in terms of minimizing travel time. Specifically, information about the environment is obtained using an on-board Light Detection and Ranging (LIDAR) sensor. Ensuring the vehicle’s dynamical safety is translated into avoiding single tire lift-off. The obstacle avoidance problem is formulated as a multi-stage optimal control problem with a unique optimal solution. To solve the optimal control problem, it is transcribed into a nonlinear programming (NLP) problem using a pseudo-spectral method, and solved using the interior-point method. Sensing and control delays are explicitly taken into consideration in the formulation. Simulation results show that the algorithm is capable of generating smooth control commands to avoid obstacles while guaranteeing dynamical safety.
- Dynamic Systems and Control Division
A Multi-Stage Optimization Formulation for MPC-Based Obstacle Avoidance in Autonomous Vehicles Using a LIDAR Sensor
Liu, J, Jayakumar, P, Stein, JL, & Ersal, T. "A Multi-Stage Optimization Formulation for MPC-Based Obstacle Avoidance in Autonomous Vehicles Using a LIDAR Sensor." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 2: Dynamic Modeling and Diagnostics in Biomedical Systems; Dynamics and Control of Wind Energy Systems; Vehicle Energy Management Optimization; Energy Storage, Optimization; Transportation and Grid Applications; Estimation and Identification Methods, Tracking, Detection, Alternative Propulsion Systems; Ground and Space Vehicle Dynamics; Intelligent Transportation Systems and Control; Energy Harvesting; Modeling and Control for Thermo-Fluid Applications, IC Engines, Manufacturing. San Antonio, Texas, USA. October 22–24, 2014. V002T30A006. ASME. https://doi.org/10.1115/DSCC2014-6269
Download citation file: