This article presents a model predictive control based obstacle avoidance algorithm for autonomous ground vehicles in unstructured environments. The novelty of the algorithm is the simultaneous optimization of speed and steering without a priori knowledge about the obstacles. Obstacles are detected using a planar light detection and ranging sensor and a multi-phase optimal control problem is formulated to optimize the speed and steering commands within the detection range. Acceleration capability of the vehicle as a function of speed, and stability and handling concerns such as tire lift-off are taken into account as constraints in the optimization problem, whereas the cost function is formulated to navigate the vehicle as quickly as possible with smooth control commands. Thus, a safe and quick navigation is enabled without the need for a preloaded map of the environment. Simulation results show that the proposed algorithm is capable of navigating the vehicle through obstacle fields that cannot be cleared with steering control alone.
- Dynamic Systems and Control Division
An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles
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Liu, J, Jayakumar, P, Stein, JL, & Ersal, T. "An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles." Proceedings of the ASME 2015 Dynamic Systems and Control Conference. Volume 3: Multiagent Network Systems; Natural Gas and Heat Exchangers; Path Planning and Motion Control; Powertrain Systems; Rehab Robotics; Robot Manipulators; Rollover Prevention (AVS); Sensors and Actuators; Time Delay Systems; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamics Control; Vibration and Control of Smart Structures/Mech Systems; Vibration Issues in Mechanical Systems. Columbus, Ohio, USA. October 28–30, 2015. V003T44A003. ASME. https://doi.org/10.1115/DSCC2015-9747
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