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An MPC Algorithm With Combined Speed and Steering Control for Obstacle Avoidance in Autonomous Ground Vehicles

[+] Author Affiliations
Jiechao Liu, Jeffrey L. Stein, Tulga Ersal

University of Michigan, Ann Arbor, MI

Paramsothy Jayakumar


Paper No. DSCC2015-9747, pp. V003T44A003; 10 pages
  • 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
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5726-7
  • Copyright © 2015 by ASME


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.

Copyright © 2015 by ASME
Topics: Algorithms , Vehicles



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