Traction Adaptive Motion Planning and Control at the Limits of Handling
Journal article, 2022

In this article, we address the problem of motion planning and control at the limits of handling, under locally varying traction conditions. We propose a novel solution method where traction variations over the prediction horizon are represented by time-varying tire force constraints, derived from a predictive friction estimate. A \CFTOClong (\CFTOCshort) is solved in a receding horizon fashion, imposing these time-varying constraints. Furthermore, our method features an integrated sampling augmentation procedure that addresses the problems of infeasibility and sensitivity to local minima that arise at abrupt constraint alterations, for example, due to sudden friction changes. We validate the proposed algorithm on a Volvo FH16 heavy-duty vehicle, in a range of critical scenarios. Experimental results indicate that traction adaptive motion planning and control improves the vehicle's capacity to avoid accidents, both when adapting to low local traction, by ensuring dynamic feasibility of the planned motion, and when adapting to high local traction, by realizing high traction utilization.

collision avoidance

Vehicle dynamics

motion planning

autonomous vehicles

Planning

friction

Trajectory

Adaptive control

optimization-based motion planning

vehicle control.

sampling-based motion planning

Force

Tires

Friction

Roads

Author

Lars Svensson

Royal Institute of Technology (KTH)

Monimoy Bujarbaruah

University of California

Arpit Karsolia

Chalmers, Electrical Engineering, Systems and control

Christian Berger

University of Gothenburg

Martin Torngren

Royal Institute of Technology (KTH)

IEEE Transactions on Control Systems Technology

1063-6536 (ISSN) 15580865 (eISSN)

Vol. 30 5 1888-1904

Subject Categories

Robotics

Control Engineering

Signal Processing

DOI

10.1109/TCST.2021.3129373

More information

Latest update

10/17/2022