Traction Adaptive Motion Planning and Control at the Limits of Handling

Abstract

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 constrained finite time optimal control problem (CFTOC) 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.

Model predictive path-following framework for generalised N-trailer vehicles in the presence of dynamic obstacles modelled as soft constraints

“…o < 0 into the problem described by Eq. (9), where (x i , y i ) represents the coordinates of the i-th segments of the vehicle, and (x o , y o ) represents the obstacle's position in the plane. For case ii), no extra constraints are added, but additional terms are introduced into the cost function from Eq.…”

Self-Learning Robot Autonomous Navigation with Deep Reinforcement Learning Techniques

“…The predictive control model also addresses the problem of planning feasible trajectories under driving situations close to the limit. In [18], the parameters of the embedded model were updated over the horizon in situations close to the limit based on a predictive friction estimate to obtain even more reliable and feasible trajectories. However, there was still a reliance on models or estimators that could have differences with respect to reality and the computational consumption was still very high due to the usage of an iterative optimizer.…”

“…The commanded linear and angular velocities of the robot were converted in wheel rotational speed by means of the kinematic equations of the differential robot ( 17) and (18).…”