Experimental validation of geometric path following control with demand supervision on an over-actuated robotic vehicle

Abstract

This work describes the development and experimental validation of a geometric path following control strategy with demand supervision applied to an over-actuated robotic vehicle, the ROboMObil [1]. The proposed method enables the ROboMObil to automatically follow paths while the driver is free to control the velocity along the path. Beside the longitudinal degree of freedom, two lateral degrees of freedom can be controlled relative to the path. If this demand interface were provided without supervision, the driver may potentially overwrite the path following control in a manner such that the vehicle limits are violated and the vehicle becomes unstable. To avoid such critical situations a demand supervisor is introduced into the path following framework. The work concludes by a simulative demonstration of the supervised control system and an experimental validation of the presented approach implemented in the ROboMObil.

Learning-Based Path Following Control for an Over-Actuated Robotic Vehicle

“…The interface to the path following control is a motion demand parametrized by the path arc length : ( ) ∈ ℝ 5 . It consists of the following five quantities: the absolute estima P I = [ P I ( ), P I ( )] of the reference path, the corresponding path orientation P ( ), its curvature P ( ) and a desired longitudinal velocity P, P ( ):…”

“…In previous works, this kind of control problem has been tackled with model-based control methods, where a kinematic or dynamic model of the vehicle is employed to systematically construct the control law, while fulfilling important control requirements, like input and state constraints as well as closed loop robust stability [1], [2], [3], [4], [5], [6].…”

Optimal Velocity Profile Generation for Semi-Autonomous Vehicles

“…Fig. 1 shows a schematic of the motion planning framework of the ROboMObil, where the velocity profile generation (marked in red) together with the online path planner [5] serves as an input to the vehicle's path following control [6]. Thereby, the velocity planning module receives the driver's preference 𝜖 via joystick input, the path curvature 𝜅 from the path planner, and environmental information, such as the tyre-road friction coefficient 𝜇 and the slope angle of the path 𝛾.…”