Title: Optimal path planning for an autonomous articulated vehicle with two trailers

Authors: Amr Mohamed; Jing Ren; Haoxiang Lang; Moustafa El-Gindy

Addresses: Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada ' Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), Canada

Abstract: This paper proposed an optimal path planning algorithm for autonomous vehicle with two trailers in autonomous navigation. The proposed algorithm is based on combination of artificial potential field (APF) method and optimal control theory. A linear two-degree-of-freedom vehicle model with both lateral and yaw motion is derived and simulated in MATLAB environment. The optimal control theory is applied to generate an optimal free-obstacle path of the robotic vehicle from a starting point to the goal location. The obstacle-avoidance technique is mathematically modelled using a potential function based on the proposed sigmoid function. The constructed potential field model can achieve an accurate analytic description of objects in three dimensions. Moreover, the proposed model of potential field requires very modest computation at run time. The APF includes both the attractive (the target) and repulsive (the obstacles) potential fields that will control the steering angle of the vehicle so that it can reach to its target location. Several simulations are carried out to check the fidelity of the proposed technique. The illustrated results demonstrate the generated optimal path of autonomous vehicles with consideration of vehicle dynamics constraints, obstacle avoidance and collision free criteria in reaching the goal location.

Keywords: path planning; autonomous vehicle; vehicle with two trailer; artificial potential field; APF; optimal control theory.

DOI: 10.1504/IJAAC.2018.092850

International Journal of Automation and Control, 2018 Vol.12 No.3, pp.449 - 465

Received: 05 Feb 2017
Accepted: 04 Mar 2017
Published online: 01 Jul 2018 *

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