Title: MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles
Authors: D. Ganesha Perumal; Seshadhri Srinivasan; B. Subathra; G. Saravanakumar; Ramakalyan Ayyagari
Addresses: Department of Electrical and Electronics Engineering, Kalasalingam University, Srivilliputtur 626126, India ' International Research Center, Kalasalingam University, Srivilliputtur 626126, India ' International Research Center, Kalasalingam University, Srivilliputtur 626126, India ' Department of Electrical and Computer Engineering, University of Gondar, Post Box No. 196, Gondar, Ethiopia ' Department of Control and Instrumentation Engineering, National Institute of Technology-Tiruchirappalli, Tiruchirappalli 620015, India
Abstract: Autonomous vehicles (AVs) manoeuvring in unknown environment require path-planning algorithms that are safe, yet optimal to circumvent dynamic obstacles with minimum fuel-cost. This investigation presents an autonomous vehicle path-planning (AVPP) controller that uses mixed integer linear programming to decide the blending and switching actions among possible vehicle behaviours depending on local sensed information. Our results illustrate the safety and optimality of the controller for AVPP in unknown environments with dynamic obstacles. Comparison with existing methods shows that the proposed method is more robust to collisions than the fuzzy and extended Kalman filter based arbitration mechanism studied in literature. Further, as behaviours breakdown the complex path-planning problem into simple tasks, controller realisation becomes simple.
Keywords: autonomous vehicles; vehicle path planning; MILP; mixed integer linear programming; behaviour-based control; autonomous electric vehicles; hybrid supervisory control; controller design; unknown environments; dynamic obstacles; collision avoidance.
International Journal of Heavy Vehicle Systems, 2016 Vol.23 No.4, pp.350 - 369
Received: 17 Nov 2014
Accepted: 17 Jul 2015
Published online: 13 Sep 2016 *