MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles. (2016)

• Record Type: Journal Article
• Authors: Perumal, D. Ganesha; Srinivasan, Seshadhri; Subathra, B.; Saravanakumar, G.; Ayyagari, Ramakalyan
• Is Part Of: International journal of heavy vehicle systems. Volume 23:Number 4(2016)
• Page Start: 350
• Page End: 369
• Publication Date: 2016
• Subjects: 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; Motor vehicles -- Design and construction -- Periodicals; Commercial vehicles -- Design and construction -- Periodicals; Motor vehicles -- Design and construction; Periodicals; 629.224
• Languages: English
• ISSNs: 1744-232X

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.