MILP based autonomous vehicle path-planning controller for unknown environments with dynamic obstacles Online publication date: Mon, 26-Sep-2016
by D. Ganesha Perumal; Seshadhri Srinivasan; B. Subathra; G. Saravanakumar; Ramakalyan Ayyagari
International Journal of Heavy Vehicle Systems (IJHVS), Vol. 23, No. 4, 2016
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.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Heavy Vehicle Systems (IJHVS):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com