Open AccessAn adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking
Author(s) -
Naitik M. Nakrani,
Maulin Joshi
Publication year - 2021
Publication title -
iaes international journal of artificial intelligence
Language(s) - English
Resource type - Journals
eISSN - 2252-8938
pISSN - 2089-4872
DOI - 10.11591/ijai.v10.i3.pp687-697
Subject(s) - obstacle avoidance , computer science , motion planning , obstacle , path (computing) , controller (irrigation) , fuzzy logic , trajectory , autonomous system (mathematics) , artificial intelligence , control engineering , real time computing , mobile robot , robot , engineering , agronomy , programming language , physics , astronomy , political science , law , biology
In the recent era, machine learning-based autonomous vehicle parking and obstacle avoidance navigation have drawn increased attention. An intelligent design is needed to solve the autonomous vehicles related problems. Presently, autonomous parking systems follow path planning techniques that generally do not possess a quality and a skill of natural adapting behavior of a human. Most of these designs are built on pre-defined and fixed criteria. It needs to be adaptive with respect to the vehicle dynamics. A novel adaptive motion planning algorithm is proposed in this paper that incorporates obstacle avoidance capability into a standalone parking controller that is kept adaptive to vehicle dimensions to provide human-like intelligence for parking problems. This model utilizes fuzzy membership thresholds concerning vehicle dimensions and vehicle localization to enhance the vehicle’s trajectory during parking when taking into consideration obstacles. It is generalized for all segments of cars, and simulation results prove the proposed algorithm’s effectiveness.