IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 10, No. 3, September 2021, pp. 687~697 ISSN: 2252-8938, DOI: 10.11591/ijai.v10.i3.pp687-697  687 Journal homepage: http://ijai.iaescore.com An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking Naitik M. Nakrani 1 , Maulin M. Joshi 2 1 Department of Electronics and Communication Engineering, Uka Tarsadia University, India 2 Department of Electronics and Communication Engineering, Gujarat Technological University, India Article Info ABSTRACT Article history: Received Dec 3, 2020 Revised Apr 14, 2021 Accepted May 15, 2021 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. Keywords: Adaptive fuzzy systems Autonomous vehicles Dynamic path planning Potential field Ultrasonic sensing This is an open access article under the CC BY-SA license. Corresponding Author: Naitik M. Nakrani Department of Electronics and Communication Engineering Uka Tarsadia University Bardoli-Mahuva Road, Surat, India Email: naitiknakrani@gmail.com 1. INTRODUCTION Autonomous parking systems and navigation issues have attracted much research recently due to the development of autonomous vehicle technologies. One prominent reason for using this technique is to improve human performances via advanced robotic technologies, such as ‘smart parking’ and ‘smart highways’. In modern times, these technologies demand that autonomous robots function intelligently as a human in an unknown and unstructured environment. A mobile robot must sense its surroundings and determine appropriate actions to work in such under-constrained, dangerous, and dynamic domains. Autonomous parking involves proper path planning and control for a car-like mobile robot (CLMR) without human intervention. Parking is considered a challenging task for a machine due when the environment considered is dynamic and uncertain. In such an environment, online parking systems give better performance compared to traditional offline parking systems. Online parking systems carried out path planning in a parallel while; i) moving towards a goal and ii) perceiving the environment. This perceived information is used in multiple ways in parking systems, e.g., environment mapping, parking space detection, obstacle detection and avoidance, collision detection, and following to walls. Past studies involved different types of sensors in the parking system for various purposes. In earlier work, [1] presented a skill-based fuzzy logic approach for forward and reverse parking. They used six infrared sensors to achieve wall following behavior and past parking posture correction. Another skill-based fuzzy logic approach is given in [2]. Their algorithm generates