An adaptive motion planning algorithm for obstacle avoidance in autonomous vehicle parking

Abstract

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.

Hybrid approach for multi-objective optimization path planning with moving target

“…A robot follows a complete path which is generated by an Indonesian J Elec Eng & Comp Sci ISSN: 2502-4752  algorithm before it begins to move. In order to determine the path of a mobile robot locally, the robot relies on a local sensor to collect information about the environment and construct a new path based on that information [3]. The cell decomposition and roadmap have been used to solve problems related to path planning.…”