OA-PINN: Efficient Obstacle Avoidance for Autonomous Vehicle Safety with Physics-Informed Neural Networks

Majumder, R and Chakaravarthy, SS and Samahith, SA and Sundaram, S and Patel, H (2024) OA-PINN: Efficient Obstacle Avoidance for Autonomous Vehicle Safety with Physics-Informed Neural Networks. In: IEEE International Conference on Electronics, Computing and Communication Technologies, CONECCT 2024, 12 July 2024 through 14 July 2024, Bangalore.

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Abstract

The safe operation of Autonomous Vehicles (AV) primarily relies on effective collision avoidance techniques. Therefore it is essential to integrate obstacle avoidance features into the motion planning of vehicles. This research focuses on obstacle avoidance using the CBF which has evolved as an efficient mathematical tool for unmanned vehicles to ensure safe navigation. A Quadratic Programming (QP) problem for collision avoidance is formulated using CBF as a constraint. The Hamilton-Jacobi-Bellman (HJB) equation derived for the QP problem gives rise to a Partial Differential Equation (PDE), the solution of which generates the necessary control input for the vehicles to successfully avoid the obstacles. The key novelty of this research lies in solving the HJB equation using a multilayer perceptron, called a Physics-informed Neural Network (PINN), which needs less computation in a cluttered environment. The proposed methodology integrates the safety and reliability aspects of CBF with an obstacle avoidance solution using PINN (OA-PINN). The performance of OA-PINN is validated using numerical simulations and related hardware experiments. © 2024 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings of CONECCT 2024 - 10th IEEE International Conference on Electronics, Computing and Communication Technologies
Publisher:	Institute of Electrical and Electronics Engineers Inc.
Additional Information:	The copyright for this article belongs to the publishers.
Keywords:	Magnetic levitation vehicles; Multilayer neural networks; Quadratic programming, Autonomous Vehicles; Barriers functions; Collisions avoidance; Control barrier function; Control barriers; Hamilton Jacobi Bellman equation; Neural-networks; Obstacles avoidance; Physic-informed neural network; Quadratic programming problems, Motion planning
Department/Centre:	Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited:	29 Oct 2024 12:44
Last Modified:	29 Oct 2024 12:44
URI:	http://eprints.iisc.ac.in/id/eprint/86637

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