Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi

• URL: http://arxiv.org/abs/2112.03577v1
• Date: Tue, 7 Dec 2021 09:00:14 GMT
• Title: Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi
• Authors: Serena Raju, Sherin Shibu, Riya Mol Raji and Joel Thomas
• Abstract summary: The proposed system is a cost-efficient approach that is implemented to facilitate a Raspberry Pi controlled four-wheel-drive non-holonomic robot map a grid. Q learning and Deep-Q learning are used to find the optimal path while avoiding collision with static obstacles. A novel algorithm to decode an array of directions into accurate movements is also proposed.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, pragmatic implementation of an indoor autonomous delivery system that exploits Reinforcement Learning algorithms for path planning and collision avoidance is audited. The proposed system is a cost-efficient approach that is implemented to facilitate a Raspberry Pi controlled four-wheel-drive non-holonomic robot map a grid. This approach computes and navigates the shortest path from a source key point to a destination key point to carry out the desired delivery. Q learning and Deep-Q learning are used to find the optimal path while avoiding collision with static obstacles. This work defines an approach to deploy these two algorithms on a robot. A novel algorithm to decode an array of directions into accurate movements in a certain action space is also proposed. The procedure followed to dispatch this system with the said requirements is described, ergo presenting our proof of concept for indoor autonomous delivery vehicles.

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