Pragmatic Implementation of Reinforcement Algorithms For Path Finding On Raspberry Pi

TL;DR

This paper presents a cost-effective indoor autonomous delivery system using Raspberry Pi, implementing reinforcement learning algorithms like Q-learning and Deep-Q learning for path planning and collision avoidance.

Contribution

It introduces a pragmatic deployment of RL algorithms on a Raspberry Pi robot, including a novel direction decoding algorithm for precise movement execution.

Findings

Successful implementation of RL algorithms on Raspberry Pi for navigation

Effective collision avoidance in indoor environments

Proof of concept for autonomous delivery vehicles

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.