Algorithm for obstacle avoidance in mobile robot navigation using Q-learning and blockchain technology

Abstract

A robot movement modeling algorithm with obstacle avoidance using the Q-learning machine learning method is proposed. Q-learning allows for preserving the rewards obtained during modeling by performing optimal actions in each specific state. The Q-table contains information about the state and actions of the robot. Storing the Q-table in the blockchain using IPFS (InterPlanetary File System) technology ensures reliable and decentralized storage of data about the robot's states and actions. Content addressing in IPFS separates the data from its location and retrieves files from multiple sources in a peer-to-peer mode. A computational experiment for the proposed algorithm was conducted using a robot movement simulation environment. In the Gazebo 11 visualization package, it was shown that using the new algorithm, obstacles are avoided faster (by 59.8 %) compared to the previous version of the algorithm.