Experimentation on the motion of an obstacle avoiding robot

TL;DR

This paper presents an experiment with a Lego Mindstorms robot using hill climbing search to navigate mazes, demonstrating AI search algorithms in obstacle avoidance and pathfinding.

Contribution

It implements a maze navigation experiment with a Lego robot using hill climbing, showcasing practical application of AI search algorithms in robotics.

Findings

Robot successfully navigates maze using hill climbing

Demonstrates effectiveness of simple AI algorithms in obstacle avoidance

Flexible maze design tested with robot

Abstract

An intelligent robot can be used for applications where a human is at significant risk (like nuclear, space, military), the economics or menial nature of the application result in inefficient use of human workers (service industry, agriculture), for humanitarian uses where there is great risk (demining an area of land mines, urban search and rescue). This paper implements an experiment on one of important fields of AI Searching Algorithms, to find shortest possible solution by searching the produced tree. We will concentrate on Hill climbing algorithm, which is one of simplest searching algorithms in AI. This algorithm is one of most suitable searching methods to help expert system to make decision at every state, at every node. The experimental robot will traverse the maze by using sensors plugged on it. The robot used is E.V.3 Lego Mind storms, with native software for programming LabView. The reason we chose this robot is that it interacts quickly with sensors and can be reconstructed in many ways. This programmed robot will calculate the best possibilities to find way out of maze. The maze is made of wood, and it is adjustable, as robot should be able to leave the maze in any design.