A Novel Navigation System for an Autonomous Mobile Robot in an Uncertain Environment

TL;DR

This paper presents a new robot navigation system that detects, classifies, and predicts obstacles to find optimal collision-free paths efficiently in uncertain environments.

Contribution

It introduces a novel integration of obstacle detection, classification, and path planning algorithms tailored for autonomous robots in unpredictable settings.

Findings

Effective collision avoidance in simulations and real robot tests.

Reduced computational complexity compared to existing methods.

Successful navigation in eight diverse scenarios.

Abstract

In this paper, we developed a new navigation system, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Mophin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The experiments on simulation and a robot for eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment.