Autonomous Mobile Robot Navigation: Tracking problem

TL;DR

This paper explores key behaviors for autonomous robot navigation, including odometry, target tracking, and obstacle avoidance, validated through experiments on different robot platforms in simulated and real environments.

Contribution

It introduces integrated navigation techniques combining vision, ultrasonic sensors, and odometry for improved autonomous robot performance.

Findings

Odometry achieves precise path following with wheel encoder data.

Vision-based target tracking effectively pursues designated targets.

Ultrasonic sensors enable safe obstacle avoidance in cluttered environments.

Abstract

This paper presents a study on autonomous robot navigation, focusing on three key behaviors: Odometry, Target Tracking, and Obstacle Avoidance. Each behavior is described in detail, along with experimental setups for simulated and real-world environments. Odometry utilizes wheel encoder data for precise navigation along predefined paths, validated through experiments with a Pioneer robot. Target Tracking employs vision-based techniques for pursuing designated targets while avoiding obstacles, demonstrated on the same platform. Obstacle Avoidance utilizes ultrasonic sensors to navigate cluttered environments safely, validated in both simulated and real-world scenarios. Additionally, the paper extends the project to include an Elegoo robot car, leveraging its features for enhanced experimentation. Through advanced algorithms and experimental validations, this study provides insights into developing robust navigation systems for autonomous robots.