Design and Development of a Research Oriented Low Cost Robotics Platform with a Novel Dynamic Global Path Planning Approach

TL;DR

This paper presents a low-cost robotics platform integrated with a novel dynamic global path planning method, enabling affordable and effective autonomous navigation research and education.

Contribution

It introduces a cost-effective AlphaBot2-based platform with a new dynamic global path planning approach within ROS, facilitating accessible robotics research.

Findings

Improved navigation performance with dynamic path planning

Cost reduction enabling wider accessibility

Successful integration of Intel RealSense D435 camera

Abstract

Autonomous navigation systems based on computer vision sensors often require sophisticated robotics platforms which are very expensive. This poses a barrier for the implementation and testing of complex localization, mapping, and navigation algorithms that are vital in robotics applications. Addressing this issue, in this work, Robot Operating System (ROS) supported mobile robotics platforms are compared and an end-to-end implementation of an autonomous navigation system based on a low-cost educational robotics platform, AlphaBot2 is presented, while integrating the Intel RealSense D435 camera. Furthermore, a novel approach to implement dynamic path planners as global path planners in the ROS framework is presented. We evaluate the performance of this approach and highlight the improvements that could be achieved through a dynamic global path planner. This low-cost modified AlphaBot2 robotics platform along with the proposed dynamic global path planning approach will be useful for researchers and students for getting hands-on experience with computer vision-based navigation systems.