Executive Summary
This thesis explores the coordination of an unmanned multi-vehicle team, specifically a UAV and UGV, for navigation in cluttered environments. It outlines a novel approach using computer vision and optimal trajectory algorithms for collision-free operations, validated through lab experiments. The research compares global optimal trajectory planning with reactive obstacle avoidance techniques.
Why It Matters
This document is crucial for defense analysts as it details advanced navigation and coordination techniques for unmanned aerial and ground vehicles, directly impacting future military robotics and autonomous mission capabilities. It provides insights into the practical implementation of multi-robot systems in complex operational environments.
Key Takeaways
- A novel approach for UAV-UGV coordination uses computer vision and optimal trajectory algorithms for navigation in cluttered environments.
- The Inverse Dynamics in the Virtual Domain (IDVD) method enables collision-free path planning by separating spatial and temporal planning.
- The system was validated in a controlled lab environment, comparing global optimal trajectory planning with sonar-based reactive obstacle avoidance.
- The research highlights the use of commercial off-the-shelf components (Quanser Qball, Qbot) for military-relevant robotics development.
Strategic Relevance
This research directly contributes to the development of autonomous multi-robot systems for military applications, enhancing capabilities for reconnaissance, surveillance, and hazardous environment operations. Improved navigation and coordination are critical for effective deployment of UAV-UGV teams in complex, contested spaces, reducing human risk and increasing operational efficiency.