Sparse Robot Swarms: Moving Swarms to Real-World Applications.

Area of Science:

• Robotics
• Artificial Intelligence
• Distributed Systems

Background:

• Current robot swarms operate autonomously with local perception and neighbor coordination.
• Existing swarm implementations are limited to controlled indoor environments and close-range robot proximity.
• Dense swarms with close-range communication are impractical for large-scale, long-distance applications such as environmental monitoring.

Purpose of the Study:

• To introduce and explore the concept of sparse swarms.
• To address the limitations of dense swarms in real-world, large-distance applications.
• To identify challenges and propose solutions for developing sparse swarms.

Main Methods:

• Proposing the sparse swarm concept.
• Illustrating sparse swarm applications through four scenarios.
• Analyzing challenges in hardware and communication-constrained coordination algorithms for sparse swarms.

Main Results:

• The sparse swarm concept is proposed as a solution for scalable robot groups operating over large distances.
• Application scenarios, including forest monitoring with rovers, highlight the challenges and potential of sparse swarms.
• Discussion of hardware and algorithmic solutions for communication-constrained coordination in sparse swarms.

Conclusions:

• Sparse swarms offer a viable approach for deploying scalable robot groups in large-scale, real-world environments.
• Further research is needed to address theoretical and practical challenges in sparse swarm development.
• The sparse swarm concept has potential applications in environmental monitoring, precision agriculture, and beyond.