  Distributed sensing by cooperative drone swarms is crucial for several Smart
City applications, such as traffic monitoring and disaster response. Using an
indoor lab with inexpensive drones, a testbed supports complex and ambitious
studies on these systems while maintaining low cost, rigor, and external
validity. This paper introduces the Multi-drone Sensing Experimentation Testbed
(M-SET), a novel platform designed to prototype, develop, test, and evaluate
distributed sensing with swarm intelligence. M-SET addresses the limitations of
existing testbeds that fail to emulate collisions, thus lacking realism in
outdoor environments. By integrating a collision avoidance method based on a
potential field algorithm, M-SET ensures collision-free navigation and sensing,
further optimized via a multi-agent collective learning algorithm. Extensive
evaluation demonstrates accurate energy consumption estimation and a low risk
of collisions, providing a robust proof-of-concept. New insights show that
M-SET has significant potential to support ambitious research with minimal
cost, simplicity, and high sensing quality.
