Exploration of unknown indoor regions by a swarm of energy-constrained drones

TL;DR

This paper introduces distributed algorithms for energy-efficient exploration of unknown indoor regions by a swarm of autonomous, energy-constrained drones, utilizing local sensing, virtual pheromones, and optimized entry rates.

Contribution

It presents novel algorithms for coordinated exploration with energy constraints, including virtual pheromone-based communication and energy optimization strategies.

Findings

Algorithms achieve uniform coverage of indoor regions.

Settled agents act as virtual pheromones to guide exploration.

Optimal entry rate minimizes total energy consumption.

Abstract

Several distributed algorithms are presented for the exploration of unknown indoor regions by a swarm of flying, energy constrained agents. The agents, which are identical, autonomous, anonymous and oblivious, uniformly cover the region and thus explore it using predefined action rules based on locally sensed information and the energy level of the agents. While flying drones have many advantages in search and rescue scenarios, their main drawback is a high power consumption during flight combined with limited, on-board energy. Furthermore, in these scenarios agent size is severely limited and consequently so are the total weight and capabilities of the agents. The region is modeled as a connected sub-set of a regular grid composed of square cells that the agents enter, over time, via entry points. Some of the agents may settle in unoccupied cells as the exploration progresses. Settled agents conserve energy and become virtual pheromones for the exploration and coverage process, beacons that subsequently aid the remaining, and still exploring, mobile agents. The termination of the coverage process is based on a backward propagating information diffusion scheme. Various algorithmical alternatives are discussed and upper bounds derived and compared to experimental results. Finally, an optimal entry rate that minimizes the total energy consumption is derived for the case of a linear regions.