A Queue-Stabilizing Framework for Networked Multi-Robot Exploration

Motivated by planetary exploration, we consider the problem of deploying a network of mobile robots to explore an unknown environment and share information with a stationary data sink. The configuration of robots affects both network connectivity and the accuracy of relative localization. Robots explore autonomously and can store data locally in their queues. When a communication path exists to the data sink, robots transfer their data. Because robots may fail in a non-deterministic manner, causing loss of the data in their queues, enabling communication is important. However, strict constraints on connectivity and relative positions limit exploration. To take a more flexible approach to managing these multiple objectives, we use Lyapunov-based stochastic optimization to maximize new information while using virtual queues to constrain time-average expectations of metrics of interest. These include queueing delay, network connectivity, and localization uncertainty. The result is a distributed online controller which autonomously and strategically breaks and restores connectivity as needed. We explicitly account for obstacle avoidance, limited sensing ranges, and noisy communication/ranging links with line-of-sight occlusions. We use queuing theory to analyze the average delay experienced by data in our system and guarantee connectivity will be recovered when feasible. We demonstrate in simulation that our queue-stabilizing controller can reduce localization uncertainty and achieve better coverage than two state of the art approaches.