Coordinated Multi-Agent Exploration, Rendezvous, & Task Allocation in Unknown Environments with Limited Connectivity

The lack of communication between agents in a multi-robot system is often regarded as a limiting factor that can affect and delay cooperative exploration and exploitation of cluttered and uncertain environments. On the contrary, this paper proposes a complete planning framework to enable cooperative behavior without the need for constant communication between robots, demonstrating drastic improvements in task completion and coverage time as compared to both fully connected robotic networks and widely used frontier-based exploration methods. Specifically, the proposed scheme considers three behaviors: i) exploration, promoting separation and disconnection, ii) rendezvous to reconnect and share information gained during exploration, and iii) task allocation for prioritized objectives. Exploration is achieved via a Sobel edge detection frontier algorithm that enables navigation of unknown complex (both convex and non-convex) environments. Once a task is discovered, a multi-objective weighted sum optimization method is proposed for allocating tasks based on prioritization and expectation estimation. The utility, generality, and scalability of the proposed approach is demonstrated using extensive simulations and experiments with unmanned ground vehicles in various cluttered environments.