Online decentralized information gathering with spatial-temporal constraints.

We are interested in coordinating a team of autonomous mobile sensor agents in performing a cooperative information gathering task while satisfying mission-critical spatial-temporal constraints. In particular, we present a novel set of constraint formulations that address inter-agent collisions, collisions with static obstacles, network connectivity maintenance, and temporal-coverage in a resource-efficient manner. These constraints are considered in the context of the target search problem, where the team plans trajectories that maximize the probability of target detection. We model constraints continuously along the agents' trajectories and integrate these constraint models into decentralized team planning using a computationally efficient solution method based on the Lagrangian formulation and decentralized optimization. We validate our approach in simulation with five UAVs performing search, and through hardware experiments with four indoor mobile robots. Our results demonstrate team planning with spatial-temporal constraints that preserves the performance of unconstrained information gathering and is feasible to implement with reasonable computational and communication resources. [ABSTRACT FROM AUTHOR]