Multi-Robot Multitask Gaussian Process Estimation and Coverage
This addresses the challenge of efficient multi-robot deployment for monitoring applications, representing an incremental advance by extending single-task coverage to multitask settings.
The paper tackles the problem of deploying multi-robot systems for coverage tasks with multiple sensory demands, developing algorithms for both known and unknown demand scenarios. For unknown demands, they integrate multitask Gaussian Processes with coverage control to achieve sublinear cumulative regret, with numerical results demonstrating performance.
Coverage control is essential for the optimal deployment of agents to monitor or cover areas with sensory demands. While traditional coverage involves single-task robots, increasing autonomy now enables multitask operations. This paper introduces a novel multitask coverage problem and addresses it for both the cases of known and unknown sensory demands. For known demands, we design a federated multitask coverage algorithm and establish its convergence properties. For unknown demands, we employ a multitask Gaussian Process (GP) framework to learn sensory demand functions and integrate it with the multitask coverage algorithm to develop an adaptive algorithm. We introduce a novel notion of multitask coverage regret that compares the performance of the adaptive algorithm against an oracle with prior knowledge of the demand functions. We establish that our algorithm achieves sublinear cumulative regret, and numerically illustrate its performance.