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This paper introduces a multitask coverage problem for multi-robot systems, where robots must cover areas with multiple sensory demands. For known demands, a federated multitask coverage algorithm is designed and proven convergent. For unknown demands, a multitask Gaussian Process (GP) framework is used to learn sensory demand functions and integrated with the coverage algorithm, achieving sublinear cumulative regret compared to an oracle.
Multi-robot coverage can now handle multiple sensory demands simultaneously, with provable guarantees on performance even when those demands are initially unknown.
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.
