多机器人协同覆盖新方法，能自适应学习环境需求并逼近最优性能。
Multi-Robot Multitask Gaussian Process Estimation and Coverage
- 基于多任务高斯过程动态学习环境感知需求
- 算法实现子线性累积遗憾，逼近已知需求的最优解
- 适合需要自适应覆盖的多机器人系统研究者
覆盖控制对优化传感器部署以监控或覆盖具有感知需求的区域至关重要。传统覆盖仅涉及单任务机器人，而当前自主能力提升使多任务操作成为可能。本文提出一种新型多任务覆盖问题，并针对已知与未知感知需求两种情况分别解决：对于已知需求，设计联邦多任务覆盖算法并证明其收敛性；对于未知需求，采用多任务高斯过程（GP）框架学习感知需求函数，并与覆盖算法结合，开发出自适应算法。引入新的多任务覆盖遗憾概念，用于衡量自适应算法相对于事先知晓需求函数的最优代理（oracle）的性能差距。理论证明该算法达到子线性累积遗憾，数值实验验证了其有效性。
原文摘要 · Abstract (English)
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.
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