In this paper, we introduce a novel coordination strategy for a group of autonomous robots tasked with estimating the source term of an airborne chemical release. This strategy integrates distributed Bayesian filtering, coverage control, information-theoretic sampling, and proximity constraint handling, forming an efficient and fully distributed coordination protocol. In the proposed framework, each robot employs a consensus-based belief update rule, allowing it to adaptively incorporate information from neighbouring robots to ensure a unified belief across the network. The overall control action is designed to maximise information gain while maintaining network connectivity and minimising communication requirements during movement between sampling points. Extensive numerical simulations are conducted to analyse the performance of the proposed strategy, which demonstrate significant performance improvements compared to popular filtering practices and advanced path-planning strategies. The simulation study is also designed to substantiate the design choices of the proposed coordination strategy and to emphasise its advantages.

中文翻译：

---

具有覆盖控制和基于信息论协调的分布式多机器人源项估计


在本文中，我们为一组自主机器人介绍了一种新颖的协调策略，其任务是估计空气中化学物质释放的源项。该策略集成了分布式贝叶斯过滤、覆盖控制、信息论采样和邻近约束处理，形成了高效且完全分布式的协调协议。在所提出的框架中，每个机器人都采用基于共识的信念更新规则，使其能够自适应地合并来自相邻机器人的信息，以确保整个网络的统一信念。总体控制操作旨在最大限度地提高信息增益，同时保持网络连接并最大限度地减少采样点之间移动期间的通信要求。进行了广泛的数值模拟来分析所提出的策略的性能，与流行的过滤实践和先进的路径规划策略相比，这表明了显着的性能改进。模拟研究还旨在证实所提出的协调策略的设计选择并强调其优势。