A Distributed Active Perception Strategy for Source Seeking and Level Curve Tracking

• 成果类型: Article
• 署名作者: Al-Abri, Said; Zhang, Fumin
• 署名单位: University System of Georgia; Georgia Institute of Technology
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2021.3077457
• 发表日期: 2022
• 页码: 2459-2465
• 关键词: Principal component analysis; Position measurement; Active perception; CONVERGENCE; Covariance matrices; tracking; Heuristic algorithms; Bio-inspired algorithms; distributed active perception; input-to-state stability; level curve tracking; singular perturbation; source seeking

摘要

Algorithms for multiagent systems to locate a source or to follow a desired level curve of spatially distributed scalar fields generally require sharing field measurements among the agents for gradient estimation. Yet, in this article, we propose a distributed active perception strategy that enables swarms of various sizes and graph structures to perform source seeking and level curve tracking without the need to explicitly estimate the field gradient or explicitly share measurements. The proposed method utilizes a consensus-like principal component analysis perception algorithm that does not require explicit communication in order to compute a local body frame. This body frame is used to design a distributed control law where each agent modulates its motion based only on its instantaneous field measurement. Several stability results are obtained within a singular perturbation framework that justifies the convergence and robustness of the strategy. Additionally, efficiency is validated through robots experiments.