Cooperative information-driven source search and estimation for multiple agents

• Published in: Information fusion Vol. 54; pp. 72 - 84
• Main Authors: Park, Minkyu, Oh, Hyondong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.02.2020
• Subjects: Autonomous search; Bayesian inference; Dispersion modelling; Multi-sensor network; Sensor management; Sequential Monte Carlo; Sensor management; Sequential Monte Carlo; Bayesian inference; Autonomous search; Dispersion modelling; Multi-sensor network
• ISSN: 1566-2535; 1872-6305
• DOI: 10.1016/j.inffus.2019.07.007

•Different levels of coordination methods for autonomous source search are proposed.•Infotaxis is extended for multiple mobile sensors in a decentralized manner.•The proposed cooperative Infotaxis shows faster search time than existing methods. This paper proposes different levels of coordination methods for information-driven source search and estimation in a stochastic and turbulent atmospheric dispersion event. Multiple mobile sensors are assumed to communicate one another over a wireless network and share the minimal data (e.g. current position, sensor measurements, and control decision) to reduce the communication burden. The particle filter, sampling-based sequential Monte Carlo method, suitable for highly non-linear and non-Gaussian systems and the measurement sensor fusion method are used for the estimation of the source position and release rate. For efficient autonomous search, three coordination methods are introduced based on the Infotaxis algorithm: non-coordination, passive coordination, and negotiated coordination. To demonstrate the benefit of the proposed cooperative multi-mobile sensor system, extensive simulations on simulated and real experimental data are performed for different levels of coordination methods and the number of mobile sensors.