2021/03/18 by Tahiya Salam, Salam, Tahiya, M. Ani Hsieh +1
Computer Science · Decision Sciences · Engineering · #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Modular Robots and Swarm Intelligence #Robotics (cs.RO) #Scientific Computing and Data Management
paper · pdf · doi:10.48550/arxiv.2103.10383
openalex publication_date 2021/03/18 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
This paper presents a framework to enable a team of heterogeneous mobile robots to model and sense a multiscale system. We propose a coupled strategy, where robots of one type collect high-fidelity measurements at a slow time scale and robots of another type collect low-fidelity measurements at a fast time scale, for the purpose of fusing measurements together. The multiscale measurements are fused to create a model of a complex, nonlinear spatiotemporal process. The model helps determine optimal sensing locations and predict the evolution of the process. Key contributions are: i) consolidation of multiple types of data into one cohesive model, ii) fast determination of optimal sensing locations for mobile robots, and iii) adaptation of models online for various monitoring scenarios. We illustrate the proposed framework by modeling and predicting the evolution of an artificial plasma cloud. We test our approach using physical marine robots adaptively sampling a process in a water tank.