Sensing-motion co-planning for reconstructing a spatially distributed field using a mobile sensor network

Abstract

We investigate the problem of simultaneous parameter identification and mapping of a spatially distributed field using a mobile sensor network. We first develop a parametrized model that represents the spatially distributed field. Based on the model, a recursive least squares algorithm is developed to achieve online parameter identification. Next, we design a global state observer, which uses the estimated parameters, together with data collected by the mobile sensor network, to real-timely reconstruct the whole spatial-temporal varying field. Since the performance of the parameter identification and map reconstruction algorithms depends on the trajectories of the mobile sensors, we further develop a Lyapunov redesign based online trajectory planning algorithm for the mobile sensor network so that the mobile sensors can use local real-time information to guide them to move along information-rich paths that can improve the performance of the parameter identification and map construction. Lastly, a cooperative filtering scheme is developed to provide the state estimates of the spatially distributed field, which enables the recursive least squares method. To test the proposed algorithms in realistic scenarios, we first build a CO2 diffusion field in a lab and construct a sensor network to measure the field concentration over time. We then validate the algorithms in the reconstructed CO 2 field in simulation. Simulation results demonstrate the efficiency of the proposed method.