Squared error distortion metrics for motion planning in robotic sensor networks

We examine the problem of planning the trajectory of a robotic vehicle to gather data from a deployment of stationary sensors monitoring a set of dynamic source signals. The robotic vehicle and the sensors are equipped with wireless modems (e.g., radio in terrestrial environments or acoustic in underwater environments), which provide noisy communication across limited distances. In such scenarios, the robotic vehicle can improve its efficiency by planning an informed data gathering trajectory. We propose a novel performance metric for data gathering in robotic sensor networks based on the concept of squared error distortion. We analyze the formal properties of the distortion function, and we propose a sampling-based motion planning algorithm for optimizing data gathering tours for minimal distortion. The proposed algorithms are compared in simulation, and the results show that distortion metrics provide substantial improvements in data gathering efficiency.