Adaptive Sampling With Multiple Mobile Robots

When a scalar field, such as temperature, is to be estimated from sensor readings corrupted by noise, the estimation accuracy can be improved by judiciously controlling the locations where the sensor readings (samples) are taken. In this paper, we solve the following problem: given a set of static sensors and a group of mobile robots equipped with the same sensors, how to determine the data collecting paths for the mobile robots so that the reconstruction error of the scalar field is minimized. In our scheme, the static sensors are used to provide an initial estimate and the mobile robots refine the estimate by taking additional samples at critical locations. Unfortunately, it is computationally expensive to search for the best set of paths that minimizes the field estimation errors and hence the field reconstruction errors as well). We propose a heuristic to find ‘good’ paths for the robots. Our approach first partitions the sensing field into equal gain subareas and then we use a single robot planning algorithm to generate a path for each robot separately. The properties of this approach are studied in simulation. Our approach also implicitly solves a multi-robot coordination/task allocation problem, where the robots are homogeneous and the size of task set might be large.