Adaptive Sampling by Using Mobile Robots and a Sensor Network
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. Following is the problem we are solving: 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). In the case of single mobile robot, we propose an Approximate Breadth First Search to find a 'good' path for the robot. We have validated the path planning algorithm both in simulation and with the NAMOS system. In the case of multiple mobile robots, our approach first partitions the sensing field into equal gain subareas and then we use the single robot planning algorithm to generate a path for each robot separately. The properties of this approach are studied in simulation.