Theory and experimental results for the multiple aspect coverage problem

We introduce a novel task that arises in underwater search and inspection. In this task, an underwater vehicle must re-acquire and identify clusters of discrete objects. The challenge is to generate an efficient path for the vehicle given a probabilistic description of potential target locations. We propose an algorithm that generates an efficient path and show that the algorithm is superior to standard approaches. Hardware implementation and field experiments using this algorithm are presented using a REMUS-100 autonomous underwater vehicle on simulated targets. The use of this algorithm is compared to two baseline methods of performing a multiple-aspect coverage mission. The field experiments imply that the proposed algorithm is superior in speed to the baseline methods. ⺠We introduce a novel task in which an AUV must survey clusters of objects. ⺠We propose an algorithm that generates an efficient path. ⺠We show algorithm is superior to standard approaches. ⺠Hardware implementation and field experiments using this algorithm are presented. ⺠Experiments imply that the algorithm is superior in speed to the baseline methods.