Underwater Data Collection Using Robotic Sensor Networks

We examine the problem of utilizing an autonomous underwater vehicle (AUV) to collect data from an underwater sensor network. The sensors in the network are equipped with acoustic modems that provide noisy, range-limited communication. The AUV must plan a path that maximizes the information collected while minimizing travel time or fuel expenditure. We propose AUV path planning methods that extend algorithms for variants of the Traveling Salesperson Problem (TSP). While executing a path, the AUV can improve performance by communicating with multiple nodes in the network at once. Such multi-node communication requires a scheduling protocol that is robust to channel variations and interference. To this end, we examine two multiple access protocols for the underwater data collection scenario, one based on deterministic access and another based on random access. We compare the proposed algorithms to baseline strategies through simulated experiments that utilize models derived from experimental test data. Our results demonstrate that properly designed communication models and scheduling protocols are essential for choosing the appropriate path planning algorithms for data collection.United States. Office of Naval Research (ONR N00014-09-1-0700)United States. Office of Naval Research (ONR N00014-07-1-00738)National Science Foundation (U.S.) (NSF 0831728)National Science Foundation (U.S.) (NSF CCR-0120778)National Science Foundation (U.S.) (NSF CNS-1035866

Coactive learning for multi-robot search and coverage

We investigate a search and coverage planning problem, where an area of interest has to be explored by a number of vehicles, given a fixed time budget. A good coverage plan has a low probability of a target remaining unobserved. We introduce a formal problem statement, suggest a greedy algorithm to solve the problem, and show experimental results on a number of simulated coverage problems. Our work offers three main contributions. First, we propose an offline planning algorithm that, given some prior knowledge about the target probability in an environment, surveys the area to find the targets as fast as possible while minimizing the energy used. The planning algorithm plans targets to visit and paths to follow for multiple robots, which may have different performance characteristics such as speed, power, and sensor quality. Our second main contribution is to integrate our planning algorithm in the framework of coactive learning, where the system learns the cost function of an in situ human expert, who edits and improves the solutions generated by the system. Our third contribution is an empirical evaluation of the system and a comparison to a state-of-the-art system with provable performance gaurantees on a simulator. The results show that our system yields comparable performance to the state-of-the-art system while respecting hard budget constraints and running orders of magnitude faster.Keywords: search and coverage planning problem, Multi-robot Search and Coverage, Coactive LearningKeywords: search and coverage planning problem, Multi-robot Search and Coverage, Coactive Learnin