Distributed coordination and data fusion for underwater search

Abstract

Abstract-This paper presents coordination and data fusion methods for teams of vehicles performing target search tasks without guaranteed communication. A fully distributed team planning algorithm is proposed that utilizes limited shared information as it becomes available, and data fusion techniques are introduced for merging estimates of the target's position from vehicles that regain contact after long periods of time. The proposed data fusion techniques are shown to avoid overcounting information, which ensures that combining data from different vehicles will not decrease the performance of the search. Motivated by the underwater search domain, a realistic underwater acoustic communication channel is used to determine the probability of successful data transfer between two locations. The channel model is integrated into a simulation of multiple autonomous vehicles in both open ocean and harbor search scenarios. The simulated experiments demonstrate that distributed coordination with limited communication significantly improves team performance versus prior techniques that continually maintain connectivity.

Underwater Data Collection Using Robotic Sensor Networks

“…The solid curve represents the log-distance model (5), whose parameters g 0 and k 0 were obtained by first-order polynomial fitting. 3 We emphasize again that the model parameters will in general depend on the operational conditions [28], i.e. that the values indicated in the figure are representative of the 8-12 kHz acoustic band and transmission distances on the order of several hundreds of meters.…”

“…In the present paper, we utilize a probabilistic acoustic communication model that degrades with distance. Such models have been used to optimize control strategies for both underwater station-keeping [15] and underwater search [3].…”

Autonomous data collection from underwater sensor networks using acoustic communication

“…We utilize a probabilistic underwater acoustic communication model developed in prior work [2], [14]. This model accounts for noise factors in the environment, such as wind and shipping activity, as well as the distance and frequency dependent attenuation of the signal in the water medium.…”

“…Ultimately, the SNR is a function of the transmitted power, frequency of transmission, wind speed, shipping factor, the distance and the bandwidth used (see reference [2] for more detail). For a packet with Q symbols, the probability of transmission error can then be computed as…”

“…Examples include algal blooms [1], seismic activity, and intrusion of enemy submarines [2]. In underwater monitoring scenarios, many standard methods of communication are no longer feasible (e.g., WiFi, cellular, satellite).…”