Intermittent Information-Driven Search for Underwater Targets

TL;DR

This paper introduces an intermittent, information-driven search strategy for autonomous underwater target detection, combining fast ballistic movements with slow sensing phases to maximize information gain and improve search efficiency.

Contribution

It develops a novel intermittent search method that integrates ballistic and sensing phases, optimizing movement decisions based on expected information gain for underwater target detection.

Findings

Efficient search strategy demonstrated with autonomous amphibious drone.

Improved detection performance over traditional methods.

Effective in complex underwater environments.

Abstract

The problem is area-restricted search for targets using an autonomous mobile sensing platform. Detection is imperfect: the probability of detection depends on the range to the target, while the probability of false detections is non-zero. The paper develops an intermittent information-driven search strategy, which combines fast and non-receptive displacement phase (ballistic phase) with a slow displacement sensing phase. Decisions where to move next, both in the ballistic phase and the slow displacement phase, are information-driven: they maximise the expected information gain. The paper demonstrates the efficiency of the proposed strategy in the context of a search for underwater targets: the searcher is an autonomous amphibious drone which can both fly and land or takeoff from the sea surface.