Autonomous underwater vehicles often perform surveys that capture multiple views of targets in order to provide more information for human operators or automatic target recognition algorithms. In this work, we address the problem of choosing the most informative views that minimize survey time while maximizing classifier accuracy. We introduce a novel active perception framework for multi-view adaptive surveying and reacquisition using side scan sonar imagery. Our framework addresses this challenge by using a graph formulation for the adaptive survey task. We then use Graph Neural Networks (GNNs) to both classify acquired sonar views and to choose the next best view based on the collected data. We evaluate our method using simulated surveys in a high-fidelity side scan sonar simulator. Our results demonstrate that our approach is able to surpass the state-of-the-art in classification accuracy and survey efficiency. This framework is a promising approach for more efficient autonomous missions involving side scan sonar, such as underwater exploration, marine archaeology, and environmental monitoring.

As the future of autonomous underwater vehicle (AUV) deployments tends to multi-vehicle systems, new approaches in coordination and control are needed. In this work, we consider the problem of simultaneous survey and inspection where one vehicle dynamically discovers objects while another vehicle must inspect as many of the objects as possible over the course of the mission. This requires a fully autonomous inspection vehicle, and to this end, we present a planning approach which couples sampling-based motion planning with timed roadmap constraints as well as a real-time execution framework. The methods presented address the underlying challenges that arise during simultaneous survey and inspection using AUVs, namely those of communication constraints, safety of navigation constraints, and dynamically discovered tasks. Additionally, we present field results for the simultaneous survey and inspection mission using teamed AUVs.

We provide theoretical bounds on the worst case performance of the greedy algorithm in seeking to maximize a normalized, monotone, but not necessarily submodular ob-jective function under a simple partition matroid constraint. We also provide worst case bounds on the performance of the greedy algorithm in the case that limited information is available at each planning step. We specifically consider limited information as a result of unreliable communications during distributed execution of the greedy algorithm. We utilize notions of curvature for normalized, monotone set functions to develop the bounds provided in this work. To demonstrate the value of the bounds provided in this work, we analyze a variant of the benefit of search objective function and show, using real-world data collected by an autonomous underwater vehicle, that theoretical approximation guarantees are achieved despite non-submodularity of the objective function.

We present a multi-agent approach to receding horizon path planning that utilizes terminal costs. We show that the value of the receding horizon paths produced using the proposed methods have a guaranteed lower bound that can be determined using any readily-available, naive solution. We present a modified sequentially allocated optimal path planner with terminal costs that is guaranteed to satisfy the assumptions required to provide a guaranteed lower bound. We utilize a slightly modified version of the Decentralized Monte Carlo Tree Search algorithm to solve for near-optimal paths within a short planning horizon with an appended terminal cost to demonstrate the flexibility of the proposed method. We compare these receding horizon methods that incorporate a terminal cost to related receding horizon methods that do not incorporate a terminal cost. Our approach is developed specifically for multiple agents engaged in search, but can be easily adapted for other information gathering applications.