Teams of cooperating autonomous underwater vehicles (AUVs) rely on acoustic communication for coordination, yet this communication medium is constrained by limited range, multi-path effects, and low bandwidth. One way to address the uncertainty associated with acoustic communication is to learn the communication environment in real-time. We address the challenge of a team of robots building a map of the probability of communication success from one location to another in real-time. This is a decentralized classification problem – communication events are either successful or unsuccessful – where AUVs share a subset of their communication measurements to build the map. The main contribution of this work is a rigorously derived data sharing policy that selects measurements to be shared among AUVs. We experimentally validate our proposed sharing policy using real acoustic communication data collected from teams of Virginia Tech 690 AUVs, demonstrating its effectiveness in underwater environments.

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.

We present the results of experiments performed using a team of small autonomous underwater vehicles (AUVs) to determine the location of an isobath. The primary contributions of this work are (1) the development of a novel objective function for level set estimation that utilizes a rigorous assessment of uncertainty, and (2) a description of the practical challenges and corresponding solutions needed to implement our approach in the field using a team of AUVs. We combine path planning techniques and an approach to decentralization from prior work that yields theoretical performance guarantees. Experimentation with a team of AUVs provides empirical evidence that the desirable performance guarantees can be preserved in practice even in the presence of limitations that commonly arise in underwater robotics, including slow and intermittent acoustic communications and limited computational resources.

Cooperating autonomous underwater vehicles (AUVs) often rely on acoustic communication to coordinate their actions effectively. However, the reliability of underwater acoustic communication decreases as the communication range between vehicles increases. Consequently, teams of cooperating AUVs typically make conservative assumptions about the maximum range at which they can communicate reliably. To address this limitation, we propose a novel approach that involves learning a map representing the probability of successful communication based on the locations of the transmitting and receiving vehicles. This probabilistic communication map accounts for factors such as the range between vehicles, environmental noise, and multi-path effects at a given location. In pursuit of this goal, we investigate the application of Gaussian process binary classification to generate the desired communication map. We specialize existing results to this specific binary classification problem and explore methods to incorporate uncertainty in vehicle location into the mapping process. Furthermore, we compare the prediction performance of the probability communication map generated using binary classification with that of a signal-to-noise ratio (SNR) communication map generated using Gaussian process regression. Our approach is experimentally validated using communication and navigation data collected during trials with a pair of Virginia Tech 690 AUVs.

We present the results of experiments performed using a small autonomous underwater vehicle to determine the location of an isobath within a bounded area. The primary contribution of this work is to implement and integrate several recent developments real-time planning for environmental map-ping, and to demonstrate their utility in a challenging practical example. We model the bathymetry within the operational area using a Gaussian process and propose a reward function that represents the task of mapping a desired isobath. As is common in applications where plans must be continually updated based on real-time sensor measurements, we adopt a receding horizon framework where the vehicle continually computes near-optimal paths. The sequence of paths does not, in general, inherit the optimality properties of each individual path. Our real-time planning implementation incorporates recent results that lead to performance guarantees for receding-horizon planning.

We consider the coordinated escort problem, where a decentralised team of supporting robots implicitly assist the mission of higher-value principal robots. The defining challenge is how to evaluate the effect of supporting robots' actions on the principal robots' mission. To capture this effect, we define two novel auxiliary reward functions for supporting robots called satisfaction improvement and satisfaction entropy, which computes the improvement in probability of mission success, or the uncertainty thereof. Given these reward functions, we coordinate the entire team of principal and supporting robots using decentralised cross entropy method (Dec-CEM), a new extension of CEM to multi-agent systems based on the product distribution approximation. In a simulated object avoidance scenario, our planning framework demonstrates up to two-fold improvement in task satisfaction against conventional decoupled information gathering. The significance of our results is to introduce a new family of algorithmic problems that will enable important new practical applications of heterogeneous multi-robot systems.

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.

In marine robotics, data-collection operations often require an autonomous underwater vehicle (AUV) to collaborate with an unmanned surface vehicle (USV). The mission for the AUV is to reach many goal locations, avoid obstacles and unsafe areas, and maintain communication with the USV. The goals, however, are not known a priori, but are dynamically discovered by the USV as it moves along a predefined path. The USV communicates the discovered goals to the AUV along with rewards for reaching each goal to incentivize the AUV to increase the sum of the rewards when obstacles, time, and communication constraints make it impossible to reach all the goals. We develop a framework comprised of an execution module and a multi-layered planner to enable the AUV to avoid collisions, maintain communication with the USV, and increase the sum of the rewards by reaching many of the discovered goals. The execution module enables the AUV to follow the planned motions, invoking the planner when new goals are discovered. To facilitate navigation, the planner constructs a 3D roadmap that captures the connectivity of the environment by sampling and connecting waypoints in the free space. The high-level planning layer is based on informed discrete search to find roadmap paths that satisfy the communication constraints and increase the sum of the goal rewards. The low-level layer uses sampling-based motion planning to expand a tree of feasible motions along these roadmap paths. The layers interact to update the planned motions as new goals are discovered. Experiments using 3D environments and an increasing number of goals demonstrate the efficiency of the approach to solve dynamic multi-goal motion-planning problems with communication constraints.Note to Practitioners—This paper is motivated by the problem of at-sea data collection using unmanned vehicles where inter-vehicle communications constraints must be maintained. This is a challenging problem that has generally required significant human monitoring and intervention due to the communication constraints and planning challenges. In this paper, we leverage recent advances in underwater communications and focus on new problems that arise in the planning space, specifically, how we generate trajectories for an AUV that satisfies communication range constraints while still performing an underlying task. We show that it is possible to plan for an AUV in real-time while considering these constraints. This paper suggests that our approach can support these complex at-sea missions. Future research will focus on field deployments and introducing more uncertainty into the underlying sensor models during planning.

Planning the motion of bearings-only sensors is critical for enabling accurate tracking of the positions of moving targets. In this paper, we demonstrate planning the observer's motion over horizons greater than one step for estimating an unknown and varying number of indistinguishable, maneuvering targets of interest using a probability hypothesis density (PHD) filter, with a Rériyi divergence reward for selecting actions. We describe approximations to make this approach computationally feasible, and we propose using Monte Carlo tree search (MCTS) to further reduce the cost. Finally, we present simulation results showing that longer planning horizons reduce the error in the estimates and that MCTS can reduce the cost of planning without sacrificing the quality of the estimates.

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.

Ocean Worlds represent one of the best chances for extra-terrestrial life in our solar system. A new mission concept must be developed to explore these oceans. This mission would require traversing the 10s of km thick icy shell and releasing a submersible into the ocean below. During the transit of the icy shell and the exploration of the ocean, the vehicle(s) would be out of contact with Earth for weeks or potentially months at a time. During this time the vehicle must have sufficient autonomy to locate and study scientific targets of interest. One such target of interest is hydrothermal venting. We have previously developed an autonomous nested search method to locate and investigate sources of hydrothermal venting by locating local maxima in hydrothermal vent emissions. In this work we demonstrate this approach on board an OceanServer Iver2 AUV in Chesapeake Bay, MD using simulated sensor data from a hydrothermal plume model. This represents the first step towards the deployment of this approach in conditions analogous to those that we might expect on an Ocean World.

The computational difficulty of planning search paths that seek to maximize a general deterministic value function increases dramatically as desired path lengths increase. Mobile search agents with limited computational resources often utilize receding horizon methods to address the path planning problem. Unfortunately, receding horizon planners may perform poorly due to myopic planning horizons. We provide methods of incorporating terminal costs in the construction of receding horizon paths that provide a theoretical lower bound on the performance of the search paths produced. The results presented in this paper are of particular value in subsea search applications. We present results from simulated subsea search missions that use real-world data acquired by an autonomous underwater vehicle during a subsea survey of Boston Harbor.

We present a novel method of using terminal costs in the construction of a receding horizon search path. We prove that the proposed method of constructing search paths provides a theoretical lower bound on the performance of the search path. Our result can be interpreted as ensuring that the receding horizon path performs no worse in expectation than a given sub-optimal search path. This result is especially practical for subsea applications where, due to use of side-scan sonar in search applications, search paths typically consist of parallel straight lines. Thus for subsea search applications, our approach ensures that expected performance is no worse than the usual subsea search path, and it might be much better. We demonstrate the efficacy of the proposed method by planning search paths in simulation using real-world data that was acquired by an autonomous underwater vehicle during a subsea survey of Boston Harbor.

We present an anytime Monte Carlo tree search (MCTS) algorithm to generate real-time, near-optimal search paths in large subsea environments. The MCTS planner continuously builds a tree of the search space until either the allowed time per move is reached or the budget constraint for the search mission is met. In order to improve the performance of the MCTS planner, we propose a novel heuristic action selection policy to determine the value of a leaf node. The proposed heuristic is tailored to problems where making a turn incurs a higher cost than moving straight, such as the case on autonomous underwater vehicles. Through extensive simulations, we show that our heuristic yields a significant performance improvement over a lawnmover path planner - a commonly employed approach in subsea search applications - and over a simple MCTS planner where actions are selected uniformly at random. In our numerical illustrations, we use a real data set abstracted from sonar measurements acquired from the Boston Harbor.

We address the challenge of computing search paths in real-time for subsea applications where the goal is to locate an unknown number of targets on the seafloor. Our approach maximizes a formal definition of search effectiveness given finite search effort. We account for false positive measurements and variation in the performance of the search sensor due to geographic variation of the seafloor. We compare near-optimal search paths that can be computed in real-time with optimal search paths for which real-time computation is infeasible. We show how sonar data acquired for locating targets at a specific location can also be used to characterize the performance of the search sonar at that location. Our approach is illustrated with numerical experiments where search paths are planned using sonar data previously acquired from Boston Harbor.

This paper presents an efficient approach for planning collision-free, dynamically-feasible, and low-cost motion trajectories that satisfy task specifications given as formulas in a temporal logic, namely Syntactically Co-Safe Linear Temporal Logic (LTL). The planner is geared toward high-dimensional mobile robots with nonlinear dynamics operating in complex environments. The planner incorporates physics-based engines for accurate simulations of rigid-body dynamics. To obtain computational efficiency and generate low-cost solutions, the planner first imposes a discrete abstraction by combining an automaton representing the LTL formula with a workspace decomposition. The planner then uses the discrete abstraction to induce a partition of a sampling-based motion tree being expanded in the state space into equivalence classes. Each equivalence class captures the progress made toward achieving the temporal logic specifications. Heuristics defined over the abstraction are used to estimate the feasibility of expanding the motion tree from these equivalence classes and reaching an accepting automaton state. Costs are adjusted based on progress made, giving the planner the flexibility to make rapid progress while discovering new ways to expand the search. Comparisons to related work show statistically significant computational speedups and reduced solution costs.