15 accepted papers
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
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 assessme…
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 typic…
We consider the problem of decentralized multiagent environmental learning through maximizing the joint information gain among a team of agents. Inspired by subsea applications where bandwidth is severely limited, we explicitly consider the challenge of restricted communication between agents. The e…
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, an…
We seek to rigorously evaluate the benefit of using a few beams rather than a single beam for a low-cost obstacle avoidance sonar for small AUVs. For a small low-cost AUV, the complexity, cost, and volume required for a multi-beam forward looking sonar are prohibitive. In contrast, a single-beam sys…
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 algori…
In this paper, we propose two decentralized approximate algorithms for nested Gaussian processes in multi-robot systems. The distributed implementation is achieved with iterative and consensus methods that facilitate local computations at the expense of inter-robot communications. Moreover, we propo…
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 mo…
Gaussian processes (GPs) are a well-known nonparametric Bayesian inference technique, but they suffer from scalability problems for large sample sizes, and their performance can degrade for non-stationary or spatially heterogeneous data. In this work, we seek to overcome these issues through (i) emp…
In the context of a multi-agent system that uses a Gaussian process to estimate a spatial field of interest, we propose an approach that enables an agent to reduce the amount of data it shares with other agents. The main idea of the strategy is to rigorously assign a novelty metric to each measureme…
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 pr…
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 miss…
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 recedin…
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…