In this paper, we introduce a novel algorithm for incorporating uncertainty into lookahead planning. Our algorithm searches through connected graphs with uncertain edge costs represented by known probability distributions. As a robot moves through the graph, the true edge costs of adjacent edges are revealed to the planner prior to...
The workshop was funded by the National Science Foundation (NSF), OCE Division of Ocean Sciences (Award # 1817257). This report summarizes the key findings, outcomes, and recommendations of the workshop and serves as a draft of the comprehensive roadmap.
This work proposes a technique for distributed multi-robot exploration that leverages novel methods of map inference. The inference technique uses observed map structure to infer unobserved map features. The team then coordinates to explore both the inferred and observed portions of the map. Individual robots select exploration poses by accounting...
Underwater monitoring and manipulation with autonomous underwater vehicles (AUVs) are active avenues of research in the Field Robotics Community. The purpose of this document is to briefly summarize some of the more promising research applications as well as provide information from four companies working in the area of marine renewable...
Operating autonomous underwater vehicles (AUVs) near shore is challenging—heavy shipping traffic and other hazards threaten AUV safety at the surface, and strong ocean currents impede navigation when underwater. Predictive models of ocean currents have been shown to improve navigation accuracy, but these forecasts are typically noisy, making it challenging to...
This paper presents novel data fusion methods
that enable teams of vehicles to perform target search tasks
without guaranteed communication. Techniques are introduced
for merging estimates of a target’s position from vehicles that
regain contact after long periods of time, and a fully distributed
team planning algorithm is proposed that...
We propose three sampling-based motion planning algorithms for generating informative mobile robot trajectories. The goal is to find a trajectory that maximizes an information quality metric (e.g. variance reduction, information gain, or mutual information) and also falls within a pre-specified budget constraint (e.g. fuel, energy, or time). Prior algorithms have...