1 research outputs found
Asynchronous Multi Agent Active Search
Active search refers to the problem of efficiently locating targets in an
unknown environment by actively making data-collection decisions, and has many
applications including detecting gas leaks, radiation sources or human
survivors of disasters using aerial and/or ground robots (agents). Existing
active search methods are in general only amenable to a single agent, or if
they extend to multi agent they require a central control system to coordinate
the actions of all agents. However, such control systems are often impractical
in robotics applications. In this paper, we propose two distinct active search
algorithms called SPATS (Sparse Parallel Asynchronous Thompson Sampling) and
LATSI (LAplace Thompson Sampling with Information gain) that allow for multiple
agents to independently make data-collection decisions without a central
coordinator. Throughout we consider that targets are sparsely located around
the environment in keeping with compressive sensing assumptions and its
applicability in real world scenarios. Additionally, while most common search
algorithms assume that agents can sense the entire environment (e.g.
compressive sensing) or sense point-wise (e.g. Bayesian Optimization) at all
times, we make a realistic assumption that each agent can only sense a
contiguous region of space at a time. We provide simulation results as well as
theoretical analysis to demonstrate the efficacy of our proposed algorithms.Comment: Preprint under revie