Fast and Scalable Signal Inference for Active Robotic Source Seeking

TL;DR

This paper introduces a scalable factor graph model for active source seeking robots that efficiently estimates signals and accounts for obstacles, improving planning and performance in complex environments.

Contribution

The paper proposes a global and local factor graph model that scales to many measurements and represents obstacles, enhancing active source seeking capabilities.

Findings

Outperforms baseline methods in simulations

Effective in real robot experiments

Handles large measurement datasets efficiently

Abstract

In active source seeking, a robot takes repeated measurements in order to locate a signal source in a cluttered and unknown environment. A key component of an active source seeking robot planner is a model that can produce estimates of the signal at unknown locations with uncertainty quantification. This model allows the robot to plan for future measurements in the environment. Traditionally, this model has been in the form of a Gaussian process, which has difficulty scaling and cannot represent obstacles. %In this work, We propose a global and local factor graph model for active source seeking, which allows the model to scale to a large number of measurements and represent unknown obstacles in the environment. We combine this model with extensions to a highly scalable planner to form a system for large-scale active source seeking. We demonstrate that our approach outperforms baseline methods in both simulated and real robot experiments.