Multi-Objective Sparse Sensing with Ergodic Optimization

TL;DR

This paper introduces a multi-objective sparse sensing approach using ergodic optimization to efficiently guide multi-agent search tasks with limited sensor usage, maintaining coverage despite sampling constraints.

Contribution

It proposes the MO-SS-E metric for planning sensor measurements in multi-objective search problems, balancing information gathering and sensor costs.

Findings

Maintains coverage performance with fewer samples.

Effective for both homogeneous and heterogeneous multi-agent teams.

Demonstrates applicability in various multi-agent scenarios.

Abstract

We consider a search problem where a robot has one or more types of sensors, each suited to detecting different types of targets or target information. Often, information in the form of a distribution of possible target locations, or locations of interest, may be available to guide the search. When multiple types of information exist, then a distribution for each type of information must also exist, thereby making the search problem that uses these distributions to guide the search a multi-objective one. In this paper, we consider a multi-objective search problem when the cost to use a sensor is limited. To this end, we leverage the ergodic metric, which drives agents to spend time in regions proportional to the expected amount of information there. We define the multi-objective sparse sensing ergodic (MO-SS-E) metric in order to optimize when and where each sensor measurement should be taken while planning trajectories that balance the multiple objectives. We observe that our approach maintains coverage performance as the number of samples taken considerably degrades. Further empirical results on different multi-agent problem setups demonstrate the applicability of our approach for both homogeneous and heterogeneous multi-agent teams.