Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization

TL;DR

This paper extends ergodic coverage algorithms for robotic trajectory planning to constrained environments, incorporating obstacle avoidance, sensor footprints, and multi-robot coordination for improved search and surveillance tasks.

Contribution

It introduces a novel formulation that handles environmental constraints and sensor footprints, enabling effective multi-robot ergodic coverage in complex domains.

Findings

Successfully incorporates obstacle avoidance into ergodic coverage

Enables multi-robot coordination with different sensors

Demonstrates improved coverage in constrained environments

Abstract

In search and surveillance applications in robotics, it is intuitive to spatially distribute robot trajectories with respect to the probability of locating targets in the domain. Ergodic coverage is one such approach to trajectory planning in which a robot is directed such that the percentage of time spent in a region is in proportion to the probability of locating targets in that region. In this work, we extend the ergodic coverage algorithm to robots operating in constrained environments and present a formulation that can capture sensor footprint and avoid obstacles and restricted areas in the domain. We demonstrate that our formulation easily extends to coordination of multiple robots equipped with different sensing capabilities to perform ergodic coverage of a domain.