# Ergodic Coverage In Constrained Environments Using Stochastic Trajectory Optimization

arXiv: https://arxiv.org/abs/1707.04294 · HTML: https://mlsanity.com/paper/1707.04294/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jul 2017.

## Built on by (most cited first, 4 of 4)

- [Learning from demonstration using products of experts: applications to manipulation and task prioritization](https://mlsanity.com/paper/2010.03505/) (2020)
- [Volumetric Ergodic Control](https://mlsanity.com/paper/2511.11533/) (2025)
- [GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search](https://mlsanity.com/paper/2304.02075/) (2023)
- [TRACE: Ergodic Trajectory Optimization for Active Scene Reconstruction](https://mlsanity.com/paper/2608.02304/) (2026)

## Abstract (arXiv)

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.