Optimizing Start Locations in Ergodic Search for Disaster Response

TL;DR

This paper presents a method to optimize the initial deployment locations of heterogeneous robotic teams in disaster response, significantly improving search coverage by integrating start location selection into ergodic search planning.

Contribution

It introduces a joint optimization framework that incorporates start location selection into ergodic search for heterogeneous robots, addressing a previously overlooked problem.

Findings

Achieved up to 36% improvement in coverage performance.

Demonstrated effectiveness on both synthetic and real-world data.

Enhanced search efficiency for heterogeneous robotic teams.

Abstract

In disaster response scenarios, deploying robotic teams effectively is crucial for improving situational awareness and enhancing search and rescue operations. The use of robots in search and rescue has been studied but the question of where to start robot deployments has not been addressed. This work addresses the problem of optimally selecting starting locations for robots with heterogeneous capabilities by formulating a joint optimization problem. To determine start locations, this work adds a constraint to the ergodic optimization framework whose minimum assigns robots to start locations. This becomes a little more challenging when the robots are heterogeneous (equipped with different sensing and motion modalities) because not all robots start at the same location, and a more complex adaptation of the aforementioned constraint is applied. Our method assumes access to potential starting locations, which can be obtained from expert knowledge or aerial imagery. We experimentally evaluate the efficacy of our joint optimization approach by comparing it to baseline methods that use fixed starting locations for all robots. Our experimental results show significant gains in coverage performance, with average improvements of 35.98% on synthetic data and 31.91% on real-world data for homogeneous and heterogeneous teams, in terms of the ergodic metric.