Strategic Control of Proximity Relationships in Heterogeneous Search and Rescue Teams

TL;DR

This paper introduces a mixed integer programming approach for strategic mission planning in heterogeneous search and rescue teams, enabling flexible control of agent proximity to optimize coverage and cooperation.

Contribution

It presents a novel mathematical formulation with soft constraints for controlling spatial relationships among diverse agents in rescue missions.

Findings

Effective control of agent proximity improves coverage

Flexible planning enhances cooperation among heterogeneous agents

Simulation demonstrates applicability to real-world scenarios

Abstract

In the context of search and rescue, we consider the problem of mission planning for heterogeneous teams that can include human, robotic, and animal agents. The problem is tackled using a mixed integer mathematical programming formulation that jointly determines the path and the activity scheduling of each agent in the team. Based on the mathematical formulation, we propose the use of soft constraints and penalties that allow the flexible strategic control of spatio-temporal relations among the search trajectories of the agents. In this way, we can enable the mission planner to obtain solutions that maximize the area coverage and, at the same time, control the spatial proximity among the agents (e.g., to minimize mutual task interference, or to promote local cooperation and data sharing). Through simulation experiments, we show the application of the strategic framework considering a number of scenarios of interest for real-world search and rescue missions.