Strategic Control of Proximity Relationships in Heterogeneous Search and Rescue Teams
This addresses mission planning challenges for heterogeneous teams in search and rescue, though it appears incremental as it builds on existing mathematical programming methods with added flexibility.
The paper tackles the problem of mission planning for heterogeneous search and rescue teams by developing a mixed integer programming formulation with soft constraints to control spatio-temporal relationships among agents, resulting in solutions that maximize area coverage while managing proximity for cooperation or interference reduction.
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.