  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.
