Initial Condition 1 with hard difficulty object locations. The A1 is given the red global waypoints and the HSR is given green global waypoints and global paths are shown as the green line connecting each colored set of waypoints.  Neither object is found due to the global path plans not visually inspecting the suitcases, hidden from plain sight behind desk and chairs.

The figure title represents {Planner Setting} - {Initial Condition} - {Obj1/Obj2 Difficulty}. It displays robot trajectories overlaid on the static map during execution of the trial. Target object locations are labeled by stars.  Trajectory plots exist for all experimental trials and can be found in the Google Drive.

The A1 successfully detects the suitcase along its path, however the HSR fails in this trial due to a navigation (bumps into object). Due to the fact that Heuristic Visual CPP  requires robots to traverse longer distances over longer time periods, it is more likely that these sort of autonomy failures occur. 

While the CPP methods fail to find both objects, our proposed method robustly detects the hard difficulty object locations. Each object is found with the help of curiosity. The black arrows represent the next goal point for each agent, as sent from the Waypoint Manager. While following the global path plans, each robot takes a priority waypoint that deviates from the global path plans.