1. Towards an Understanding of the Impact of Autonomous Path Planning on Victim Search in USAR Paul Scerri, Prasanna Velagapudi, Katia Sycara, Huadong Wang, Shih-Yi James Chien and Michael Lewis Robotics Institute, Carnegie Mellon University School of Information Sciences, University of Pittsburgh

2. Urban Search and Rescue Focus on Chemical, Biological, Radiological, Nuclear events Use multiple robots to search for victims in dangerous urban disaster environment Complex environment means that humans are required

3. Operator Tasks Operator has several independent tasks Processing vision data (identifying victims/problems) Rescuing stuck or broken robots (trapped under chairs or high centered) Planning exploration Coordinating robots

4. Increasing Robot:Operator Ratio Operators are extremely expensive compared to robots Easier to get more robots than more operators Robots spend much of their time moving slowly between locations Operator time is not efficiently utilized Unpredictably required

5. Autonomous Path Planning Robots are already performing SLAM w/ LIDAR data Allow robots to plan their own paths, to cooperatively explore the environment Path planning is mature, reasonably reliable in some environments Suspect that operators spend a lot of effort thinking about path planning, for little gain

6. Tradeoffs of Autonomy Large amount of operator time saved, corresponding increase in efficiency Robots use abstracted data to decide where to explore, human insight/semantic knowledge might be more efficient Operators may lose some situational awareness if they don’t need to control robots

7. Lattice Planning Straightforward implementation of published algorithms Nodes valued by expected information gain of going to that location Edges valued by probability of traversing safely Thresholded, with bias against paths of other robots Branch and bound search to find path that maximizes information gain Some limits on path length, preference for straightness, etc.

8. NodesEdgesOccupancy grid Path

9. USARSim High-fidelity simulator based on Unreal Tournament Real-time physics with physics card Open source, freely available Maintained by NIST

10. Experiment Design 60 paid subjects in 30 teams of 2 used both designs 24 P3ATs, 25 min., large office environment, find/mark victims Auto: Path planner, with operator able to teleop or waypoint plan Manual: Waypoint planning for each robot, teleop when required

11. Thumbnails for each robot Enlarged video and teleop Map and Victim Marking

13. More Area Explored Auto Manual (p < 0.001) (Manual condition: 4.26 robots ignored)

14. More Victims Found Auto Manual (p = 0.003) (Same victims/area in both groups)

15. Decreased Marking Error Auto Manual p = 0.002

16. Same Workload Auto Manual

17. Conclusions Autonomous path planning a useful way of reducing operator load when environment allows it Benefits (faster planning, handling more robots) outweigh costs (loss of situation awareness, lack of human insight) Operator’s time taken up with other activities Not clear they fully exploit all robots Future focus on data presentation/visualization