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
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
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
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
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
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
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.
Nodes
Edges
Occupancy grid
Path
USARSim
•
High-fidelity simulator
based on Unreal
Tournament
•
Real-time physics with
physics card
•
Open source, freely
available
•
Maintained by NIST
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
Thumbnails for
each robot
Enlarged video and teleop
Map and Victim Marking
More Area Explored
(p < 0.001)
Auto
Manual
(Manual condition: 4.26 robots ignored)
More Victims Found
(p = 0.003)
Auto
Manual
(Same victims/area in both groups)
Decreased Marking Error
p = 0.002
Auto
Manual
Same Workload
Auto
Manual
Conclusions
•
Autonomous path planning a useful way of reducing
operator load when environment allows it
•
•
Operator’s time taken up with other activities
•
•
Benefits (faster planning, handling more robots)
outweigh costs (loss of situation awareness, lack of
human insight)
Not clear they fully exploit all robots
Future focus on data presentation/visualization