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 UniversitySchool 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

robotEnlarged video and teleop

Map and Victim Marking

More Area Explored

Auto Manual

(p < 0.001)

(Manual condition: 4.26 robots ignored)

More Victims Found

Auto Manual

(p = 0.003)

(Same victims/area in both groups)

Decreased Marking Error

Auto Manual

p = 0.002

Same Workload

Auto Manual

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