1 darpa tmr program collaborative mobile robots for high-risk urban missions second quarterly ipr...
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DARPA TMR Program
Collaborative Mobile Robots forHigh-Risk Urban Missions
Second Quarterly IPR Meeting
January 13, 1999
P. I.s: Leonidas J. Guibas and Jean-Claude Latombe
Computer Science Department
Stanford University
http://underdog.stanford.edu/tmr
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• P.I.s: Profs. Leonidas J. Guibas and Jean-Claude Latombe.• Post-docs:
– Alon Efrat: map building, target finding.– T. M. Murali: map building, target finding.– Rafael Murrieta: target tracking, robot experiments.
• Ph. D. Students:– H. Gonzalez-Banos: map building, target tracking.– Cheng-Yu Lee: target finding in 3D.– David Lin: target finding in 2D.
Research Group
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Research Focus• Gather information in an urban environment.
– Automatic generation of motion strategies.
– Multiple autonomous but coordinated robots.
• Three primary tasks:– Map building: Given no or partial a priori map,
navigate robots in the environment to collect data to form a map.
– Target finding: Sweep the environment with the robots to detect and localise potential targets.
– Target tracking: Move robots to maintain visibility of detected targets.
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Challenges and Issues• Limitations of sensing capabilities:
– Range, incidence angles.
– Trade-off between sensing models and motion planning strategies.
• Errors in sensing and localisation.
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Challenges and Issues• Collaboration between multiple robots:
– Avoiding replication of work.
– Maintaining communication network to share information.
– Relative localisation.
• Collaboration between air and ground robots.
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Experimental Setup
• One Nomad SuperScout: SICK’s time-of-flight range sensor for 2D map building.
• One Nomad 200: triangulating laser sensor for 3D sensing.
• Two Nomad SuperScouts: tracking cameras for target finding and target tracking.
• One Nomad 200: camera for target finding and target tracking, target itself.
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Map Building• Task: Given no or partial a priori map, navigate
robots in a building to collect data to form a map.• Goal: Algorithms for efficient exploration
strategies.– Minimise time to build the map.
– Coordinate multiple robots.
– Take sensing limitations into account.
• Technique: interactions between 2D and 3D.– Build 2D map using next-best view technique.
– Exploit 2D map to decide where to perform complex 3D sensing operations.
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Environment Model• Set of 2D layouts:
– Geometric representation (polygons).
– Also represent uncertainty.
• Layouts include obstacles:– Obstruct motion (glass windows, mines).
– Obstruct visibility.
• Layouts include landmarks for localisation. • Set of partial 3D models (images from selected
points in the 2D maps).
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Sensor Model for Map Building• 2D map building (range finder, stereo camera):
– minimum and maximum range.– maximum incidence angle.– cone of visibility.
• 3D sensing (colour camera):– focal length, depth of field.– cone of visibility.
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Next-Best View Map-Building Algorithm• Identify unexplored portions of the boundary of the map
built so far.
• Travel towards boundary edge with highest rank.– Estimate new information gained by exploring an edge.
– Compute cost of travelling to that edge.
– Rank of an edge is ratio of information and cost.
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Features of Map-Building Algorithm• Makes global decisions.
– Minimises total distance travelled.
• Can exploit a priori information about the environment.
• Scales to multiple robots:– Send robots to edges with high rank that are far apart.
– Different robots explore different portions of environment.
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Target Finding• Task: Sweep the environment with the robots to
detect and localise potential targets.• Goal: Generate reliable motion strategies for the
robots. • Techniques: reliable in spite of recontamination.
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Target Finding in 2D
• Restricted visibility models (cone of vision, minimum/maximum range):– Robot moves with “back to the wall.”
• Communication maintenance:– Robots move while maintaining a network of
communication links.
– Robots protect each other and share information.
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Target Finding in 3D• Observer is aerial (helicopter).• Targets are on the ground.• Obstacles are buildings.• Compute a path for helicopter that sweeps the
buildings.
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Target Tracking• Task: Move robots to maintain visibility of detected
targets.• Goal: On-line techniques to decide how robots should
move to minimise chance of targets moving out of sight.• Technique: Use map to estimate motion of targets.
Compute next position of robots to minimise escape time for the targets.
Allow dynamic exchanges of targets tracked among the robots.
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Progress to DateTask Due Status
Algo. Impl.
Defining models Q2
Model buildingNext-best view algorithm Q2Computing positions for 3D sensing operations Q4
Target findingExtended visibility models Q3Overhead/aerial robot ---Communication maintenance ---
Target tracking Q4
Real-time planning (moving obstacles) ---
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Achievements• Report on models developed for representing environment,
sensors, mobility, and motion plans.
• Implemented next-best view planner for constructing 2D model of an urban environment.
• Implemented target-finding planner in 2D for single robot with cone vision.
• Implementation of target-finding planner for single aerial robot.
• Developed algorithms for target-finding in 2D for a team of robots that maintain communication links.
• Implemented real-time planner for motion in the presence of moving obstacles.