Whole-Body Collision-Free Motion Planning
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oratory for Perceptual Robotics – Department of Computer Science Whole-Body Collision- Free Motion Planning Brendan Burns Laboratory for Perceptual Robotics University of Massachusetts Amherst
description
Whole-Body Collision-Free Motion Planning. Brendan Burns Laboratory for Perceptual Robotics University of Massachusetts Amherst. Why motion planning?. The real world is complicated. Collisions are hazardous. Mobility. How to motion plan?. Configuration space is big! (exponential) - PowerPoint PPT Presentation
Transcript of Whole-Body Collision-Free Motion Planning
Laboratory for Perceptual Robotics – Department of Computer Science
Whole-Body Collision-Free Motion Planning
Brendan BurnsLaboratory for Perceptual Robotics
University of Massachusetts Amherst
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Why motion planning?
The real world is complicated
Collisions are hazardous
Mobility
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How to motion plan?
Configuration space is big! (exponential) Exact methods are intractable Sampling-Based Planning (PRM)
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Probabilistic Roadmap PlanningKavraki & Overmars 1996
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Structure & Exploration
Identify the structure to expect
Acquire knowledge about structure
Exploit understanding as a guide
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Models
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Predictive Models
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Active Sampling
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Predictive Edge Checking
Edge checking is expensive
Our predictive model already exists
Construct a predictive roadmap
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Predictive Roadmaps
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Path Extraction
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Path Extraction
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Path Extraction
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Path Extraction
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Path Extraction
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Experiments
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Uniform Bridge Active Predictive
Guided Sampling Path ExtractionEdge Validation Collision CheckRoadmap Building
9-DOF
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Uniform Bridge Active Predictive
Guided Sampling Path ExtractionEdge Validation Collision CheckRoadmap Building
12-DOF
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Coming Soon…
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Stop
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Models
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Optimal Sampling
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Optimal Sampling
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Active Sampling
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Models
An approximate model of our current understanding
Predicts the state of unobserved configuration-space
Locally Weighted Regression (Atkeson et al.) Others are possible
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Active Sampling
Our current understanding suggests areas of improvement
Sample to reduce maximize the expected reduction in model variance (Cohn et al.)
Direct sampling in proportion to complexity
