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Young J. Kim Ming C. Lin Dinesh Manocha
DEEP : Dual-space Expansion for Estimating Penetration depth between convex polytopes Young J. Kim Ming C. Lin Dinesh Manocha Dept of Computer Science UNC – Chapel Hill
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Background Need for the distance measure for the extent of interpenetration Application: Robot Motion Planning, Dynamic Simulation, Haptic Rendering, etc. Penetration Depth (PD) Minimum translational distance to make P and Q disjoint over all possible directions An incremental PD estimation algorithm, DEEP. ICRA 2002 UNC in Chapel Hill
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Previous Work Cameron and Culley ’86 Dobkin et. al. ’93 Cameron ’97
n2 algorithm based on explicit Minkowski sum computation. Dobkin et. al. ’93 Directional PD algorithm. Cameron ’97 Rough PD estimation based on the GJK algorithm. Agarwal et. al. ’00 Randomized algorithm. Bergen ’01 Expanding Polytope Algorithm (EPA). IMPLEMENTED. ICRA 2002 UNC in Chapel Hill
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Preliminaries (Minkowski Sum)
Minkowski Sum and Minkowski Difference P+Q = { p+q | pP, qQ } P-Q = { p-q | pP, qQ }, a.k.a. CSO or TCSO PD := minimum distance btwn OQ-P and (P-Q). P OP OQ P - Q OQ-P ICRA 2002 UNC in Chapel Hill
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Preliminaries (Gauss Map)
Gauss Map (F S2) : Dual mapping from feature space to normal space Face f Point n (outward normal of f). Edge e Great Arc a (locus of normals of two adjacent faces). Vertex v Region r (bounded by a’s) F S2 ICRA 2002 UNC in Chapel Hill
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Preliminaries (Minkowski Sum)
Inverse Map Fig: Bekker ‘01 Overlay Gauss Map ICRA 2002 UNC in Chapel Hill
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DEEP : Why Incremental ? n2 computation time is unaffordable for interactive applications. Observations PD is shallow. Motion Coherence. Penetration Separation ICRA 2002 UNC in Chapel Hill
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DEEP : Overview Localized computations for Gauss map and Overlay
Iterative Optimization Identify an initial feature for walk Measure the current PD March toward the local optimum OQ-P P - Q PD PD PD ICRA 2002 UNC in Chapel Hill
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DEEP: Initialization Find a subset of the overlay. n n -n ICRA 2002
UNC in Chapel Hill
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DEEP : Initialization Guessing an Optimal PD direction Motion
Coherence Centroid Difference Penetration Witness Feature ICRA 2002 UNC in Chapel Hill
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DEEP : Iteration OQ-P P - Q ICRA 2002 UNC in Chapel Hill
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DEEP : Local Minima The algorithm can be stuck in local minima.
In practice, we can avoid it by using various heuristics. ICRA 2002 UNC in Chapel Hill
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DEEP: Performance Random Models with different complexities and aspect ratios; e.g. sphere, ellipsoid, pen. One object revolves around the other object while rotating on its center of mass. ICRA 2002 UNC in Chapel Hill
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Timings (Fixed PD) ICRA 2002 UNC in Chapel Hill
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Timings (Variable PD) ICRA 2002 UNC in Chapel Hill
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PD Direction Tracking (DEEP)
ICRA 2002 UNC in Chapel Hill
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PD Direction Tracking (EPA)
ICRA 2002 UNC in Chapel Hill
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Application to 6DOF Haptic Rendering
The PHANTOM 1.5 Sensable Technology ICRA 2002 UNC in Chapel Hill
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6DOF Haptic Rendering Using Localized Contact Computations
ICRA 2002 UNC in Chapel Hill
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Summary and Future Work
Incremental Penetration Depth Estimation Algorithm (DEEP) Library: Better way to avoid the local minima problem. Extension to non-convex polyhedron Recently a new method combining the object space and image space has been proposed. ICRA 2002 UNC in Chapel Hill
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Sponsors ARO DOE NSF ONR Intel ICRA 2002 UNC in Chapel Hill
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Thanks
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DEEP : Iteration 1. Construct G-map G1 and G2 for V1 and V1’
2. Do the central projection 3. Compute the intersection ui ‘s in O(n) 4. In object space, determine which ui produces the best local improvement 5. Repeat this process until there is no improvement ICRA 2002 UNC in Chapel Hill
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DEEP : Local Minima The algorithm can be stuck in local minima.
In practice, we can avoid it by using various heuristics. Centroid Difference ICRA 2002 UNC in Chapel Hill
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Degeneracies Coplanar Faces Central Projection
Mapped to the same point on Gauss map Simply ignore duplicated points. Need to expand the search for neighborhood Central Projection Equator: projected to infinity, crossing arc can be broken Solved by local projection. ICRA 2002 UNC in Chapel Hill
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