Slides - Geometric Algorithms for Modeling, Motion, and Animation

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Core 1 Task 0 Task i External Task Pools … pump kernel abort or con,nue Global Task Pools Global Task Pools …… Task k U9liza9on Core k … Task k+i manage kernel balance kernel abort or con,nue …… …… …… Task n Core n … Task n+i abort or con,nue full empty full empty task kernel
Per<strong>for</strong>mance Analysis • We can prove that our parallel algorithms on GPU are work efficient, i.e. not slower than the serial implementa,on. • Tserial> Tbasic > Tpacket > Tworkload ≈ Tserial/#processor Tserial Tbasic Tpacket Tworkload
Page 1 and 2:
Real-‐Time Mo,on Planning and H
Page 4 and 5:
Sample-‐based Planners • Coll
Page 6 and 7:
Prior work on collision checking
Page 8 and 9:
Mo9on Planning for Physical Robots
Page 10 and 11:
Mo9on Planning for Physical Robots
Page 12 and 13:
Robot Sensors: Data Collec,on Camer
Page 14 and 15:
Demonstra,on of Point Cloud Data In
Page 16 and 17: Kinect Sensors http://graphics.stan
Page 18 and 19: Handling Noisy Point Cloud Data •
Page 20 and 21: Errors in Point Clouds Position err
Page 22 and 23: Handling Point Cloud Collision: Two
Page 24 and 25: Mesh Reconstruc,on => Collision •
Page 26 and 27: Classifica,on-‐based Collision
Page 28 and 29: Standard SVM Robust Classifica,on R
Page 30 and 31: Probabilis,c Collision between Two
Page 32 and 33: Results: Small Noise Deep Collision
Page 34 and 35: PR2 Robot Sensor Results Deep Colli
Page 36 and 37: Benefit • Speed is comparable to
Page 38 and 39: Mobile Manipulators: Onboard Comput
Page 40 and 41: Real-‐,me Mo,on Planning • Co
Page 42 and 43: Current GPU Architectures Host Grid
Page 44 and 45: GPGPU: GPUs for non-‐graphics a
Page 46 and 47: Probabilis,c Roadmap Method • Use
Page 48 and 49: Our GPU-‐based Pipeline
Page 50 and 51: Parallel Sampling • Real parallel
Page 52 and 53: BoBlenecks • High number of colli
Page 54 and 55: • Construct BVH on GPU BVH Constr
Page 56 and 57: GPU-‐based Parallel Algorithms
Page 58 and 59: GPU Memory Model • Shared memory
Page 60 and 61: Our Solu,ons • Parallel Collision
Page 62 and 63: Query Clustering • Find clusters
Page 64 and 65: Parallel Collision Query with Workl
Page 68 and 69: K-‐nearest neighbor computa,on
Page 70 and 71: Graph Search • Currently perform
Page 73 and 74: Timing Results: Collision Checking
Page 75 and 76: Overall Performance • Our paralle
Page 77 and 78: Preliminary Results • Scale well
Page 79 and 80: Applica,ons to PR2 Model • DOF 12
Page 81: Results: PR2 Model
Page 84 and 85: • Funding agencies - ARO - NSF Ac
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Slides - Geometric Algorithms for Modeling, Motion, and Animation

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