Interactive Reach Planning for Animated Characters Using Hardware Acceleration Ying Liu Center for Human Modeling and Simulation Department of Computer Information and Science University of Pennsylvania

2 Overview b The problem b Our solutions 3-Step planning3-Step planning Sequential planningSequential planning Strength guided planningStrength guided planning b Summary and discussion b Contributions and future work

3 Problem Description b Input 3D environment (workspace)3D environment (workspace) A human modelA human model A start configuration and a goal locationA start configuration and a goal location b Output A collision-free path that specifies all the configurations for the arm to move from the start to the goalA collision-free path that specifies all the configurations for the arm to move from the start to the goal

4 Difficulty b The worst-case time bound for any complete motion planning algorithm is exponential in the dimensionality of the configuration space. b Human arm has 7 degrees of freedom Shoulder --- 3Shoulder Elbow --- 1Elbow Wrist --- 3Wrist --- 3

5 Goal and Approach b Real time performance Trading completeness for efficiencyTrading completeness for efficiency Efficient searchEfficient search Fast collision detectionFast collision detection b Natural movements Human strength modelHuman strength model

6 Applications b Computer animation b Real-time applications b Ergonomic design b Evaluation of workplaces and maintenance facilities

7 Basic Approaches b Roadmap Capture the connectivity of free C-spaceCapture the connectivity of free C-space Graph searchGraph search b Potential field Goal configuration generates an “attractive potential”Goal configuration generates an “attractive potential” Obstacles generate “repulsive potential”Obstacles generate “repulsive potential”

8 Previous Work b J. Lengyel et al (Cornell Univ.) Use graphics hardware to rasterize C-spaceUse graphics hardware to rasterize C-space b Y. Koga et al (Stanford Univ.) Randomized Path Planner (RPP)Randomized Path Planner (RPP) b N. Amato et al (Texas A&M) Obstacle-base PRMObstacle-base PRM b L. Kavraki et al (Rice Univ.) Lazy PRMLazy PRM

9 Previous Work b J. Kuffner et al (Stanford Univ.) Rapidly-exploring Random Trees (RRTs)Rapidly-exploring Random Trees (RRTs) b M. Garber and M. Lin 2002 (UNC) Constraint-basedConstraint-based Hardware-accelerated Voronoi diagramHardware-accelerated Voronoi diagram

10 Our Algorithms b 3-Step Planning b Sequential Planning b Strength Guided Planning

11 Assumptions b For each arm link, all interior surfaces are completely visible from its proximal end b Fixed shoulder position

12 3-Step Planning 3-Step Planning b Spatial search Search paths for end effector in 3D workspaceSearch paths for end effector in 3D workspace b Inverse kinematics IKANIKAN b Collision detection Graphics hardware assistedGraphics hardware assisted

13 Spatial Search b In discretized 3D workspace b Search a path for end effector IncrementallyIncrementally Within 6 adjacent neighborsWithin 6 adjacent neighbors –Top, bottom, left, right, front, back b Best-first search Distance-to-goal evaluation functionDistance-to-goal evaluation function

14 Search for End Effector

15 Inverse Kinematics b IKAN [Tolani et al. 2000] b Analytical method b Fast and reliable b One input 3D position, one output configuration

16 Depth Buffer b Also referred to as Z-buffer. b Consists of an array containing the depth value for each pixel of the image to be displayed. b Updated automatically each time when the scene is rendered.

17 Collision Detection b Link-wise, hardware-based b Set a virtual camera at the proximal end of a link and the view direction points to its distal end b Render the link only and get the depth map from depth buffer b Render the environment and get depth map b Compare the two depth maps

18 Collision Detection

19 Planning Diagram

20 Examples

21 Experimental Results Arm radius is Polygon primitives > 30,000. Arm radius is Polygon primitives > 30,000. Resolution of the depth buffer: 100*100.

22 More Examples …

23 Dynamic Planning

24 Summary b Spatial Search Fast and easyFast and easy b Collision detection Most time-consumingMost time-consuming Hardware helps achieve satisfactory performanceHardware helps achieve satisfactory performance b IKAN IncompleteIncomplete Lack of control over the outputsLack of control over the outputs

25 Sequential Planning b Previous work [ Ching and Badler 1992 ] and [ Gupta 1998 ][ Ching and Badler 1992 ] and [ Gupta 1998 ] b Basic idea To plan paths in 3D workspace that satisfy certain constraints for wrist, elbow and hand, respectivelyTo plan paths in 3D workspace that satisfy certain constraints for wrist, elbow and hand, respectively b Major components Spatial searchSpatial search Collision detectionCollision detection

26 Spatial Search b Best-first search For wristFor wrist –Least distance-to-goal For elbowFor elbow –Least distance-to-move For handFor hand –Least joint stress

27 Search for Wrist Least distance-to-goal Search for Wrist – Least distance-to-goal

28 Search for Elbow Least distance-to-move Search for Elbow – Least distance-to-move

29 Search for Hand Least joint stress Search for Hand – Least joint stress

30 Collision Detection

31 Planning Framework

32 Example

33 Comparisons to 3-Step Planning

34 Better Completeness

35 Experimental Results

36 Problem

37 Unnatural Movements

38 Strength Guided Planning b Human strength Definition and related topicsDefinition and related topics b Strength data and modeling b Algorithm Procedure and strategiesProcedure and strategies b Example

39 Human Strength b Strength is defined as the maximal force or torque that a muscle or a group of muscles can exert in a single voluntary effort under prescribed conditions. b Factors affecting strength Body configuration, anthropometry, age, gender, handedness, fatigue etc.Body configuration, anthropometry, age, gender, handedness, fatigue etc.

40 Effect of Joint Angles on Strength [Mital and Faard 1990]

41 Joint Angle Definition [Mital and Faard 1990]

42 Effect of Joint Angles on Strength [Pandya 1989]

43 Why Strength ? b Humans adopt postures of minimum discomfort among all feasible body configurations. [ Jung and Kee 1996 ] and [ Dysart and Woldstad 1996 ][ Jung and Kee 1996 ] and [ Dysart and Woldstad 1996 ] b When the upper-limb are kept in favorable positions, the strength increases and the discomfort decreases. [ Gil Coury et al ][ Gil Coury et al ]

44 Strength Data and Modeling

45 Strength Data and Modeling

46 Planning Procedure

47 Elbow Evaluation

48 Preliminary Screening b Joint limits constraint b Fine motion constraint

49 First Level b Comfortable Strength Level

50 Second Level – Least Effort b

51 Second Level – Maximum Strength increase b

52 Second Level – Maximum Strength increase b

53 Third Level – Preferred Side

54 Elbow Evaluation

55 Example

56 Reach Paths

57 Comparisons

58 Experimental Results

59 Summary

60 Discussion b Local, approximate algorithm b Real-time performance b No explicit restrictions on the geometry b No preprocessing and prior knowledge about the environment is required b Dynamic planning enabled

61 Contributions b Methodologically Direct planning in 3D workspace with constraintsDirect planning in 3D workspace with constraints Joint-based sequential planningJoint-based sequential planning b Technologically Biomechanically guided planningBiomechanically guided planning Interactivity, real time performanceInteractivity, real time performance

62 Future Work b More sophisticated criteria b More efficient search and more intelligent backtracking Global informationGlobal information Feedbacks from collision detectionFeedbacks from collision detection b Performance improvement Faster collision detectionFaster collision detection b Extension to include body posture

63 The End Thank You!