CPG 1 1 CG CPG(Central Pattern Generator) Research on Generating Human Motion Using Musculoskeletal Model and CPG Akira Okazaki 1 and DongSheng Cai 1 Animation is one of the key elements of the CG, and there is strong demand that is the generation of human motion from many sectors. Recently, the purpose of many existing studies is reducing labor costs and realization of a natural motion. Based on these, we propose method of generating human motion using musculoskeletal model and CPG, and our method aims at autonomous and precise. In addition, we generate a walking motion. 1. (CG) CG 1 University of Tsukuba 1) (IK) 2) CPG 2. 2.1 FD IK 3) 1 c 2012 Information Processing Society of Japan
3 Hill Fig. 3 Hill s muscle model 2 1 2 1 Fig. 1 Mainly the lower limb musculoskeletal model 2 Fig. 2 Link structure of the bone OpenSim 4)5) 2.2 OpenSim 1 2 3 (Ball Joint) 1 (Pin Joint) 2.3 1 46 92 2.3.1 1 2.3.2 Hill Hill 6) Hill 3 (Contractile Element:CE) 2 c 2012 Information Processing Society of Japan
4 Fig. 4 f ce, l M f ce, l M curve 5 Fig. 5 f pee, l M f pee, l M curve 6 Fig. 6 f T, l T f T, l T curve 7 Fig. 7 M, v M M, v M curve (Parallel Elastic Element:PEE) (Series Elastic Element:SEE) 3 CE CE PEE SEE Hill α (Pennation Angle) fo M (Muscle Maximum) lo M (Muscle Optimal Length) ls T (Tendon Slack Length) v o (Contractile Max Velocity) OpenSim 7) 2.3.3 Hill ( 1 ) (CE) (l M ) (lo M ) 4 ( l M = lm ) l M o CE ( f ce = f ce ) l M l M fo M o l M 1 f ce 1 ( 2 ) (PEE) (l M ) (lo M ) 5 ( l M = lm ) l M o PEE ( f pee = f pee ) l M = 1 l M f pee ( 3 ) f M o (SEE) PEE (l T ) (ls T ) 6 ( l T = lt ) l T s SEE ( f T = f T ) l T = 1 l T f T fo M ( 4 ) CE PEE (f M = f ce + f pee ) (v M ) ( f M = f ce + f pee ) ( v M = v M vo ) 7 2.3.4 CE 8) a 0.0 a 1.0 3 c 2012 Information Processing Society of Japan
OpenSim 3. CPG 8 Fig. 8 M, l M (a = 1.0) M, l M curve(a = 1.0) 10 Fig. 10 9 Fig. 9 M, v M (a = 0.2) M, v M curve(a = 0.2) M, l M (a = 0.2) M, l M curve(a = 0.2) 1.0 ( f M ) 8 1.0 9 0.2 8 4 5 9 8 CE PEE SEE 6 f M, lm f M, vm 0.2 10 3.1 CPG(Central Pattern Generator) CPG 9) CPG CPG CPG CPG CPG 3.2 CPG CPG 10) 3 T r dx i dt = x i + s i b i f i n a ij y i (1) j=1 y i = max(0, x i) (2) 4 c 2012 Information Processing Society of Japan
1 CPG Table 1 parameters of CPG 11 2 CPG Fig. 11 CPG of two neurons parameter value s 1, s 2 5.0 T r 1.0 T a 10.0 b 1, b 2 2.5 a 12, a 21 1.5 12 CPG Fig. 12 Output of CPG T a df i dt = fi + yi (3) 13 Fig. 13 Hase s Model i x i i s i i f i i y i i T r T a x i f i b i a ij i j 2 CPG 11 11 CPG 1 CPG 12 12 2 4. CPG 11) 13 CPG CPG CPG 5 c 2012 Information Processing Society of Japan
( 1 ) ( 2 ) IK ( 3 ) OpenSim CMC(Computed Muscle Controls) ( 4 ) FD 5.2 ( 1 ) CMC CPG CPG ( 2 ) CPG FD ( 3 ) FD 14 Fig. 14 Procedure in the proposed method Open- Sim 1.5 5. 2 14 5.1 6. 1 0.25 15 16 17 11 12 CPG 16 17 CPG 6 c 2012 Information Processing Society of Japan
Fig. 16 16 Activation of biceps femoris muscle Fig. 17 17 Activation of Long head of the biceps femoris muscle 15 Fig. 15 Generated basic walk motion 1) CG-ARTS (2009). 2) Wei, X., Min, J. and Chai, J.: Physically-valid Statistical Motion Models for Hu- 7 c 2012 Information Processing Society of Japan
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