tailieunhanh - Humanoid Robots Part 12

Tham khảo tài liệu 'humanoid robots part 12', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | 268 Humanoid Robots From a biomechanics point of view the coordinates of the center of mass COM are estimated by Eq 9 . If we consider a body with n segments Si S2 . . Sn and if we assume that the weight of these segments are m1 m2 . .mn the center of mass can be estimated. In our case we need to estimate the coordinates of the CoM projection on the floor plan which means that we need a pair of coordinates COM x y . The relative masses of the segments depend on the weight of the skeleton on the nature and proportion of the used effectors and sensors alternatively it can be directly inspired from human anthropomorphic studies even if human muscles still better than any industrial actuators. x com y com n - Sw . m x J-1 J J M En _Sw . m y j-1 J j 8 9 M Where i represents the iteration number n_Sw is the number of sub-Swarms in the proposed model here n_Sw 6 see figure 1 a . M is the mass of the locomotion system and mj represents the mass of the segment Sj . If we assume that the robot has a footprint which is propositional to its locomotionsystem dimensions and if we assume that the footprint is rectangular see subsection A we deduce a simple representation of the sustention polygon in both double and single support phases see Figure 5. The foot-print is supposed to be rectangular with the length fl and a breadthfb Eq 1 and 2 . If only single support phases are used during the walking cycle the sustention polygon is limited to the segment joining left ankle to right ankle this is a constrained solution compared to the first one. Fig. 5. A static walking cycle foot prints COM circle projection on the footprints rectangle . Fitness functions In our case we need both local finesses functions in order to select local bests particles Toward Intelligent Biped-Humanoids Gaits Generation 269 and a global fitness function that allow us to select the best posture within those assuming stability. Local best particles are those minimizing the error expressed by Eq