# ZMP Trajectory Generation for Reduced Trunk Motions of Biped Robots

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1 ZMP Trajectory Generation for Reduced Trunk Motions of Biped Robots Jong H. Park School of Mechanical Engineering Hanyang University Seoul, 33-79, Korea Yong K. Rhee School of Mechanical Engineering Hanyang University Seoul, 33-79, Korea Abstract Trunk motions are typically used to stabilize the locomotion of biped robots, which can be very large in some leg trajectories. This paper proposes a method to reduce the motion range of the trunk by generating a desired trajectory of the ZMP. The trajectory is determined by a fuzzy logic based upon the leg trajectories that are arbitrary selected. The resulting ZMP trajectory is similar to human s one and the ZMP continuously moves forward. The proposed scheme is simulated on a 7-degree-of-freedom biped robot. Its results indicate that the proposed ZMP trajectory increases the stability of the locomotion and thus resulting in reduction of motion range of the trunk. Introduction Generating desired trajectories for dynamic walking of biped robots is one of the important research areas. The simplest method to generate trajectories is the inverted pendulum mode [, 2]. However, when the trajectory is implemented, the stability of the biped robot is compromised by the masses that are assumed to be negligible in generating the trajectory. There eists a method to keep the simplicity of the inverted pendulum mode while improving the stability [3]. However, all these methods are not fleible in generating arbitrary leg motions of the biped robot. A more complicated but more accurate method is based upon the zero-moment point (ZMP) equation, which describes relationship between the joint motions and the forces applied at the ground [4, 5, 6]. Yamaguchi et al. [5]and Li et al. [7]used trunk swing motions and trunk yaw motions, respectively, to increase the locomotion stability for an arbitrary robot locomotion. It is very important to coordinate the upper body and the lower legs in order to stabilize dynamic locomotions of the biped robot. Another work by [8] controls the actual ZMP to track a reference ZMP. However, previous researches have assumed that the ZMP does not change at all or that ZMP changes in an uncoordinated fashion during walking as long as one foot is on the ground. The human has more stable and adaptive locomotion by changing his ZMP appropriately. We propose in this paper that the ZMP should be changed as the body moves during a stride. It is based upon observations of human locomotion, where the heel of the free leg contacts the ground first, and then its middle part and finally its toe contact the ground. In the proposed scheme, the contact point with the ground, i.e., the ZMP, moves continuously forward as the locomotion progresses. The desired ZMP trajectory depends upon the robot configuration, especially hip position and the swinging free leg. We implemented a fuzzy-logic algorithm to generate the desired ZMP trajectory depending upon the body posture. The control scheme is simulated only in the sagittal plane. In this paper, locomotion only with the single-leg-support phase, where only one leg supports the weight of the biped robot, is considered. Section 2 eplains the ZMP equation and Section 3 describes the desired ZMP trajectory and the fuzzy logic to generate it along with overall control system configuration. Section 4 covers computer simulations of the proposed control scheme and their results, followed by conclusions in Section 5. 2 ZMP Equation Suppose that the ground applies a pure force to the biped robot at a location p, called the ZMP, which

2 M j z m i z F k s k r i P r i -P m i+ y q o q 2 q 3 q 7 q 4 q 5 q 6 Figure : The coordinate frame and the ZMP. should be within the foot of the supporting leg for the biped robot to be stable. Then the dynamics of the biped robot under other eternal forces and moments becomes m i (r i p) ( r i + g)+ M j i j + (s k p) F k = () k where m i is the mass of the link i; r i is the position of the mass center of link i; M j is an eternal moments applied to the biped robot; s k is the location where eternal force F k is applied; and g =[g g y g z ] T r i =[ i y i z i ] T p =[ zmp y zmp ] T F k =[F,k F y,k F z,k ] T s k =[ k y k z k ] T r i =[ i y i z i ] T M j =[M,j M y,j M z,j ] T. Note that in normal locomotion on a horizontal surface, g z = g. See Fig.. Considering the dynamics in the y-direction only under the assumption of no eternal moments, i.e., M j =, Eq. () can be epressed as zmp = n i= m i( z i g z ) i n i= m i(ẍ i g )z i n i= m i( z i g z ) k F z,k + k (z kf,k k F z,k ) n i= m i( z i g z ) k F. (2) z,k Figure 2: Model of a 7 degree-of-freedom biped robot. where n is the total number of components of the robot. 3 ZMP Trajectory Control 3. ZMP Trajectory Motions of the biped robot can be considered as two parts: leg motions and the trunk (or upper-body) motions. Leg motions should be determined depending upon its environment. For eample, if there is an obstacle is in the way of the swinging free leg, the foot of the free leg should be lifted up higher. Or if the obstacle is at the location where the free leg is to land on the ground, the landing point and thus the walk pace should be modified. In general, terrain environment is not known a priori, it should be possible to change the walking pattern anytime. On the other hand, it is desired that the posture of the trunk is kept stationary. This allows, for eample, the robot to carry objects in a stable manner, or to get scenery information with vision cameras. In normal locomotion, the trunk should be in a straight-up position. When the biped robot climbs up a hill, its trunk should lean forward to increase the stability of locomotion. Assuming that the trajectory of the legs are already determined, for eample, by the terrain constraints, we are to determine the trunk motion that would keep its stationary posture. Figure 2 shows the model of the biped robot to be considered here. Assuming that the height of the trunk is constant because of the stationary posture, i.e., z 7 = ż 7 =,

3 . NB NM NS Z PS PM PB. NV NB NM NS Z PS PM PB PV (m) NB NS Z PS PB (m) zmp Figure 4: Fuzzy membership functions for zmp and the defuzzification using the center of gravity (m) Figure 3: Fuzzy membership functions for 3 and 6. Eq. (2) becomes m 7 ẍ 7 z 7 + m 7 g z 7 = 6 m i ( z i g z ) i i= 6 m i (ẍ i g )z i + m 7 g z 7 i= + zmp {m 7 g z 6 i= m i ( z i g z )+ k F z,k }. (3) If the ZMP is determined and no eternal force eists, the right-hand side terms of the equation are known ecept for eternal forces and thus differential equation of Eq. (3) can be easily solved numerically. 3.2 Fuzzy Algorithm for the ZMP When a human walks, the ZMP is not fied but rather moves. In the human locomotion, it is at the heel that the foot of the free leg contacts the ground first. And then the middle part of the foot and finally its toe come into a contact with the ground. This indicates that a human uses strategy of moving the ZMP continuously forward as the locomotion progresses. Based upon this observation, we implemented a fuzzy logic to generate the desired ZMP trajectory zmp (t) which depends upon the configuration of the biped robot. The rule base of the logic is shown in Table. Table : The rule base to generate the desired ZMP trajectory depending upon the hip and the free leg positions. 6 3 PB PM PS Z NS NM NB PB PV PV PB PS NS NM NB PS PV PB PM Z NS NB NB Z PV PB PM Z NM NB NV NS PB PB PS Z NM NB NV NB PB PM PS NS NB NV NV The desired ZMP is greatly influenced by the position of the hip, 3, and in a less degree by the position of the free leg configuration, which is represented by the foot position of the free leg, 6. Five fuzzy membership functions are used for each of 3 and 6 as shown in Fig. 3. The logic shows that the more the hip is in front of the supporting foot, the more positive the ZMP becomes. A similar relationship eists between the free leg and the ZMP in a less degree. Total seven fuzzy membership functions are used for the ZMP as shown in Fig. 4. The asymmetry reflects a human foot, which the heel is slightly behind the ankle and the toe is farther away in front of the ankle. Defuzzification based upon the center of gravity is used. 3.3 Joint Controller Based upon the ZMP computed by the fuzzy logic, and under the assumption that no eternal force is applied to the robot, we used the 4th-order Runge-Kutta method to solve Eq. (3) for 7 with the desired ZMP trajectory generated by the fuzzy logic. Trunk trajectory 7 (t) and leg trajectories i (t), for i =,...,6, determines the complete desired motion of the biped robot. The desired joint trajectory q d (t) isthencom-

5 angle (deg) Fied ZMP Moving ZMP time (s) Figure 7: Trunk motions when the fied and moving ZMP trajectories are used. Figure 9: Walking simulation with the fied ZMP Error in ZMP (m) time (s) Figure 8: Walking simulation with the moving ZMP. Figure : Errors in the ZMP trajectory. implemented. Figure 8 and 9 show the walking simulation with the moving ZMP and with the fied ZMP at =, respectively. With the fied ZMP, the trunk has to swing back and forth from - to 3 while it moves less than ±5 with the moving ZMP. Figure indicates the actual ZMP follows closely with the desired ZMP by the computed-torque controller. Another simulation was done with the leg trajectories that are significantly different from the one used in the previous simulation. The simulation results are showninfig.forthemovingzmpandfig.2for the fied ZMP. Even though the trunk motions generally become larger than the ones in the previous simulation, they are again smaller with the moving ZMP than with the fied ZMP. 5 Conclusions This paper proposed a new control scheme for biped robots based upon a varying ZMP rather than a fied ZMP. This scheme is very similar to what can be observed in the human locomotion. The ZMP trajectory produced by a fuzzy-logic algorithm moves continuously forward depending upon the hip position and the free leg configuration. The proposed scheme is simulated on a 7 degree-of-freedom biped robot and the simulation results show that it does not require a large swing motion of the trunk and thus significantly stabilizes the biped robot.

6 Figure : ZMP Walking simulation 2 with the moving Figure 2: Walking simulation 2 with the fied ZMP. Acknowledgments The authors would like to thank Prof. Takanish for his discussions with us. References [2]S. Kajita and K. Tani, Eperimental study of biped dynamic walking in the linear inverted pendulum mode, in Proceedings of the IEEE International Conference on Robotics and Automation, (Nagoya, Japan), pp , 995. [3]J. H. Park and K. D. Kim, Biped robot walking using gravity-compensated inverted pendulum mode and computed torque control, in Proceedings of the IEEE International Conference on Robotics and Automation, (Lueven, Belgium), 998. [4]A. Takanish, M. Tochizawa, H. Karaki, and I. Kato, Dynamic biped walking stabilized with optimal trunk and waist motion, in Proceedings of the IEEE/RSJ International Workshop on Intelligent Robotics and Systems, (Tsukuba, Japan), pp , 989. [5]J. Yamaguchi, A. Takanish, and I. Kato, Development of a biped walking robot compensating for three-ais moment by trunk motion, in Proceedings of the IEEE/RSJ International Workshop on Intelligent Robotics and Systems, (Yokohama, Japan), pp , 993. [6]J. Yamaguchi, N. Kinoshita, A. Takanish, and I. Kato, Development of a dynamic biped walking system for humanoid development of a biped walking robot adapting to the humans living floor, in Proceedings of the IEEE International Conference on Robotics and Automation, (Minneapolis, MN), pp , 996. [7]Q. Li, A. Takanish, and I. Kato, Learning control of compensative trunk motion for biped walking robot based on ZMP stability criterion, in Proceedings of the IEEE/RSJ International Workshop on Intelligent Robotics and Systems, (Raleigh, NC), pp , 992. [8]K. Sorao, T. Murakami, and K. Ohnishi, A unified approach to ZMP and gravity center control in biped dynamic stable walking, in Proceedings of Advanced Intelligent Mechatronics, (Tokyo, Japan), 997. []S. Kajita and K. Tani, Study of dynamic biped locomotion on rugged terrain: Derivation and application of the linear inverted pendulum mode, in Proceedings of the IEEE International Conference on Robotics and Automation, (Sacramento, CA), pp. 45 4, 99.

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