This paper presents the design and implementation of an intelligent wireless remote control system for a biped robot. A recurrent cerebellar model neural network (RCMNN) is introduced, then it is used for the walking control of a biped robot. Furthermore, the remote communication module is designed for the remote control of a robot. Finally, experimental results show that the developed system can achieve satisfactory control performance for the walking control of a high-order nonlinear biped robot

Chen, Chih-Hsuan

Lin, Chih-Min

Taiwan Association of Engineering and Technology Innovation: E-Journals

Proceedings of Engineering and Technology Innovation, vol. 6, 2017, pp. 01 - 05  Intelligent Wireless Remote Control System Design for a Biped Robot Chih-Hsuan Chen1, Chih-Min Lin1, 2,* 1Department of Electrical Engineering, Yuan Ze University, Taoyuan, Taiwan, ROC. 2School of Informatics, Xiamen University, Xiamen, Fujian, China. Received 30 July 2017; received in revised form 15 August 2017; accepted 26 August 2017  Abstract This paper presents the design and implementation of an intelligent wireless remote control system for a biped robot. A recurrent cerebellar model neural network (RCMNN) is introduced, then it is used for the walking control of a biped robot. Furthermore, the remote communication module is designed for the remote control of a robot. Finally, experimental results show that the developed system can achieve satisfactory control performance for the walking control of a high-order nonlinear biped robot.  Keywords: biped robot, wireless remote control, cerebellar model neural network  1. Introduction Biped robots are a favorite topic in the field of robotics [1-3]. Many dynamics analysis and system control researches have been presented for the biped robots [4-5]. In order to achieve adaptive control of a biped robot to face unknown environment, an intelligent control algorithm will be proposed in this study. Cerebellar model neural network (CMNN), first proposed by Albus, has been applied to control a robot manipulator [6]. CMNN has been applied for the control of complex dynamic systems due to its good generalizing capability and fast learning property. In this paper, a recurrent CMNN (RCMNN) is introduced; then, an adaptive RCMNN-based control system is designed for the walking control of a biped robot. Remote control has been frequently used in a lot of auto-matic control systems. Recently, the wireless remote control robots have been developed to dictate a biped robot to move according to the user’s commands . The proposed walking command system can be applied to the remote medical robot, the exploration robot, the home security robot, and so on [7]. In the deve loped biped robot, the Zigbee wireless communication has been designed for the remote communication between the monitoring computer and the robot. 2.  Remote Control of the Biped Robot In order to achieve remote wireless control, a wireless module is used for the communication between the robot and personal computer (PC), as shown in Fig. 1. The wireless module is also used to monitor the motion of the robot. The Zigbee-100, which has 2.4 GHz bandwidth, is designed for robot communication. This peer-to-peer communication module is not easy to be interfered by other equipment or bands .                                                                  * Corresponding author. E-mail address: cml@saturn.yzu.edu.tw Proceedings of Engineering and Technology Innovation, vol. 6, 2017, pp. 01 - 05 Copyright ©  TAETI 2  Fig. 1 Block diagram of remote communication robot 3. Adaptive RCMNN-Based Control System 3.1.   Cerebellar model neural network  A recurrent CMNN (RCMNN) is introduced as shown in Fig. 2. The basic functions of RCMNN are described as follows. Input Space Receptive -FieldSpace Weight MemorySpaceAssociation MemorySpaceRecurrent Unitk1nkOutput Space kow  panp1pk1 kna kikr TikrOno1osI sA sR sW sO  Fig. 2 Architecture of an RCMNN (1) Input spacesI : For a given aanTnppp  ],,,[ 21 p , where an  is the number of input state variables, each input state variable   ip is assumed to be quantized into en  discrete regions (called “neurons”) according to a given control space. (2) Association memory spacesA : Several elements, fn , can be accumulated as a “block”. In this space, each block performs a receptive-field basis function, and the Gaussian function is adopted here as the receptive-field basis function, which can be represented as 22( ),   1, 2,rik ikik dikp cexp for k nv      (1) where   ik represents the output of the k-th receptive-field basis function for the i-th input with the mean   ikc and variance  . ikv In addition, the input of this block can be represented as  )()()( Ttrtptp ikikirik    (2) where ikr  is the recurrent weight, and )( Ttik  denotes the value of   ik through delay time T . It is clear that the input of this block contains the memory term )( Ttik  , which stores the past information on the network and presents a dynamic mapping. (3) Receptive-field spacesR : The k-th multidimensional receptive-field function is defined as 2211( )( , , , ) ,   1, 2,a an nrik ikk k k k ik diiikp cexp for k nv       p c v r  (3)   Computer terminal  peripheral circuits  Zigbee Zigbee RS232 Wireless communicate  Computer Proceedings of Engineering and Technology Innovation, vol. 6, 2017, pp. 01 - 05 Copyright ©  TAETI 3 where aanTknkkk ccc  ],,,[ 21 c , aanTknkkk vvv  ],,,[ 21 v  and aanTknkkk rrr  ],,,[ 21 r . The multi-dimensional receptive-field functions can be expressed in vector form as Tnk d],,,,[),,,( 1  rvcpΦ  (4) where dadnnTTnTkT  ],,,,[ 1 cccc   , dadnnTTnTkT  ],,,,[ 1 vvvv   and dadnnTTnTkT  ],,,,[ 1 rrrr  . (4) Weight memory spacesW : Each location of sR  to a particular adjustable value in the weight memory space can be expressed as 11 1 1111  [ , , , , ]od oood d d op nn nk kp knp nn n p n nw w ww w ww w w          W w w w  (5) where ddnTpnkppp www  ],,,[ 1 w , and kpw  denotes the connecting weight value of the p-th output associated with the k-th receptive-field. (5) Output spacesO : The output of RCMNN is the algebraic sum of the activated weights in the weight memory and is expressed as dnkkkpTpp wo1Φw (6) The outputs of CMNN can be expressed in a vector notation as ΦWo TTnp Oooo  ],,,,[ 1   (7) The architecture of RCMNN is designed to have the advantages of simple structure and dynamic characteristics. The role of the recurrent loops is to consider the past value of the receptive-field basis function in the association memory space. Thus, this RCMNN has dynamic characteristics. 3.2.   Adaptive RCMNN-based control system The biped robot system is highly nonlinear. To effectively control a biped robot, an adaptive RCMNN-based control is introduced in this subsection. The configuration of the proposed adaptive RCMNN control system of the biped robot system is illustrated in Fig. 3. There are two inputs for the adaptive RCMNN controller: first is the tracking error, wh ich is the difference between the reference trajectory md  and actual trajectory d  derived from accelerometer; and second is the angular velocity   obtained from gyroscope. The output of RCMNN is  the control signal HipU , which adjusts the hip joint to achieve a stable posture. The robot needs this on-line adjusting technique to adjust its original trajectories as it encounters unexpected situations. Another controller is the ZMP compensator, which is also an adaptive RCMNN for auto-tuning the reference trajectories. The two inputs of the ZMP compensator are: the tracking error between the reference ZMP position refZMPP  and the actual ZMP position ZMPP  derived from (8); and the change of this ZMP tracking error. The output of this RCMNN is the ZMP control effort ZMPu . By combining the pre-scheduled walking pattern P with the output of ZMP compensator ZMPu , and after inverse kinematics, the computed control torque 0  can be obtained for the biped robot. By adding the computed torque 0  with the hip compensating torque, HipU , the actual driving torque  will be applied to drive the biped robot. Proceedings of Engineering and Technology Innovation, vol. 6, 2017, pp. 01 - 05 Copyright ©  TAETI 4  Fig. 3 Block diagram of the adaptive RCMNN control biped robot system 4. Experimental Results 0 2 4 6 8 10 12 14 16-5051015202530ZMP x-coordinate(cm)Time(sec)Stable region(a)ZMP x-coordinate(cm)ZMP x-coordinate(cm) (a) x-direction 0 2 4 6 8 10 12 14 16-8-6-4-202468ZMP y-coordinate(cm)Time(sec)Stable region(b)ZMP y-coordinate(cm)ZMP y-coordinate(cm) 0 2 4 6 8 10 12 14 16-8-6-4-202468ZMP x-coordinate(cm)ZMP y-coordinate(cm)(c)ZMP y-coordinate(cm) (b) y-direction (c) x-y plane Fig. 4 Actual ZMP of biped robot Proceedings of Engineering and Technology Innovation, vol. 6, 2017, pp. 01 - 05 Copyright ©  TAETI 5 The RCMNN control system is accomplished by the proposed control system, whose structure is depicted in Fig. 2. The RCMNN used here is characterized as 9 en , 4fn . The initial learning-rates are selected as 01.0zwη  and .001.0 zrzvzm ηηη  The initial values of weights are chosen as 0kpw  and 0ikr . The initial values of parameters of means and variances are chosen as 22ikv and 1 2 3 4 5 6 7 8[ , , , , , , , ] [ 49, 35, 21, 7, 7, 21, 35, 49]i i i i i i i im m m m m m m m       for all i  and k . The receptive-fields are selected to cover the input space ]}35,35][35,35{[   along each input dimension, respectively. These parameters have been chosen through trials to achieve satisfactory con trol performance. The actual zero moment points (ZMPs) are shown in Fig. 4. The experimental results show that the robot’s ZMP has been maintained within a stable region to achieve stable walking. 5. Conclusions This paper has successfully developed and implemented an intelligent wireless remote control system for a biped robot. The RCMNN-based control system is developed. This adaptive control system has been successfully implemented on a biped robot system. The experimental results demonstrate the ability to achieve satisfactory control performance for stable walking for a high nonlinear biped robot. Acknowledgement The support of the Nation Science Council of Republic of China under grant NSC 101-2221-E-155-026-MY3 is gratefully acknowledged. References [1] Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, and K. Fujimura, “The intelligent ASIMO: System overview and integration,” IEEE/RSJ International Conf. on Intelligent Robots and Systems, September-October 2002, vol. 3, pp. 2478-2483. [2] J. H. Kim and J. H. Oh, “Walking control of the humanoid platform KHR-1 based on torque feedback control,” Proc. IEEE International Conf. on Robotics and Automation, April-May 2004, vol. 1, pp. 623-628, 2004. [3] Y. D. Hong, Y. H. Kim, J. H. Han, J. K. Yoo, and J. H. Kim, “Evolutionary multiobjective footstep planning for humanoid robots,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 41, no. 4, pp. 520-532, July 2011. [4] K. Hosoda, T. Takuma, A. Nakamoto, and S. Hayashi, “Biped robot design powered by antagonistic pneumatic actuators for multi-modal locomotion,” Robotics and Autonomous Systems, vol. 56, no. 1, pp. 46-53, January 2008. [5] Y. S. Cha, , K. G. Kim, J. Y. Lee, J. J. Lee, M. J. Choi, M. H. Jeong, et al., “MAHRU-M: A mobile humanoid robot platform based on a dual-network control system and coordinated task execution,” Robotics and Autonomous Systems,  vol. 59, no. 6,  pp. 354-366, June 2011. [6] J. S. Albus, “A new approach to manipulator control: The cerebellar model articulation controller (CMAC),” Journal of Dynamic Systems, Measurement and Control, vol. 97, no. 3, pp. 220-227, September 1975. [7] T. Kim, E. Kim, and J. W. Kim, “Development of a humanoid walking command system using a wireless haptic controller,” International Conference on Control, Automation and Systems, IEEE Press, October 2008, pp. 1178-1183. 

Intelligent Wireless Remote Control System Design for a Biped Robot

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Intelligent Wireless Remote Control System Design for a Biped Robot

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