0Hindawi Publishing Corporation Advances in Mechanical Engineering Volume 2014, Article ID 852937, 11 pages http://dx.doi.org/10.1155/2014/852937
Research Article
Design and Development of the Humanoid Robot BHR-5
Zhangguo Yu, ' Qiang Huang,' ' Gan Ma, Xuechao Chen, ' Weimin Zhang, ' Jing Li,1 and Junyao Gao1,2
1 Intelligent Robotics Institute, School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
2 Key Laboratory ofBiomimetic Robots and Systems, Beijing Institute of Technology, Ministry of Education, Beijing 100081, China
3 Key Laboratory of Intelligent Control and Decision of Complex System, Beijing 100081, China
Correspondence should be addressed to Zhangguo Yu; yuzg@bit.edu.cn Received 21 February 2014; Accepted 13 April 2014; Published 3 August 2014 Academic Editor: Yong Tao
Copyright © 2014 Zhangguo Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper presents the mechanical and control system design of the latest humanoid robot platform, BHR-5, from Beijing Institute of Technology. The robot was developed as a comprehensive platform to investigate the planning and control for the fast responsive motion under unforeseen circumstances, for example, playing table-tennis. It has improvement on mechanical structure, stiffness, and reliability. An open control architecture based on concurrent multichannel communication mode of CAN bus is proposed to upgrade the real-time communication performance and the expansibility of the control system. Experiments on walking and playing table-tennis validate the effectiveness of the design.
1. Introduction
A humanoid robot features the body shape analogous to that of human being. This makes it possess the locomotion and manipulation capabilities similar to human being. It is expected that a humanoid robot may work or assist people in the human-centered environment without a need to adapt itself or to modify the environment. Therefore many researchers have made great efforts in this field. In recent decades, a series of humanoid robots such as ASIMO [1, 2], WABIAN [3], HRP [4, 5], QRIO [6] KHR [7], HRP3L-JSK [8], JOHNNIE [9], Atlas [10], THBIP [11], BHR [12,13], and a robot from Toyota Corp. [14] have been unveiled. Some researchers investigated the advanced control methods for biped robots [15-17].
Beijing Institute of Technology (BIT) launched the research on humanoid robots in 2000 and has developed generations of humanoid robot named BHR (BIT Humanoid Robots). The first four generations, from BHR-1 to BHR-4, successfully perform walking and complicated coordinated motions, such as Taichi, swordplay, and facial expressions, without external cables [12, 18]. Due to the limit of the mechanical structure and mechatronics system, the previous generations of robots could not satisfy the requirements
of the fast responsively coordinated whole-body motion under unforeseen circumstances; for example, they cannot play table-tennis. Hence, a new humanoid robot, BHR-5, is developed.
To achieve fast tasks, a humanoid robot should possess the property of high stiffness, light weight and inertia, high power output, and more rigorous real-time information sensing and processing capability. For BHR-5 platform, an integration design for mechanical structure and the casting manufacturing method was explored to enhance the stiffness. A two-degree of freedom (DOF) waist was added to improve agility and enlarge the workspace. The arm of the robot was changed from 6 DOFs to a redundant configuration to reinforce the agility of the manipulation. A flexible foot for landing impact absorption and foot attitude estimation was explored. An open control architecture based on concurrent multichannel communication mode of CAN (control area network) bus was proposed to shorten the periodical time of real-time communication to 2 msec. This design greatly enhanced capability of information sensing and processing and stability of the robot.
The remainder of this paper is organized as follows. The overview of the humanoid robot is described in Section 2. In Section 3, a mechanical design method is presented to
Ankle joint
(a) Photo of humanoid robot
(b) Joint configuration model
Figure 1: The humanoid robot.
Table 1: Specifications of the leg.
Table 2: Specifications of the arm.
Items Specifications Items Specifications
Degree of freedom 6 DOFs Degree of freedom 7 DOFs
(DOF) (DOF)
16.5 kg (including mechanism, 4.5 kg (including mechanism,
Mass of the leg motors and controllers, gears, Mass of the total arm motors, harmonic gears,
foot, and exterior case) bearings, and joint controllers)
Length of the thigh 0.3 m
Length of the shank 0.3 m
Yaw -30° ~30°
Hip Roll -25° ~25°
Range constraints of Pitch -30° ~60°
the joints Knee Pitch -90° ' ~5°
Ankle Pitch -25° ~50°
Roll -30° ~30°
enhance the robot's stiffness and reliability. In Section 4, the open control architecture is presented, which enables short communication period and good expansibility. In Section 5, the motion planning and experiments on walking and playing table-tennis are described. Finally, the conclusions are given in Section 6.
2. Overview of the Humanoid Robot
Our latest humanoid robot platform, BHR-5, shown in Figure 1, consists of two legs, two redundant arms, a torso, and a head. It has a total of 30 DOFs. The height of the robot is 1.62 m, and its weight is 65 kg. It is capable of walking up to 2.0 km/h and manipulating a variety of objects, for example, playing table-tennis. There are 2 color cameras, 3 gyroscopes and 3 accelerometers, and 2 six-axis force/torque sensors in the robot. There is a distributed real-time control
Length ofthe upper arm 0.30 m
Length ofthe forearm 0.22 m
Pitch -60° ~120°
Shoulder Roll -90° ~10°
Range constraints of Yaw -90° ~90°
the joints Elbow Pitch 0° ~ 125°
Yaw -90° ~90°
Wrist Pitch -60° ~60°
Roll -60° ~60°
End velocity at wrist >3 m/s
system for the motion control and information acquisition. The mechanical frame and skeleton are made of aluminum, and the exterior is made of hard plastic. Every joint is actuated by brushless DC motor and harmonic drive gear. The robot is powered by lithium-polymer battery. The detailed specifications of legs and arms are shown in Tables 1 and 2.
3. Mechanical Design of the BHR-5 Robot
3.1. Design Principles. The mechanical design of BHR-5 follows the main principles below.
(1) Bionic principles: the robot should have higher similarity in structures and functions with human being
Pitch joint in hip Roll joint in hip
Knee joint
Ankle joint
(a) (b)
Figure 2: Comparison of the thigh design between BHR-3 (a) and BHR-5 (b).
Pitch joint in hip
Roll joint in hip
Timing belt
(a) (b)
Figure 3: Comparison of the ankle joint design between BHR-3 and BHR-5 (b).
and human-like height, size, DOFs, and the movement ranges.
(2) High stiffness and light weight principles: high stiffness leads to less mechanical deformation and flexible vibration. Low weight may reduce the requirements of actuator's power and torque output. This would be useful to the balance control.
(3) Integration design principles: to enhance the stiffness and reliability, an integration design for the mechanical structure and the casting manufacturing were employed.
(4) Versatility principles: this robot is expected as a comprehensive research platform for robotics techniques, so it should be competent for the researcher to investigate diverse robotics issues.
(5) Reliability and maintainability principles: there are more than 30 joints in the robot. Each joint is composed of motor, reducer, sensors, and motor controller. The mechatronics system of the robot is quite complicated. The reliability is vital to the robot.
For example, even if only one joint has faulted, the robot would risk tipping over.
3.2. Mechanical Structure Design. Based on the design principles, we designed the waist, leg, arm, torso, foot, and their connections. Every active joint is driven by brushless DC motor from Maxon Company, and harmonic driver is employed as reducer in each joint. The power of all the motors in the legs is all 200 W. The harmonic gear has the features of no-backlash and high reduction ratios in small space with low weight. The total gear ratio of each joint in the legs is 320.
To reduce weight and preserve stiffness and strength, we used the integration design for the leg instead of our previous discrete design. We dug holes to decrease the weight and also employed the finite element analysis (FEA) to check the strength and stiffness of the mechanical components. Timing belt is adopted to change the gear ratio and the motion transmission link in terms of transmission mode and arrangement of the motor and moving joint. Figure 2 shows the discrete design of the thigh, hip pitch joint, and the ankle joint of the BHR-3 and the integration design for the BHR-5.
Plastic case
Figure 4: Right leg of the BHR-5 robot.
Yaw joint of waist
Roll joint of waist
Figure 5: Two-DOF waist joint of BHR-5 robot.
Figure 3 shows the comparison of the ankle design between the BHR-3 and BHR-5. In our previous robots, the ankle joint consisting of the pitch and roll DOF are assembled by many discrete components with bolts and screws. In the newly designed one, there is only an entire cross part with a hole for the electric cables to pass through. This enhances greatly the stiffness, strength, and reliability. Analogously, the same cross part is used for the joints in hip.
The front view (left figure) and lateral view (right figure) of the whole right leg are shown in Figure 4.
More DOFs are required to achieve the complicated motions, like playing table-tennis. A waist can help the robot to enlarge the workspace of the arm's end-effector through yaw and roll motion. Moreover, the motion of the waist joint can be used to compensate the torque that resulted from the legs during swing. The two-DOF waist is shown in Figure 5.
Rubber cushion
-Upper panel
Circuit board
Aluminium board
Rubber cushion Figure 6: Flexible foot of BHR-5 robot.
Figure 7: Front and left view of the right arm.
The design of the feet is crucial for humanoid robots because the feet are the main contact part between the robot and the environment. This design is to build up a new flexible robot foot capable of performing an effective role in landing impact absorption and foot attitude estimation. As a part of a humanoid robot, some basic characteristics should be met such as small size, light weigh, and capable of dealing with rough terrain, stairs, and obstacle environments [19]. The foot structure is illustrated in Figure 6. The upper panel is attached with the six-axis force/torque sensor, which is directly linked
Table 3: Mass distribution.
A leg_Joint 1/Yaw_Joint 2/Roll_Joint 3/Pitch_Joint 4/Pitch_Joint 5/Pitch_Joint 6/Roll
Mass (kg) 2.35 1.052 3.56 3.80 1.75 3.59
An arm Joint 1/Pitch Joint 2/Roll Joint 3/Yaw Joint 4/Pitch Joint 5/Yaw Joint 6/Pitch
Mass (kg) 1.08 0.7 1.17 0.41 0.71 0.3
Joint Joint 7ofarm Roll joint ofwaist Yawjoint ofwaist
Mass (kg) 0.2 3.15 4.29
Part Chest (with battery) Neck and Head
Mass (kg)_13.26_2.45_
Figure 8: Control system architecture for BHR-5 robot.
(1) for i = 1to max.nodes.num do
(2) for j = 1to max.channels num do
(3) read CAN data from jth channel
(4) if exists CAN frame then
(5) for k = 1to6
(6) if can.id == (0x180 + k) then
(7) sensor_result ^ CAN data
(8) end if
(9) end for
(10) end if
(11) end for
(12) end for
(13) return sensor_result
Algorithm 1: Algorithm of concurrent receiving.
(1) for i = 1to max.nodes.num do
(2) delay 150 ^s
(3) for j = 1to max „channels num do
(4) reference data ^ CAN bus
(5) end for
(6) end for
together with the leg. At the back of the aluminum base board, there is a sunken structure which provides a raillike mechanism for the upper panel to move vertically with an appropriate clearance. Four pieces of rubber cushion are placed between the upper panel and the aluminum base board, acting as impact absorption. Four pieces of rubber cushion are sticked onto the four corners of the underside of the aluminum base board, which is expected to keep the humanoid robot away from slipping and reduce the shock of impact between the base board and floor.
Algorithm 2: Algorithm of concurrent sending. Table 4: Walking parameters.
Items Value
Walking speed 1.5 km/h
Step length 0.33 m Walking cycle 0.8 s
Single-support phase duration 0.16 s
Double-support phase duration 0.64 s Steps number 6
The arm of BHR-5 robot has seven DOFs configured in an anthropomorphic way: 3 DOFs in the shoulder, 1 DOF in the elbow, and 3 DOFs in the wrist. The axes of the three joints in the shoulder intersect at one point. The axes ofthe three joints in the wrist intersect at another point. The spherical wrist enables the arm to change the posture of the end-effector suddenly without utilizing the whole arm to imitate an abrupt
Alnico
Hall board Hall sensor
Alnico
Figure 9: Home position determination.
1 0.8 0.6 0.4 0.2 0
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 y (m)
Figure 10: Stick figure of walking in the sagittal plane.
wrist motion usually employed by human. The arm is fixed on a frame (torso). To enlarge the valid workspace leftward for right arm, vice versa, rightward for the left arm and to decrease the possibility of collision between end-effector and the torso, there is an angle of 10 degrees indenting along the coronal plane. The front and left view of the right arm are shown in Figure 7. The left arm is almost the same to the right arm.
The mass distribution of the designed robot is shown in the Table 3.
4. Control System of the BHR-5 Robot
Generally, a humanoid robot has more than thirty DOFs to be servoactuated and should deal with multiple sensor information, which requires high speed computing capability. Moreover, the control system must be small sized and of low weight so as to become well embedded in the humanoid body. Therefore, the performance and architecture of the control system designed for the humanoid robot are crucial problems.
Up to date, some humanoid robot, for example, WABIAN-2 [3], adopted the centralized control architecture, and some other humanoid robots such as Hubo [20] and BHR-3 [13] employed the distributed control architecture. But for the latter one, the periodical time of distributed
communication is comparatively long relative to the centralized architecture. For example, the periodical control time of the Hubo is 10 ms, and BHR-3's is 5 ms. Usually, shorter periodical time is expected for a humanoid robot to acquire its current state and then to regulate itself to keep balance. In addition, as a robotics platform, a humanoid robot should have expansibility to interface with sensors, motor drivers, and controllers. To solve these problems, we propose an open control architecture based on concurrent multichannel communication mode of CAN bus to shorten greatly the periodical time to 2 ms.
4.1. Control Hardware Architecture. The control system of the humanoid robot is composed of two main parts: the vision and audio computer system and the real-time motion control system (Figure 8). The vision and audio computer system is responsible for vision and audio processing, and its operating system is Windows. The motion control system is used to coordinate the acquisition of sensors and the realtime servocontrol for all the joint controllers of the legs, arms, torso, and head. To achieve real-time capability, the operating system of the motion control computer is Linux plus RT-Linux core. The CAN bus works at 1Mbps. The Ethernet and CAN bus are used to exchange non-real-time data and real-time data, respectively, between the vision and audio computer and the motion control computer.
Two 6-axis force/torque sensors are located on each foot to measure the contact force and torque between the foot and the ground. And then force/torque data is acquired by a 4-channel PCI interface card to compute the actual COP (center of pressure) for stability control.
In order to enhance the expansibility of the control system, CANopen protocols are employed. Moreover, in order to reinforce the communication speed, we use three commercial CAN interface cards to make six CAN channels communicate concurrently. The algorithm for concurrently communication is shown in Algorithms 1 and 2. These algorithms result in the total time consumption of CAN bus communication in every control period that is around 0.5 ms. It should be pointed out that the CAN bus would work at the nonblock mode. To preserve enough time for carrying out motion planning and stability regulation algorithm, the periodical time of servocontrol is set at 2 ms.
4.2. Motor Controller. The motor controller receives the reference trajectory and drives the motor to follow the reference trajectory. In BHR-5, the motor controller feeds back the encoder counter and serves as a finder of the joint home (zero) position. We developed a high power density and output motor driver for the BHR-5 robot [21]. It includes two PCB boards layered and linked with connectors. One is the control board and the other is the power board. Its size is 88 x 64 x 25 mm. Each controller regulates one motor. It has three types of control patterns: current control loop, velocity control loop, and position control loop. The rated voltage is 100 V, and the peak current is 50 A. It complies with the major entries of the CANopen DS301 and DS402 protocol. For example, we employ the object 0x2002 for
0.2 0.15 0.1 0.05 0
-0.05 -0.1 -0.15 -0.2
1 2 3 4 5 6 7 t (s)
X support border Xzmp
1 2 3 4 5 6 7 t (s)
---Y support border
- Yzmp
Figure 11: ZMP in X and Y direction.
246 t (s)
246 t (s)
Figure 12: Ankle movement trajectory in the sagittal plane.
position reference value and 0x6064 for position feedback value. The object 0x2002 is mapped to RPDO1 and the object 0x6064 is mapped to TPDO1 via SDOs. The interpolated position mode is used in BHR-5 to coordinate the motion of all the motors.
We determine the home (zero) position of each joint by the combination of Hall sensors with Z-pulse (index pulse) of incremental encoder. A Hall circuit board with three or six Hall switch sensors is mounted on one part of the joint, and an alnico is mounted on the opposite, as shown in Figure 9. The Hall sensors and alnico are used to detect relative displacement of joint approximately. The Z-pulse is used to determine the accurate position at each turn. In other words, the Hall sensor is used to indicate which rotary turn generates the Z-pulse. Hence, the accurate home point can be determined by this method. The board is connected to
the driver. Except for zero point determination, it can also limit joint rotation angles. Two Hall sensors (number 1 and number 3) are mounted at the rotation boundaries of the joint and act as the angle limitation switch. If the joint exceeds its limitation, the driver will stop the joint rotation, which ensures the safety of the joint.
5. Motion Planning and Experiments
5.1. Walking Patterns Generation. To realize smooth walking, we selected the parameters of foot trajectories and hip trajectories considering the characteristics of human walking parameters collected from human. The other joint angles parameters can be obtained by inverse kinematics after the foot trajectories and the hip trajectories are computed. We
10 0 2 4 6
Figure 13: Hip movement trajectory in the sagittal plane.
Figure 14: Snapshots of a video of robot walking.
use the zero moment point (ZMP) criterion to evaluate the stability of the humanoid motion. The ZMP can be computed by the following equations [22]:
h=i mi & + g)xi - Zi=i m^i - Ii= i hy
Ii= 1 mi (zi + d) lN i mi fe + g)yi- iZ i mJizi + iZi 4Ä
Ii= i mi i + d)
where (xzmp, yzmp, 0) is the coordinate of the ZMP, m; is the mass of link i, g is the gravitational acceleration, N is the number of links, (xi,yi,zi) is the coordinate of the mass centre of link i on the absolute Cartesian coordinate system, Iix and Iiy are the inertial components, and Qix and Qy are the absolute angular velocity components around x-axis and y-axis at the centre of gravity of link i.
The convex hull of the contact points between the feet and the ground is defined as stable region. If the ZMP is within the stable region, the robot is able to walk stably.
§ -0.5
tin -1
Joint angle
¡JehT : ---- ------
... ....... —-
----------- ------
----------- ------
0.1 0.15 0.2 0.25 0.3 0.35 0.4
01 (pitch)
02 (roll) 03(yaw) 04 (pitch)
Time (s)
- 05 (yaw)
06 (pitch) - 07 (roll)
Figure 15: Racket trajectories in world reference frame and joint trajectories for stroke.
Firstly, the desired angles of foot when it leaves and lands on the ground are determined. Then, we obtain a particular foot trajectory by third-order spline interpolation in terms of given parameters of walking speed, walking stride, and the height of the swing foot, and so forth. Thirdly, we generate a series of trajectory candidates of hip by changing the key parameters and compute ZMP stability margin of each trajectory. The optimized trajectory with the largest ZMP stabilitymargincan be foundbyexhaustivesearchcalculation [22].
5.2. Walking Simulation and Experiment. The parameters of the planning trajectory are in Table 4. Biped walking of humanoid robotiscomposedoftwo phases: adouble-support phase and a single-support phase. We choose the humanlike 20% double-supporting phase [23]. Figure 10 is the stick figure of 6-step walking including an initial phase of lowering the centre of gravity (COG) from the ready state and an end phase oflifting COG. We can see that the feet leave and land on the ground with the desired angles. Figure 11 shows the ZMP in X and Y direction, the ZMP is always in the convex hull, and the robot is possible to walk according to the ZMP criterion. Figures 12 and 13 are the positions of ankle and hip in the world coordinate in the sagittal plane.
We conducted walking experiment on the BHR-5 with the speed of 1.5km/h and the step length of 0.33 m to evaluate the planning method. Figure 14 provides snapshots of walking experiment. The BHR-5 robot walked on the floor stably. In order to overcome the error of dynamics modelling and trajectory tracking and unexpected sudden events (e.g., slightly uneven terrain), we use sensory reflex control [24] to modify the off-line walking pattern real-time.
5.3. Motion Planning and Experiment for Table-Tennis Task. To verify the fast responsive motion capability of BHR-5 robot, we investigated the motion of table-tennis task. As
for the process of table-tennis task, the robot firstly grabs the ball images and predicts the subsequent ball trajectory using a series of ball position and velocity with aerodynamics and bounce model, then plans the interception motion, and finally drives the racket to intercept the incoming ball to fly and bounce on/off the table at the opponent's side.
We use 9(t) to indicate the joint angles vector of the arm of BHR-5; that is, 9(t) = [d1(t),d2(t),... ,d7 (t)]T. As long as the velocity vectors of the racket s(t) is designated in the Cartesian reference frame (including the linear and angular velocity), 9(t) maybe found through Jacobian pseudoinverse method as follows [25]:
e (t) = j+ (d (t)) s(t) + \[i- j+ (e (t)) j (d (t))] v^d(t),
where J+(d(t)) is the Moore-Penrose pseudo-inverse of J(d(t)), X is diagonal weighting matrix, I is the identity matrix, J is the Jacobian matrix of the arm, and the vector is gradient of a function of joint angles 9(t). The matrix [I - J+(9(t))J(9(t))] is a projector in the null space of J(9(t)). The matrix X is positive definite to maximize $>(9(t)) and negative definite to minimize $(9(t)). And
J+ = JT{JJT)-1.
Once the joint velocity vector 9(t) is gained, the angle of the joints 9(t) can be obtained by integral calculation. Since this manipulator has an additionally redundant joint, we may exploit this feature to optimize the motion of the manipulator not causing motion of the end-effector in accordance with (2). We employ a concept based on the comfort of posture for the human arm to build a cost function to optimize $>(9(t)) [26].
Figure 15 gives the joint trajectories of the arm in a stroke example from the ready state to interception point. Here, the initially ready joint angles of the arm are 45, -45, 90, 80, 90, 0, and 35 degrees, respectively. Figure 16 gives the snapshots from a video of the robot playing table-tennis against a human opponent.
It should be pointed out that the control of the lower body and upper body is considered together while playing table-tennis. When the robot moves the right arm to intercept the incoming ball, the controller will compute the counter-force resulting from the moving arm and compensate the counterforce to keep the robot from tipping over through coordinating movement of the lower body with the left arm and the torso.
6. Conclusion
In this paper, the mechanical and control system design of the humanoid robot, BHR-5, is presented. The robot was designed as a robotics platform. The main contributions of this paper are as follows.
(1) An integration design for mechanical structure and the casting manufacturing method was explored to enhance the stiffness and reliability of humanoid robot.
(2) An open control architecture based on concurrent multichannel communication is proposed to reinforce the real-time communication performance and the expansibility of the control system.
(3) Experiments on walking and table-tennis task are made to validate the effectiveness of the design.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work was supported by the National Natural Science Foundation of China under Grants 61320106012, 61375103, 61273348, 61175077, and 60925014; the National High Technology Research and Development Program (863 Project) under Grant 2014AA041602; "111 Project" under Grant B08043; and the Beijing Municipal Science and Technology Project.
References
[1] Y. Sakagami, R. Watanabe, C. Aoyama, S. Matsunaga, N. Higaki, and K. Fujimura, "The intelligent ASIMO: system overview and integration," in Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 2478-2483, Lausanne, Switzerland, October 2002.
[2] T. Takenaka, T. Matsumoto, and T. Yoshiike, "Real time motion generation and control for biped robot—1st report: walking gait pattern generation," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1084-1091, St. Louis, Mo, USA, 2009.
[3] Y. Ogura, H. Aikawa, K. Shimomura et al., "Development of a new humanoid robot WABIAN-2," in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA '06), pp. 76-81, Orlando, Fla, USA, May 2006.
[4] K. Kaneko, F. Kanehiro, M. Morisawa et al., "Hardware improvement of cybernetic human HRP-4C for entertainment use," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics (IROS '11), pp. 4392-4399, San Francisco, Calif, USA, September 2011.
[5] K. Kaneko, F. Kanehiro, S. Kajita et al., "Humanoid robot HRP-2," in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 1083-1090, New Orleans, La, USA, May 2004.
[6] Y. Kuroki, M. Fujita, T. Ishida, K. Nagasaka, and J. Yamaguchi, "A small biped entertainment robot exploring attractive applications," in Proceedings of the 2003 IEEE International Conference on Robotics & Automation, pp. 471-476, September 2003.
[7] I.-W. Park, J.-Y. Kim, J. Lee, and J.-H. Oh, "Mechanical design of the humanoid robot platform, HUBO," Advanced Robotics, vol. 21, no. 11, pp. 1305-1322, 2007.
[8] J. Urata, Y. Nakanishi, K. Okada, and M. Inaba, "Design of high torque and high speed leg module for high power humanoid," in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 10), pp. 4497-4502, Taipei, Taiwan, October 2010.
[9] K. Loffler, M. Gienger, F. Pfeiffer, and H. Ulbrich, "Sensors and control concept of a biped robot," IEEE Transactions on Industrial Electronics, vol. 51, no. 5, pp. 972-980, 2004.
[10] 2014, http://www.bostondynamics.com/roboLAtlas.html.
[11] Z. Xia, L. Liu, J. Xiong, Q. Yi, and K. Chen, "Design aspects and development of humanoid robot THBIP-2," Robotica, vol. 26, no. 1, pp. 109-116, 2008.
[12] Q. Huang, Z. Yu, W. Zhang, W. Xu, and X. Chen, "Design and similarity evaluation on humanoid motion based on human motion capture," Robotica, vol. 28, no. 5, pp. 737-745, 2010.
[13] Z. Yu, Q. Huang, J. Li, X. Chen, and K. Li, "Computer control system and walking pattern control for a humanoid robot," in Proceeding of the IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM '08), pp. 1018-1023, Xian, China, July 2008.
[14] R. Tajima, D. Honda, and K. Suga, "Fast Running Experiments Involving a Humanoid Robot," in Proceedings of the IEEE International Conference on Robotics and Automation, pp. 15711576, Kobe, Japan, 1571.
[15] Z. Li and S. S. Ge, "Adaptive robust controls of biped robots," IET Control Theory and Applications, vol. 7, no. 2, pp. 161-175, 2013.
[16] S. S. Ge, Z. Li, and H. Yang, "Data driven adaptive predictive control for holonomic constrained under-actuated biped robots," IEEE Transactions on Control Systems Technology, vol. 20, no. 3, pp. 787-795, 2012.
[17] X. Chen, Q. Huang, Z. Yu, and Y. Lu, "Robust push recovery by whole-body dynamics control with extremal accelerations," Robotica, vol. 32, no. 3, pp. 467-476, 2014.
[18] Z. Yu, G. Ma, and Q. Huang, "Modeling and design of a humanoid robotic face based on an active drive points model," Advanced Robotics, vol. 28, no. 6, pp. 379-388, 2014.
[19] J. Li, Q. Huang, W. Zhang, Z. Yu, and K. Li, "Flexible foot design for a humanoid robot," in Proceedings of the IEEE International Conference on Automation and Logistics (ICAL '08), pp. 14141419, Qingdao, China, September 2008.
[20] B. Cho, J. Kim, and J. Oh, "Online balance controllers for a hopping and running humanoid robot," Advanced Robotics, vol. 25, no. 9-10, pp. 1209-1225, 2011.
[21] F. Meng, X. Chen, J. Xue et al., "A biological humanoid joint controller based on muscle model," in Proceedings of the IEEE International Conference on Robotics and Biomimetics (ROBIO '13), pp. 1336-1340, Shenzhen, China, December 2013.
[22] Q. Huang, K. Yokoi, S. Kajita et al., "Planning walking patterns for a biped robot," IEEE Transactions on Robotics and Automation, vol. 17, no. 3, pp. 280-289, 2001.
[23] H. Uustal and E. Baerga, "Gait analysis," in Physical Medicine and Rehabilitation Board Review, S. Cuccurullo, Ed., Demos Medical Publishing, New York, NY, USA, 2004.
[24] Q. Huang and Y. Nakamura, "Sensory reflex control for humanoid walking," IEEE Transactions on Robotics, vol. 21, no. 5, pp. 977-984, 2005.
[25] S. Bruno, S. Lorenzo, V Luigi, and O. Giuseppe, Robotics: Modelling, Planning and Control, Springer, New York, NY, USA, 2009.
[26] Z. Yu, Q. Huang, X. Chen, W. Zhang, and J. Gao, "Design of a redundant manipulator for playing table tennis towards humanlike stroke patterns," Advances in Mechanical Engineering, vol. 2014, Article ID 807458,11 pages, 2014.
