英文论文范例机器人分布式控制系统概述.docx

Distributed Control System Design for Humanoid RobotsAbstractA humanoid robot with multiple sensors needs a control system with severe requests like real-time control and friendly man-computer interaction. In this paper, we propose a distributed control system consisting of double PC control sub-system based on CAN bus for humanoid robot. The control system is divided into three layers. As the main control layer, double PC control architecture is formed with two sub-systems. Man-machine interface sub-system under Windows OS aims at controller debugging and information observation, meanwhile real-time motion control sub-system under RT-Linux OS is developed for real-time motion control realization. The communication layer based on CAN bus assures the reliable communication between real-time computer and joint controllers. The actuating layer is responsible for joint servo control and sensory data acquisition. At last, the experiment is implemented on newly-build humanoid robot MIH-2. Joint motor testing under Windows OS confirms the effectiveness of humanoid robot debugging and accuracy of data acquisition. Real-time control system testing based on RT-Linux OS demonstrates that the system can provide stationary time-lag ensuring the stability during the walking pattern control for humanoid robot. The experiment results show that the presented control system can meet all the requests during the humanoid robot walking control. INTRODUCTION With the development and maturity of robot technology, humanoid robot with multi-sensors has gradually become another hotspot in this field 1. Generally, a humanoid robot has more than thirty DOFs (degree of freedom) to be servo-actuated and should deal with multiple sensor information, which requires reliable and stable control system with high computing speed. Great breakthrough such as well-known humanoid robots P3 2, ASIMO and HRPs has been achieved thanks to the efforts of many institutes and researchers 3. Many of the robots adopt centralized control system that places a computer and an interface board which has A/D, D/A at the center. Controllers of the motor and sensors are all connected to the computer directly. System with this architecture has very high communication speed, which can be controlled by simple software. However, the wires connected to the central computer are too excessive to assure the reliability and stability during the control of walking pattern from the perspective of hardware. In terms of the humanoid robot requiring high real-time control, the redundant wires increase the susceptibility to noise and wiring that undermines the basic stationary time-lag for real-time control. In the case of adding a new servo module to the body, all wires need to be connected to the central computer which means the overall arrangement of existed wires should be changed 4. In order to solve these problems, the concept of distributed control system with simple hardware connection has been brought forward 5. In this paper, we propose a distributed control system based on CAN bus and double PC control architecture to solve the problem of stability and reliability during real-time control of the humanoid robot. Through the double PC control design, performance of the whole system is also optimized. We adopt centralized management and distributed control model in our system that can be divided into 3 layers based on architecture and function. Main control layer is formed with double OS (operating system) to acquire the advantages of Windows OS and also ensure the stable real-time control. Thereinto, Windows OS aiming at controller debugging and information observation provides friendly man-machine interface and meanwhile RT-Linux OS realizes the real-time motion control. Actuating layer is integrated on the robot body to implement specific servo control, motor actuation and signal acquisition. Communication layer takes responsibility for data exchange between main control layer and actuating layer. Double PCs and every I/O node of controllers are all connected with CAN bus to satisfy high-speed and reliable communication. CAN bus consists of two wires and maintains 1Mbps speed under distance of 40m. These advantages make it possible for humanoid robot to simplify its overall arrangement of wire while keeping its original properties. Because of the whole design concept that regards every controller module as an I/O node, it is very easy to add a motor or sensor so long as the capacity of the network is sufficient. As a whole, the remainder of this paper is organized as follows. The overview of the whole control system is described in Section . Every control layer is specified in this part. In Section , we present the concrete operating process of the most important layermain control layer. Functions of Windows OS and RT-Linux OS are introduced in this part as well as their realization methods. The specific experiment implementation of joint motor testing under Windows OS and real-time control testing on RT-Linux OS is demonstrated in Section . Finally, the conclusion about the properties of the whole system is given in Section . DISTRIBUTED CONTROL SYSTEM ARCHITECTUREA. Overview of the Humanoid Robot The proposed distributed control system based on CAN bus was implemented on our newly-built humanoid robot MIH-I. The humanoid robot shown in Fig.1 was equipped with stereo CCD cameras in the head, torque/force sensors at waist and feet, acceleration sensors and gyro sensors in chest and joint angle detection sensors in order to acquire environmental information and joint data. Its height and weight were 798 mm and 26 kg respectively. It consisted of 25 degree of freedoms (DOF). The configuration and joint angle constraints were shown in Table . MIH-I was developed as a platform for further research of distributed control system and algorithms for walking pattern control.(a)Photo of humanoid robot (b) Joint configuration modelFig.1 The humanoid robotB. Architecture of the Computer Control SystemThe method of centralized management and distributed control was employed in our system. According to architecture and function of this system, it was divided into 3 layers. Main control layer was formed with double OS (operating system) to acquire the advantages of Windows OS and also ensured the stable real-time control. Thereinto, Windows OS aiming at controller debugging and information observation provided friendly man-machine interface and meanwhile RT-Linux OS realized the real-time motion control. Actuating layer was integrated on the robot body to implement specific servo control, motor actuation and signal acquisition. Communication layer took responsibility for data exchange between main control layer and actuating layer. CAN bus was connected between double PC and every I/O node of controllers to satisfy high-speed and reliable communication. The specific architecture of the computer control system was shown in Fig. 2. It realized the synchronous joint motion while keeping the stability of the humanoid robot and optimized the whole control system at the same time.C. Structure of Main Control Layer Two control PCs with different OS (operating system) were recruited in main control layer. This layer produced the series of joint data and meanwhile coordinated every joint to complete the planned motion. The PC with RT-Linux OS was stable and reliable enough to complete the gait planning and walking pattern control. The other PC with Windows OS made it easy to observe statement of every working motor. Since the Windows OS could support functions of multi-media sensors, it has been convenient to debug the joint motor, check the signals sent by motors and sensors. The friendly man-machine interface assured the rapidity and accuracy during handling unexpected emergency. A PCI CAN card with two isolated and independent channels was applied in this layer to enhance anti-interface capability. The SJA1000 chip was built in the CAN card as CAN controller 6. It provided bus arbitration and error detection with an automatic transmission repetition function to reduce the chance of data loss and ensure system reliability.D. Structure of Communication Layer Communication layer transferred the information between main control layer and actuating layer. Main control PCs and all the servo controllers as I/O nodes connected with each other on CAN bus.Table (a) DEGREE OF FREEDOM OF THE ROBOTHead1DOF1=1DOFArmsShoulder3DOF2=6DOFHands3DOF2=6DOFLegsHip3DOF2=6DOFKnee1DOF2=2DOFAnkle2DOF2=4DOF(b) ANGLE RANGE CONSTRAINTS OF LEGSPartsJointMotion Range(Degree)HipYaw-40 +40Roll-30 +30Pitch-40 +65KneePitch-100 0AnklePitch-55 +63Roll-30 +30Fig.2 Humanoid robot motion control system structure diagramThe concrete implementation of the connection was as follows, the double PCs mounted on CAN bus through the PCI CAN card and every servo controller articulated on CAN bus through CAN transceivers. CAN bus was simply composed of twisted-pairs and it adopted short frame transmission to achieve the real-time message with high speed. Communication layer recorded every frame loss and offered error handling function through locating the I/O node which was out of order 2. It was essential to design the CAN communication protocol on application level between main control layer and actuating layer. We resorted to master-slave communication mode and bus arbitration in this protocol design to avoid communication conflict. The two main control PCs acted as master and every servo controller acted as slaves in the mode. The CAN network was in standard format in the case of humanoid robot, so the arbitration field consisted of the 11-bit identifier and the RTR bit. The 11-bit identifier was split into three parts illustrated in Table .Table ALLOCATION OF 11-BIT IDENTIFIER OF CAN FRAMESID bit109-54-0Function0/1Command/Response code(0-31)Slave nodes address(0-9) Since the 10th bit of identifier had the top priority, it was used to indicate the sender of CAN data. The bits from 9 to 5 of identifier were used to indicate the command frames sent to servo controllers. Similarly the bits from 4-0 were used to mark the ID of I/O node which the master wanted to control or the slaves needed to communicate with.E. Structure of Actuating Layer Actuating layer took responsibility for joint servo control of the humanoid robot. To achieve the severe request of real-time and stable circumstance, several intelligent modules were employed to realize the function of actuating layer 7. Every intelligent module was independent and was formed with MCU (micro controller unique), the actuator and the corresponding sensors shown in Fig. 3. Intelligent module analyzed the joint motion data received from main control layer to drive the motors. On the other hand, intelligent module acquired the sensor data which was sent to be utilized by the master. Every intelligent module cooperated with the master to handle unexpected emergency during walking pattern control of the robot. Every joint was controlled by the corresponding intelligent module. DSP2812 was utilized as MCU because of its powerful function integrated inside. The PD control algorithm was uploaded inside MCU to compute data and control the actuator. The QEP (quadrature encoder pulse) circuit integrated in DSP received data from incremental encoder sensors to attain the relative position of the motor. D/A converter was applied to provide analog voltage as command signal.Fig. 3 Block diagram of intelligent module. IMPLEMENTATION OF MAIN CONTROL LAYERA. Design of Control System Based on Windows The software design based on Windows was divided into three layers: denotation layer, data layer and operation layer as shown in Fig. 4 8. Denotation layer was utilized as man-machine interface to set system parameters and working model. It monitored the statement of humanoid robot timely and supported dynamic display function of environmental information. Data layer accomplished the walking pattern planning via the analysis of the data which was reserved from sensors and the environment. Operation layer took the responsibility for command process, task process, track producing and coordination motion control.Overall, operation layer was the core of these three layers. It completed the management of dynamic environmental information and sent reference data of every joint to the robot. Task process module was the scheduling centre that assorted operation layer with other modules. It received instruction from command process module and environmental information from global information module. The history records in data layer could be available to task process module if necessary. Motion control module encapsulated the planned walking array as CAN data packet which was transferred on CAN devices. Sensor signal module acquired and processed information from gyro and other sensors then reflected the accurate result to the monitor.Fig. 4 Software framework of control system based on WindowsB. Design of Control System Based on RT-Linux Main control system based on RT-Linux remedied the defect of Windows OS. Thanks to the RT-Linux, the request of hard real-time control and preemptive model was fulfilled. To assure the stability during walking, RT-Linux OS shortened the control period and kept stationary time lag. The response within limited time and the capability to handle multi tasks at the same time made the robot robust enough to adjust the surroundings in time. RT-Linux as a real-time kernel interfaced at the bottom of Linux OS and allowed Linux tasks to work with the lowest priority. In this way, RT-Linux amended the thread scheduling and interruption process according to Linux OS. Non-real time tasks worked under Linux OSto provide function of Internet communication and meanwhile real-time tasks worked under RT-Linux OS to complete assignment with severe limitation of time. RT-Linux real-time control system was formed with device driver module and real-time control module as shown in Fig. 5.Device driver module supplied the sockets between application programs and CAN bus devices to realize the basic transmission of CAN data. Real-time control module ran under RT-Linux kernel to carry out the major tasks such as transmission of joint angle data, receiving of sensor information and completion of algorithm.Fig. 5 Block diagram of real-time control system based on RT-Linux . EXPERIMENTS The proposed distributed control system based on humanoid robot was experimented on the robot which consisted of two parts: joint motor debugging and real-time system test.A. Joint Motor Debugging To acquire the information of every joint and perceive the surroundings, test based on sensors of vision, angle, electronic compass and encoder was employed in this part. The debugging man-machine interface was shown in Fig. 6 and Fig. 7. Every button of the joints corresponded to every joint motor as presented in the picture. Manipulator tested every joint motor of the humanoid robot respectively and the angle data of the joint was reflected on the monitor according to the planned data. All the joints could also cooperate to finish tasks regardless of the request of precision. The expected sensor data and CAN frames were chosen during the initialization stage of the system whereafter the expected data was processed and displayed by sensor signal module based on Windows. In addition, the sensor signal module handled information of vision and hearing to improve the multi-media function.Fig. 6 Operation interface of motion control moduleFig. 7 Display interface of sensor signal collection and processing moduleB. Real-time System Test To compare the real-time accuracy under different OS platform, the CAN communication baud rate was set to 500 kbps. The property of walking control under Windows OS and RT-Linux OS was recorded while the control period was set to 10 ms as shown in Fig. 8.Fig. 8 Transmission time intervals of joint signals based on Windows or RT-Linux control system Even though the control system based on Windows OS was more convenient in terms of joint motor debugging, it caused too much time lag out of the tolerance of the humanoid robot while the control period was 10 ms. When the control period approached 40 ms, the time interval of sent data was out of control with random time lag. On the contrary, we achieved stationary time lag about 0.12 ms which was accepted by humanoid robot while the control period was 10 ms and the time interval of sent data stayed the same even if the control period added up to 40 ms. CONCLUSION This paper provided a description of a distributed control system based on double PC control architecture for humanoid robot. The principal results of this paper were summarized as follows. 1) A distributed control system with Windows OS and RT-Linux OS was proposed and implemented. This design took advantage of the double PC control architecture to realize the friendly man-machine interface and met the requirements of communication speed, stability, real-time and reliability during the walking pattern control. 2) The software design based on CAN bus for data transmission between real-time system and non-re