2.1 Dual-Arm Humanoid Robot A dual-arm humanoid robot is actuated by rubbertuators, which are McKibben pneumatic artiæcial muscles as shown in Figure
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1 Integrating Visual Feedback and Force Feedback in 3-D Collision Avoidance for a Dual-Arm Humanoid Robot S. Charoenseang, A. Srikaew, D. M. Wilkes, and K. Kawamura Center for Intelligent Systems Vanderbilt University Nashville, TN 37235, USA fsammy, srikaewa, wilkes, kawamurag@vuse.vanderbilt.edu Abstract This paper presents a simple but eæective method for integrating sensors and actuators in both real and virtual environments using an interactive simulator over the Internet. Our research explores the combination of visual feedback and force feedback to enhance our 3-D collision avoidance approach for a dual-arm humanoid robot. The robot control server uses 3-D vision-based position estimates of obstacles, such asahuman hand or objects on the table, to compute a collision-free path in real-time. The dual-arm robot is capable of performing multiple interactive tasks while avoiding collisions between its own arms or with other objects. The interactive simulator provides visual feedback to the user and sends the user's commands to the robot server and the stereo camera server via the Internet. Thus, it is well-designed for robot teleassistance applications. Force feedback eæects are generated, using an inexpensive commercially available joystick, to enhance the sensation of the real environment while interacting with the simulation. Finally, experimental results using our collision avoidance approach are presented to demonstrate the performance of this system. Keywords: Collision Avoidance, Dual-Arm Humanoid Robot, Interactive Simulator, Color Tracking, 3-D Position Estimation 1 Introduction Teleassistance and virtual reality techniques can provide signiæcant enhancement of current robotic applications. Recent research has been explored techniques of integrating sensors and actuators in virtual and real environments ë1ëë2ëë3ë. This paper discusses a simple but eæective and inexpensive waytointegrate actuators and sensors in real and virtual environments for robotic applications under the PC platform. The 3-D collision avoidance problem is used to illustrate this technique. The architecture of this system uses an object-oriented model and a distributed network-based paradigm. The user is allowed to control a low-cost camera head over the Internet. The camera server will report back to the user how many objects are present along with the locations. The user can use that information to build the virtual scene including the type and the location of each obstacle. An OpenGL-based simulator is used to render the virtual robotic environment atthe user site. This simulator is also connected to the camera control server and the robot control server at the robot site. Multiple clients can connect to the camera server and robot server at the same time. The operation of the robot is based on the ærstcome ærst-serve strategy. When the virtual scene is built, the user can control the positions of the robot arms. The arm server also uses 3-D vision-based position estimates of obstacle, such asahuman hand, to compute a collision-free path in real-time. The simulator will be updated via the network to provide the visual feedback. Also, force-feedback will be sent to the force-feedback joystick at the user site. When the simulator is notiæed that a potential collision is about to occur, it will generate sound feedback to the user. Speech recognition and synthesis are integrated into the simulator to obtain a friendly user interface. The experimental results are presented to demonstrate the performance of the system. 2 System Architecture To allow a user to build a virtual robotic environment at a remote site, various kinds of hardware and software modules are integrated to sense information from the real environment. Multimedia-based feedback such as vision, force and sound are also used to assist the user to intuitively operate a dual-arm humanoid from a remote site. The dual-arm robot used is shown in Figure 1. A simple real-time 3-D collision avoidance algorithm is also implemented for robot control. This system is developed for the PC platform under the Microsoft Windows 95 and Windows NT operating system.
2 2.1 Dual-Arm Humanoid Robot A dual-arm humanoid robot is actuated by rubbertuators, which are McKibben pneumatic artiæcial muscles as shown in Figure 1 ë4ë. This dual-arm robot is a service robot developed to aid the elderly and the disabled, and also for use in holonic manufacturing. A PC-based controller developed in-house is used to control each 6-DOF arm. A 6-axis forceètorque sensor is mounted at the wrist of each arm to acquire the external force and torque exerted on each arm. Figure 1: The Dual-Arm Humanoid Robot and its Simulator 2.2 Interactive 3-D Robotic Simulator A 3-D OpenGL-based robotic simulator is used to render a virtual robotic environment at the user site. 3-D graphical models of the dual-arm humanoid robot, a panètiltèverge camera head and a robotic environment including obstacles are constructed via the network as shown in Figure 1. The simulation of the dual-arm robot is updated by reading the joint angles from the robot control server over the Internet. Similarly, the camera control server updates the angles of the camera head. Moreover, a user can send control commands to the robot control server and the camera control server by using this interactive simulator. This simulator integrates multimedia-based feedback such as force feedback, visual feedback, and sound feedback as follows: A commercial force feedback joystick, the Microsoft SideWinder Force Feedback Pro, is programmed to generate force feedback eæects for the user. When a potential collision is detected or the forceètorque sensor detects some external force data, all force information will be processed and sent to the force feedback joystick to generate eæects. Further, the user can use this 3-D joystick to control the position of the panètiltèverge camera head remotely. æ Force Feedback Joystick Control: æ Speech Recognition and Synthesis: Simple speech recognition and synthesis are integrated to provide the user a natural way to communicate with the simulator. The user can issue pre-deæned voice commands, and speech synthesis is used to prompt and verify for the user's input. æ Sound Feedback: When the potential collision between an arm and an obstacle or between arms is detected, sound feedback will be generated simultaneously with force feedback at the user site. æ Live Video: Live video feedback will be provided for the user to monitor the real environment at the robot site. 2.3 Visual Tracking System The objective of the visual tracking module is to provide information èsuch as positionè about the obstacle to the robot. This tracking system is composed of a camera head with two color cameras, the Connectix color QuickCam, a color image acquisition board and two Pentium PCs for color image processing, obstacle tracking, live video transmission and camera control. Figure 2 shows two color CCD cameras and the color QuickCam mounted on a panètiltèverge system. The obstacle tracker utilizes the 2-D position of the obstacles in the image planes and subsequently controls the camera head to center the obstacle in the camera views. After the target is æxated, the 3-D position can be computed and used by the robot system to ænd the location of the obstacle relative to the robot coordinate system. Details of each module are discussed in the following sections. Figure 2: Camera Head Object Color-Based Tracking The goal of the color segmentation module is to locate an obstacle in a color image and guide the camera head to center it in the camera views. To accomplish this, color models of the obstacle were created. The color segmentation is then performed to separate the pixels into possible obstacle-color pixels and nonobstacle-color pixels by the color model. This color segmentation method was proposed by Barile ë5ë. In the segmentation stage, pixels from the input image are tested to determine if they fall inside a predeæned RGB color model space. From this process, a
3 binary mask image is created for use by later stages. In the binary mask image, the white pixels represent a color that matches the color model and black pixels represent a color that does not match the color model. A color model is created oæ-line by manually segmenting several images of the obstacle. In order to build the virtual scene, information about the workspace needs to be known; for example, the location of a box, a soda can or an orange on the table. Each object is tracked using its color attribute, one objectècolor at a time. Consequently, a color model database is created for storing color information of prospective objects in the workspace, such as, a blue box, a green soda can or an orange grapefruit. Once objectècolor is segmented, the output images are used by the camera tracker in order to estimate the 3-D position of the object. Moreover, after the virtual scene is built, the camera tracker is responsible for tracking a human obstacle such as an arm, or a hand. The color of these obstacles can be considered as a skin-tone color. This then becomes skin-tone color tracking Camera Tracker and 3-D Object Estimator Given the location of the skin-tone centroid in the mask image, the camera tracker then moves the camera head to guide the centroid towards a dead zone in the camera view. The dead zone is deæned to be a circular area in the center of the image view. The size of the dead zone indicates the accuracy of the camera æxation point. The smaller the area of the dead zone, the more accurate. The cameras move according to a direction vector generated by the distance from the skin-tone centroid to the center of the image view. The amount of the cameras' movement is proportional to the magnitude of the direction vector. The y-component of the direction vector controls the tilt motor and the x-component controls the leftèright motors. Once the target has reached the dead zone, the tracker is notiæed that the target has been æxated and then stops the camera head. This behavior-based method allows the system to focus on the robot's attention using active vision. The camera control scheme is shown in Figure 3. Figure 4: Camera Geometry D Collision Avoidance Humanoid robots have a unique opportunity to become useful assistants in homes, hospitals and factory æoors ë4ë. Since they must interact with humans at many levels, safety is a major concern. Collisions between the robots and humans or among the robots should be avoided. There have been several studies on operating multiple robots in an environment containing obstacles ë6ëë7ëë8ë. Most of these systems require complex path planning or only address collision avoidance among robots. Our approach presents a simple 3-D collision avoidance technique that can be applied to collision avoidance between the robot and external obstacles, or among robots ë9ë Multiple-Point Virtual Impedance Control The multiple-point virtual impedance control approach is used for 3-D collision avoidance. In a simple case, a virtual sphere is created to cover the endeæector of the robot arm as shown in Figure 5. Each obstacle is also covered by a virtual sphere. After a potential collision is detected for the virtual sphere on the arm, we can compute the total force exerted on the arm as follows: M e d í Xe + B e d _X e + K e dx e = F ext + F v è1è where M e, B e, and K e are the desired inertia, damp- Figure 3: Camera Control Scheme. Once the left and right cameras have æxated the target, the 3-D position of the obstacle can be determined geometrically. The coordinates of the obstacle in the camera frame can be determined as shown in Figure 4. Figure 5: Virtual Impedance Control Scheme ing, and stiæness matrices, respectively. The deviation dx e is the diæerence between the current position X of the end-eæector and the desired position X d. The
4 velocity and the acceleration of dx e are d _X e and d Xe í, respectively. F v is a virtual force generated when the obstacle is within the virtual sphere on the arm. F ext is an external force acquired by using the forceètorque sensor. With the above equation, we are able to compute a new desired position for controlling the robot's motion. For the current implementation, three virtual spheres are created to cover the elbow, the wrist, and the end-eæector for each arm. When a potential collision between the arm and the obstacle is detected, a virtual force will be generated to push the arm away from the obstacle. Further, the total computed force is used to generate a force feedback eæect on the force feedback joystick at the user site. Figure 6: Basic Component Module 3 Interface Design Since there are several diæerent software and hardware modules in this system, a well-designed interface is needed. Our research addresses a distributed networkbased interface under the PC platform. This interface component provides feasibility tocombine small modules into a large system. A small module is constructed based on the object-oriented paradigm in the Windows environment. Each module consists of a communication interface component and its own functionality such ascontrolling hardware or processing some algorithms as shown in Figure Communication Interface Component A communication interface component provides the underlying interconnectivity for each module in the system. It is the Windows socket interface handler that manages the sending and receiving of data over the network. Thus, it is also called a socket manager, which is multithreading and a message-passing handler. Multiple threads allow asocket manager to send and receive data concurrently and independently. A socket message is a C++ derived object, which can contain diæerent kinds of data such as the robot's position and camera head control command. Further, a command header in the socket message is used to indicate what type of command has been sent and received. After interpreting the command header, data inside the message will be retrieved for further processing. Thus, the socket message is reusable. There are two types of socket managers: server and client socket managers. A server socket manager can handle multiple clients at the same time. A clientsocket manager is responsible for communicating with the speciæed server. 3.2 System Conæguration As described above, each module consists of a communication interface component and its speciæc functionality. Client-server socket managers are used to create a communication link between modules. The conæguration of the current system is shown in Figure 7. Figure 7: System Conæguration of a Dual-Arm Humanoid Robot Robot Control Server This robot control server is responsible for controlling the robot arm and performing 3-D collision avoidance. It sends pressure commands to the robot controller and reads the current robot joint angles at the same time. It also contains a derived object of the server socket manager for communicating with its clients. This robot control server receives socket messages such as the arm joint angles, the position and the size of the virtual obstacles from the clients. Further, it sends the update of the robot joint angles to the clients Camera Head Control Server The camera head control server is responsible for sending and reading the angle commands to and from the panètiltèverge camera head. A server socket manager object is derived to get the joint angles of the camera head from the clients. The current joint angles of the camera head will be sent to its clients through the socket message Vision Manager Module All image processing routines are handled by this module. It communicates with the camera head control server in order to move the camera head and track objects in the image scene. In the scene building phase, a sequence performed by this module is as follows:
5 1. Move the camera head to look at the table within the workspace. 2. Perform color segmentation of current color model. 3. Fixate that object and estimate its 3-D position. 4. Change to the next color model and restart the sequence again. After all color models in a database have been searched, this module sends all of the objects' 3-D positions to the coordination module to begin the next phase. This module is then changed to a free-running mode to perform obstacle èe.g., handè tracking. If there is a hand moving into this workspace, then the camera head æxates, estimates the hand's 3-D position, and sends it to the coordination module as the obstacle information. Figure 8 shows the resulting segmentation of each object from the robot workspace used for tracking. 4.1 Design Mode In the design mode, a user can use a 3-D joystick to control the camera head. Live video of the real environment from the robot site is sent over the network. While the camera head scans the environment, it tracks multiple objects simultaneously. If it detects some objects, information such as the number and the locations of the objects will be sent to the user. The user can use that information to render the virtual scene such as specifying 3-D object models èbox, cylinder, sphereè including their dimensions as shown in Figure 9 èleftè. Further, the user can design 3-D virtual spheres with radii covering those objects as shown in Figure 9 èrightè Figure 9: Virtual Robotic Environment in the Design Mode Figure 8: Robot Workspace and the Resulting Segmentation Coordination Module Since each robot arm has its own reference frame and performs multiple tasks independently and concurrently, a coordination module is needed. This module is used to coordinate the motions of both arms and communicate with other component modules. A 3-D robot simulator with multimedia feedback is integrated into this module. This simulator renders the virtual robot environment from the robot site and displays it at the user site. Two client socket manager objects are also created. The ærst client socket manager is the robot client manager, which is responsible to communicate with the robot control server. The second client socket manager is the camera head control client manager, which is used to communicate with the camera head control server. A server socket manager is established to receive the positions of obstacles from the vision manager. 4 System Operation There are three operation modes to assist the user to build the virtual robotic environment and control the dual-arm humanoid robot over the Internet. 4.2 Preview Mode Since a virtual robotic environment is constructed, the user can a specify task such as the trajectory for each arm. By running the 3-D interactive simulator, the user can preview the result of an operation before operating the real robot. Moreover, the user is allowed to reconægure the system such as the scene model, the starting points and ending points, etc. When a potential collision is detected, some force feedback eæects will be generated on the force feedback joystick. 4.3 Operation Mode Upon the user's satisfaction in the preview mode, the user can send the control commands to operate the robot. During the robot's operation, live video is sent to the user. The 3-D obstacle locator can be used to track an external dynamic obstacle such as a human hand. The robot system is robust enough to cope with the dynamic change in the environment. When a potential collision is detected or an external force is acquired from the force sensor, some force feedback eæects will be generated on the force feedback joystick at the user site. The simulation of the virtual robotic environment is also updated based on the information obtained from the robot control server and the camera head control server. Two experimental sets are established to demonstrate the performance of this system. Figure 10 ètopè shows collision avoidance between the dual-arm robot and æve external obstacles. A virtual force is generated to push the arm away from
6 Figure 10: Collision Avoidance between the Dual- Arm Robot and the External Obstacleètopè, Collision Avoidance between Two Armsèbottomè the obstacle. The second experiment consists of moving the two arms toward each other. The result in Figure 10 èbottomè shows that new trajectories are generated in order to move both arms away from each other after a potential collision is detected. 5 Conclusions This paper presented a simple but eæective and inexpensive way to integrate sensors and actuators in real and virtual environments under the PC platform. With the interactive 3-D simulator, the user can control robots and the camera head and build a virtual scene from the remote site. The advantages of the proposed system are: 1. It can be used in an oæ-line mode as a simulatorètrainer for teleassistance 2. It can be used on-line to implement a multimedia remote teleassisted robotic system. Currently, this technique is implemented on the dual-arm humanoid robot in the Intelligent Robotics Lab at Vanderbilt University. Robotics and Automation Conference, San Diego, CA, May, ë4ë K. Kawamura, D.M. Wilkes, T. Pack, M. Bishay, and J. Barile, ëhumanoids: Future Robots for Home and Factory, "Proceedings of the First International Symposium on Humanoid Robots, Waseda University, Tokyo, Japan, pp , October, ë5ë J. Barile, M. Bishay, M. Cambron, R. Watson, R.A. Peters, K. Kawamura, ëcolor-based Initialization for Human Tracking with a Trinocular Camera System," Proceedings of the Fifth IASTED International Conference on Robotics and Manufacturing, p , ë6ë T. Nagata, K. Honda, and Y. Teramoto, ëmultirobot Plan Generation in a Continuous Domain: Planning by Use of Plan Graph and Avoiding Collisions Among Robots," IEEE Journal of Robotics and Automation, pp. 2-13, February ë7ë M. Fischer, ëeæcient Path Planning Strategies for Cooperating Manipulators in Environments With Obstacles," Proceedings of the 1994 IEEE International Conference on Robotics and Automation, pp , ë8ë B. Cao, G. I. Dodds, and G. W. Irwin, ëimplementation of Time-Optimal Smooth and Collision-Free Path Planning in a Two Robot Arm Environment," Proceedings of the 1995 IEEE International Conference on Robotics and Automation, pp , ë9ë S. Charoenseang, A. Srikaew, D.M. Wilkes, and K. Kawamura, ë3-d Collision Avoidance for the Dual- Arm Humanoid Robot, " to appear in the Proceedings of IASTED International Conference on Robotics and Manufacturing, July References ë1ë C. Cooke and S. Stansæeld, ëinteractive Graphical Model Building using Telepresence and Virtual Reality," Proceedings of the 1994 IEEE Robotics and Automation Conference, San Diego, CA, May, ë2ë Y. Kunii and H. Hashimoto, ëtele-teaching by Human Demonstration in Virtual Environment for Robotic Network System,"Proceedings of the 1997 IEEE International Conference on Robotics and Automation, ë3ë N. E. Miner and S. A. Stansæeld, ëan Interactive Virtual Reality Simulation System for Robot Control and Operator Training," Proceedings of the 1994 IEEE
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