ROBOTIC PLATFORM WITH MULTIAGENT CONTROL SYSTEM AND DISTRIBUTED ARCHITECTURE

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1 SISOM & ACOUSTICS 2014, Bucharest May ROBOTIC PLATFORM WITH MULTIAGENT CONTROL SYSTEM AND DISTRIBUTED ARCHITECTURE Cristian SPIRLEANU 1, Luige VLADAREANU 1, Eugen DIACONESCU 2 1 Institute of Solid Mechanics of the Romanian Academy, Bucharest 2 University of Pitesti, Pitesti ROMANIA cristian.spirleanu@generalserv.ro, luigiv@arexim.ro, eugen.diaconescu@upit.ro Industrial applications using multi-agent technologies are generally not widespread. A great advantage in distributed control of robots is the decentralized execution of the robotic tasks, thus can be valued the attributes of a flexible control architecture. Problems that arise in such systems are related to the portability of the robotic software applications designed for a specific hardware, and also to achieve a precise interaction between software, controller and mechanical systems. One solution seems to be offered by the robotic operating systems (ROS), some of them were already gaining popularity due to the concept of open source. A real time operating system (RTOS) is a multitasking operating system intended for real-time applications and provides facilities to guarantee that deadlines can be met generally (by the software) or deterministically (by the hardware). Because key factors in an RTOS are minimal interrupt latency and a minimal thread switching latency, most of the robotic systems are using an RTOS. In this article we present the infrastructure of our RTOS based robotic platform with the objective to decrease the complexity of applications by using a task-oriented design and to implement a control software architecture with intelligent agents. This robotic platform comprises several components: two manipulators, cameras with serial transmission, digital sensors, accelerometers and robotic controllers running MQX real time operating system. An implementation example for a flexible robotic cell it will be described, together with the application of the leader agent selection algorithm. Key-Words: robotic platform, multiagent control system, robotic operating system, distributed architecture 1. INTRODUCTION Manufacturing cells are examples of control structures that have been typically designed by using hierarchical levels. This approach trends to be inadequate because it does not effectively support the current requirements for these systems, especially related to flexibility, reconfigurability, the reaction time determined by the number and complexity of hierarchical levels. Industrial applications using multi-agent technologies are limited and generally not widespread, especially due to how communication takes place between agents and the heterogeneous set comprising monitoring devices, industrial sensors and actuators [1][2]. However, in distributed control of robots a great advantage is the decentralized execution of operations by system components [2][6][11][20], thus can be valued attributes of a flexible control architecture among which mention modularity, extensibility, fault tolerance and integrability. Problems that arise in such a system are for example the trajectory calculation for two independent robotic arms in order to avoid collisions or writing robot applications[18], which are usually closely tied to a specific manufacturer of robotic components in order to ensure their portability. In the introductory section of the article are presented different robotic platform that implements the concept of robotic operating system, focusing on ROS version which was developed and offered to the scientific community as open source [8][22]. In the second section is summarized RTOS based robotic platform - with multiagent control, distributed architecture and integrated StickOS[10] or MQX[xx]. Communication between JADE-agents[1] and robotic components such as sensors, actuators and cameras, structure and content of the messages in the communication network with modified Modbus protocol[3] are also presented in the second section. The third section describes the

2 Cristian SPIRLEANU, Luige VLADAREANU, Eugen DIACONESCU 134 Floodmax algorithm implementation for leader agent selection in a RTOS based network. In the end of the article are highlighted the benefits of this new approach to robotic platforms and conclusions on future research directions. So far, there are developed some operating systems with robotic associated development environments, one of them beeing focused on their own equipments available on the market such as RDS developed by Microsoft [20][21] that offer free software development tools, while others are available as open applicable solutions for any type of robotic equipment such as ROS developed by WillowGarage[8][22]. A robotic operating system provide libraries and tools for software developers in order to achieve different robotic applications. ROS is part of open source projects, and provides hardware abstraction, device drivers and visualization tools, manage the exchange of messages in the communication between robotic components, manage the assembly of software control modules. Commands that can be used from the ROS command line are those for file system management: rospack/ rosmake/ roscd, to display messsage data structure definitions - rosmsg/rossrv, to run an executable file contained in an archive, for reading and setting operating system parameters - rosparam get/ rosparam set. An example of robots for mobile manipulation using ROS is provided by Fraunhofer IPA, called Care-O-Bot3[25] and designed both for commercial purposes and for scientific research. The robot has two parts, one for handling tasks and the other one for interaction with the user and the environment. Handling tasks are performed using a LWA3 Schunk SDH robotic arm with gripper. In comparison, RTOS based is done on a small scale and using non-specialized components for experimental use in order to implement robotic arm part. User interaction is implemented with a touchscreen console having I/O functions. The main goals of research Care-O-Bot3 robot variant are to provide an open database and also to ensure remote access to the hardware platform. For bus communication between robotic components CAN protocol is implemented, connecting LWA3 Schunk arm, SDH gripper and the console mounted on PRL100 for user interaction and PW90/70 Schunk devices for the zoom and positioning of cameras. Communication software modules used to connect nodes in CAN network are cab_canopen_motor and cab_generic_can along with other ROS adapted modules - libntcan and libpcan. Industrial ROS operating system for robotic industrial applications [26] - is a version that provides support for various famous robotic equipment producers, combining ROS facilities with existing industrial technologies mainly characterized by poor operating security in commercial robot control applications. Care-O-bot3 (Fraunhofer IPA) PR2 (Willow Garage) Cody (Georgia Tech's) Prairie Dog (Corell Lab, UC) HSR (Toyota) Fig. 1 Types of robots that implements ROS Another interesting implementation is the Robotnik-Modular-Arm ROS[27] equipped with Schunk PowerCube hardware modules. Communication between nodes is also CAN type. Compared to CAN, RTOS based implemented modified Modbus protocol claims to ensure the support for legacy robotic devices, especially for the equipment controlled by the PLC. Another variant of mobile manipulator that implements ROS is PR2[15] developed by Willow Garage and having distributed control architecture. For motion control, specialized electronic drive modules for robotic motors were used, connected in an EtherCAT

3 135 Robotic platform with multiagent control system and distributed architecture communication network, implementing a local PI control loop, see figure 2. A mobile manipulator implementation is also conducted by Georgia's Tech Cody[9][28] with mobile base SegwayRMP50 Omni, a vertical linear actuator and a pair of arms with 7DOF and series elastic actuators, as described in[7]. Fig.2 Example for motion control under ROS Control structure uses a balance point controller in Cartesian space, which takes the information provided by the mechanism kinematics estimation module and the force-torque sensor, and acts both on Segway-type mobile base and the arms control modules. ROS was also used with other software modules such as SciPY, ROBOOP and Orocos Kinematics and Dynamics Library (KDL)[30]. PrairieDog CorellLab[22] conducted by the University of Colorado is also based on the ROS operating system, the main components are a irobotcreate mobile basis, a robotic arm with 4DOF CrustCrawler AX-12 type, and a Hokuyo URG-04LX laser sensor to measure distances in robot motion space. HSR is another example of a robot with ROS system developed by Toyota, this robot was designed to assist and support people with disabilities, is equipped with a 3DOF arm and a telescopic rear part, and is controlled via a simple graphical interface with a tablet PC. 2. FLOODMAX DISTRIBUTED ALGORITHM FOR THE ROBOTIC PLATFORM One of the problems into a network with distributed agents is the leader agent selection[11]. It is assumed that an agent is appointed as leader when one and only one agent is defined with true logic state while all others were defined with false logic state. It is assumed that there are several successive stages of communication, and for each round each agent sends to its neighbors the maximum identifier received until that time. The number of successive stages is defined by the diameter of the network, denoted diam(s J ). This algorithm is called FLOODMAX, because maximum identifier is propagated through the network in an incremental way for the communication stages noted s com [i]. In the first stage of communication, the agents that are neighbors to the agent having the maximum identifier will receive a message from him. At the next stage of communication, the neighbors of these agents will receive a message with maximum identifier and the process is repeated for a number of times equal to the diameter of the network, in order to ensure that each agent of the network will receive the maximum identifier. We note the ja the network of agents implemented under JADE with S J = ({1,..n}, E R ). The agents implemented in JADE will have the states w = ( ja ID, ja maxid, ja leader, s com ), where ja ID 1,...,n in initial state ja ID [i] = i, for each i ja maxid 1,..., n, in initial state ja maxid [i] = i, for each i

4 Cristian SPIRLEANU, Luige VLADAREANU, Eugen DIACONESCU 136 ja leader false, true,unknown, in initial state ja leader [i] = unknown for each i s com 0,1..diamS J, in initial state s com [i] = 0 for each i Implemented function for the exchange message between agents is described in programming code is : function msg(w,i) 10 if s com < diam(s J ) then 20 return ja maxid 30 else 40 return null The function implemented to define the leader state is described as : function define_leader(w,y) 10 new_ja ID := max{ ja maxid, maximum identifier from y} 20 case 30 s com < diam(s) : new_lead := unknown 40 s com < diam(s) AND ja maxid = ja ID : new_lead:=true 50 s com < diam(s) AND ja maxid > ja ID : new_lead:=false 60 return (ja ID, new_ja ID, new_lead, s com +1) In figure 5 the execution of Floodmax algorithm is presented, where the network diameter is 4 for a digraph using the ja_com, ja_con and ja_img agents. For the agent having maximum identifier, the assigned colour is blue. Leader agent is coloured in green. It is supposed that a leader agent is already defined from a previous phase. The interactions between agents is represented as a digraph and it is defined within JADE incorrelation with flexible robotic cell task scheduling. When one of the ja_com needs to be leader, the Floodmax algorithm will be initiated, and his neighbour will receive the maximum identifier. Then, the ja_con agent which previously was the leader for one FRC control operation executed from task scheduling will update his status. Finally, the condition s com =3 is achieved and the new leader is declared ja_com agent. Fig.3 FLOODMAX algorithm execution for RTOS based ja_com, ja_con and ja_img agents 3. RTOS BASED ROBOTIC PLATFORM WITH DISTRIBUTED ARCHITECTURE The robotic controller - figure 4, comprises a 32-bit processor, sensors, actuators and modified modbus interface on a single motherboard having an independent power supply. The motherboard is equipped with a Freescale ColdFire microcontroller[13] with 64K bytes 32 bytes of RAM and 512K bytes of flash memory, and provide support for all types of Xtrinsic sensors using modules that can be attached using special connectors. The motherboard can control up to 8 DC servo motors controlled by PWM. In addition to standalone operation mode, the motherboard can be connected to the Tower mechatronic modular system[16], with controller function that can provide interface to all peripheral modules available in the Tower mechatronic system and thus extending the hardware configuration.

5 137 Robotic platform with multiagent control system and distributed architecture This robotic controller has specific development tools in order to facilitate code application writing for the connected sensors and devices, to implement robotic functions as a response to touch, vibration, shock or other stimuli. Control system facilities include general purpose 16-bit resolution timers, 16 bits resolution PWM outputs with less than 1μS pulse width when driving RC-type servo motors, I2C communication in master mode for Xtrinsic sensors such as 3axis accelerometers, touch sensors and 3axis magnetometers, QSPI master communication, UART communication with or without data buffering, interrupt controller, DMA controller, FlexCan controller and DMA timers. In order to use advanced programming options such as OSBDM debugging tool implemented on the motherboard and CodeWarrior IDE are provided. OSBDM gives a complete solution for debugging robotic applications, using flash memory programming, control and runtime evolution monitoring. For example, such applications enables a robot to walk, allowing access to the information provided by various type of sensors and even allowing the reconfiguration of the processor. One such project was written in CodeWarrior environment and presents a demo code used by the robot to step and to access the information provided by its sensors. The aim is to facilitate the emergence of innovative projects as quickly and easily as possible. For RTOS based controller, MCF52259 processor was used, member of the MCF5225x microcontroller family with reduced instruction set computing (RISC) based on the V2 ColdFire[13] microarchitecture. Most of the MCUs from this family are provided with 64 Kbytes of SRAM and 512 Kbytes of Flash memory, 4 timers with 32-bit resolution with DMA request capability, a 4- channel DMA controller, two I2C modules, Fast Ethernet controller, an external Mini-FlexBus interface, and other features designed for common industrial control applications. As described in the user's guied, the StickOS is an entirely MCU-resident interactive programming environment. The editor, compiler, debugger and flash filesystem are all running within the MCU. The user interface is command-line interactive type. VideoCamera with Serial communication Mobile base Fig.4 RTOS based robotic controller and the controlled manipulator The key features are almost instantaneous compiling to intermediate code and syntax highlighting errors, debugging, tracking and program codes sequencing, approximately 95,000 instructions per second when using MCF52559 ColdFire processor, structured programming, managing interruptions for any peripheral device that can generate an interrupt, bitwise operations, random number generator, integrated controls for manipulating CPU peripherals (general purpose inputs and outputs - GPIO, analog digital converters, I2C, serial, USB, Ethernet and Wi-Fi communication drivers, LCD displays, timers, frequency generators, servo control and others) provided support by functions library, file management, and more. For the motion control and manipulator's inertial moments, a 14 bit resolution accelerometer Freescale MMA8451Q-type is used[14]. This provides integrated features that give flexibility in programming by the user, by means of two interruption pins. Integrated functions allow energy optimization because the host processor is not anymore required to carry out the cyclic query work. It provides access to data processed by HP and LP filters thus

6 Cristian SPIRLEANU, Luige VLADAREANU, Eugen DIACONESCU 138 minimizing detection analysis in order to detect shock or quick transition type events. The sensor can be configured to generate interrupts for different inertia in order to preserve a way of working with low power. consumption. Fig.5 The architecture of robotic platform with multi-agent control system Among the main features are supply voltages between 1.95V and 3.6V with selectable measuring range up to ± 2g / ± 4g / ± 8g ODR data output rate ranging between 1.56Hz and 800Hz and a low power consumption current between 6 μa and 165 μa. To synchronize the communication with image processing subsystem, message transfer speed is between 14.4kbps and 115.2Kbps. Each command for communication with the camera contains an identification number (coded on 2 bytes) followed by up to four parameters encoded in one byte. Videocamera commands such as initialization - vcinit(), the acquisition of images - vcget(), video data packet sizing - vcpacksize(), reset - vcreset(), timing and confirmation of reception - vcsync()/vcack(), are implemented[17]. The maximum images resolution is 640x480 using JPEG compression, and the size of the data packet that contains images from the camera can be changed. Synchronization is achieved by vcsync() command followed by a waiting period to confirm the vcack() reception. The agents implemented into each robotic controller comprised by the robotic platform are the agents for communication ja_com, the agents for control ja_con and the agents for image acquisition and processing ja_img. The agents are implemented in JADE running on the PC station, the connection between ja_com agents and ROPMCS controllers is achieved by implementing a modified Modbus protocol [] by using Java programming language and RS485 communication interface connected to one of the COM serial port of the PC station. In the figure 5 it is presented the architecture of RTOS based for a flexible robotic cell comprising 2 or more manipulators equipped with the robotic controller presented in figure 4, and connected along the communication bus. An automated transport line ATL receive as input products which make the subject of quality inspection for different parameters which are programmed from the PC station. The speed of ATL is variable for the zone A and for the zone B, and it is controlled by the ROPMCS controller nearest to frequency converter which control the ATL zone motor. 4. CONCLUSIONS Problems that arise in such a system are the coordination of distributed robotic system components and robot applications writing in order to ensure their portability. Using the robotic operating system concept, RTOS

7 139 Robotic platform with multiagent control system and distributed architecture based robotic platform is aimed to study and solve some of these problems. Focusing on ROS, different types of robots with specific configuration were shown in order to have an overview for the popularity of this operating system. For our platform, interactions between agents that runs inside a JADE development environment is described, and we presented the application of Floodmax algorithm for leader agent selection in RTOS based network with specialised agents for communication, control, image aquisition and processing. Future directions but not limited to them are for implementing ROS like commands using StickOS, aimed to trajectory calculation for two independent robotic arms in order to avoid collisions, by using open source Kinematics and Dynamics Library. REFERENCES [1] E.Diaconescu, C.Spirleanu, Communication solution for industrial control applications with multi-agents using OPC servers, in International Conference on Applied and Theoretical Electricity, 2012 [2] C.Spirleanu, E.Diaconescu, Distributed control system for robotic cells and intelligent building automation using agents, in International Conference on Mathematical and Computational Methods in Science and Engineering pp , 2011 [3] Modbus application protocol specification v1.1b, Modbus IDA, December 28, 2006, on modbus.org [4] L.Vladareanu, I.Ion, E.Diaconescu, G.Tont, L.M.Velea, D.Mitroi, The Hybrid Position and Force Control of Robots with Compliance Function, in Proceedings of the 10th WSEAS International Conference on Mathematical and Computational Methods in Science and Engineering, pg , 2008 [5] P.Kopacek, G.Kronreif and R.Probst, A modular control system for flexible robotized manufacturing cells, in Robotica, vol.17, pp , Cambridge University Press, 1999 [6] S.C. Lauzon, J.K. Mills, and B. Benhabib, An Implementation Methodology for the Supervisory Control of Flexible Manufacturing Workcells, in Journal of Manufacturing Systems Vol. 16/No. 2, 1997 [7] G. Pratt and M. Williamson, Series elastic actuators, in IROS, [8] M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, R. W. Eric Berger, ROS: an open-source Robot Operating System, in Open-Source Software workshop of (ICRA), [9] A.Jain and C.C.Kemp, Pulling Open Doors and Drawers: Coordinating an Omni-directional Base and a Compliant Arm with Equilibrium Point Control, on International Conference on Robotics and Automation - ICRA, pp , 2010 [10] StickOS Basic User's Guide v1.90 [11] F. Bullo, J.Cortes, S.Martınez, Distributed Control of Robotic Networks, in Applied Mathematics Series, Princeton University Press, 2009 [12] T.Laengle, T.C.Lueth, U.Rembold, A distributed control architecture for autonomous robot systems [13] MCF52259 ColdFire Integrated Microcontroller Reference Manual, in Freescale Semiconductor Data Sheet, document Number: MCF52259RM Rev. 4 3/2011 [14] MMA8451Q 3-Axis,14-bit/8-bit Digital Accelerometer, in Freescale Semiconductor Data Sheet, document Number: MMA8451Q Rev. 7.1, 05/2012 [15] PR2 User Manual, in Willow Garage user manuals, 10/2012 [16] Tower System mechatronics board and robot, Freescale Semiconductor, 2012 [17] Serial JPG Camera Module μcam, 4D Systems Data Sheet, 09/2012 [18] T.Reichenbach, Z.Kovacic, Collision-Free Path Planning in Robot Cells Using Virtual 3D Collision Sensors, in Cutting Edge Robotics, 05/2005 [19] M.Dawande, H.N.Geismar, S.P. Sethi and C.Sriskandarajah, Sequencing and scheduling in robotic cells: recent developments, in Journal of Scheduling 8, pg , Springer 2005 [20] K.Johns and T.Taylor, Professional Microsoft Robotics Developer Studio, inwrox Press, 2008 [21] S.Morgan, Programming Microsoft Robotics Studio, Microsoft Press, Redmond, 2008 [22] Robot operating system, on ros.org [23] A.Brooks, T.Kaupp, A. Makarenko, S. Williams, and A.Oreback, Towards component-based robotics, in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 05), pg , Edmonton, 05/ [24] Dave s robotic operating system, 2009, on dros.org [25] Care-o-bot research, on care-o-bot-research.org [26] ROS-Industrial Applying the Robot Operating System to Industrial Applications, on rosindustrial.org [27] Modular Arm available in ROS, on [28] Robotic Cody learns to bathe, on coe.gatech.edu/content/robotic-cody-learns-bathe [29] Ayssam Elkady and Tarek Sobh, RoboticsMiddleware: A Comprehensive Literature Survey and Attribute-Based Bibliography, in Journal of Robotics, article ID , 2012 [30] C.Schlegel, T. Hassler, A.Lotz, and A.Steck, Robotic software systems: from code-driven to model-driven designs, in Proceedings of the International Conference on Advanced Robotics (ICAR 09), pp. 1 8, Germany, 06/2009.