Published on January 3, 2008
Task-Oriented Mobile Actuator/Sensor Networks: “Distributed Measurement for Distributed Control” and/or “Distributed Control for Distributed Measurement”?: Task-Oriented Mobile Actuator/Sensor Networks: “Distributed Measurement for Distributed Control” and/or “Distributed Control for Distributed Measurement”? YangQuan Chen Center for Self-Organizing and Intelligent Systems (CSOIS), Dept. of Electrical and Computer Engineering Utah State University E: [email protected]; T: (435)797-0148; F: (435)797-3054 W: http://www.csois.usu.edu/people/yqchen October 22, 2004, CSOIS Bi-Weekly Research Seminar Series Mobile Actuator-Sensor Network(MAS-net): Mobile Actuator-Sensor Network (MAS-net) Tasks Efficiently deploy a group of mobile sensors to characterize the dynamically evolving diffusion boundary Using the same mobility platform, mobile actuators can actively control the formation of the diffusion boundary to a desired zone/shape Application scenarios - MAS-net: Three Application Scenarios: MAS-net: Three Application Scenarios Application Scenario 1 (land): The safe ground boundary determination of the radiation field from multiple nuclear radiation sources. In this case, each networked sensor is mounted on a ground mobile robot. The mission is to determine the safe radiation boundary of the radiation field from possibly multiple nuclear radiation sources. Each robot is actuated according to spatial and temporal sensed information (radiation gradient, spatial position etc.) from more than one actuated or mobile sensors. Application Scenario 2 (water): The nontoxic reservoir water surface boundary determination and zone control due to a toxic diffusion source. Similar to Application Scenario 1 if the toxic diffusion source is a one-time pouring and the diffusion is in steady state. However, the boundary may be dynamically evolving if the toxic source keeps polluting the reservoir. The actuated or mobile sensors are autonomous boats mounted with toxic chemical concentration sensors. The boats are commanded according to the spatial-temporal sensed information from more than one sensor. Furthermore, assume that some of the boats (not all of the boats) are equipped with the relevant neutralizing chemicals to make the water detoxified. By a proper design of distributed sensing and actuation/control strategies, it is possible to control the zone or shape of the toxic region to match the given desirable zone/shape. Now we have a complex distributed feedback control system that is more challenging than the networked actuators and sensors themselves. Application Scenario 3 (air): The safe nontoxic 3D boundary determination and zone control of biological or chemical contamination in the air. This scenario is similar to the above water case, but it is more complicated since 3D space must be explored. Here, the actuated or mobile sensors are unmanned aerial vehicles (UAVs) equipped with concentration detectors and anti-contamination chemical agent(s) distributors. MASNET Experimental Platform(Conceptual Block Diagram): MASNET Experimental Platform (Conceptual Block Diagram) Slide5: Actuated sensors (mote-based robots) take “plume” samples Wireless communication system broadcasts commands to actuated sensors Base station makes plume prediction and computes sensor locations Vision system for locating sensors Air outlet Fog “Contaminant” (orange) introduced into air stream Fan blows air (green) through system 2-D System Testbed Concept MAS-net Platform Development: MAS-net Platform Development System architecture Hardware configuration Robot chassis MICA board & circuit system Camera system Software configuration System diagram pGPS Mote Software MAS-netKey Sub-Systems: MAS-net Key Sub-Systems Mobility Platform (small mobile robots) Sensors on Each Mobile Robot Actuators on Each Mobile Robot Based Station Diffusion Generating Environment MAS-netMobility Platform: MAS-net Mobility Platform Mote Based Control, Wireless Communication, and Interfacing Unit Chassis, Wheel Assembly Servo Encoders IR’s The Test Bed: Motes: The Test Bed: Motes PC GUI Camera Driver Serial Cable Parallel Cable Programming Board Mote (MICA Board) Wireless Communication Motes and Robots Camera Hardware Configuration of the Mobility Platform: MICA2 (Berkeley) Control Board (USU) AVR Atmega 128 (CPU) CC1000 (Comm.) 2 Encoders 3 IR (Sharp GP2D12) 2 Photo- Resistors 2 Servos Sensors 3V Power 6V Power 2ADC 2 PWM 3 ADC 2 ADC Hardware Configuration of the Mobility Platform Software on Mobile Mote : Software on Mobile Mote TinyOS User Applications Low Level Lib 2 Encoders 2 Servos Other Sensors/Actuators Other Utilities of TinyOS 1st Prototype Photos: 1st Prototype Photos Mote-based Robot: USU MASmote With Cover: With Cover Tag on top for pGPS 10 MASmotes: 10 MASmotes The Trend of MAS-net Control: The Trend of MAS-net Control Symbolic+continuous dynamics Distributed, asynchronous, networked environment High-level coordination and autonomy Automatics synthesis of control algorithm Reliable systems made up of unreliable parts -> Huge system modeling and control IEEE Control Systems Magazine 2003 Apr + J.Song Basic Questions: Basic Questions Q1: Given the accuracy requirements, what is the minimum number of robots? Q2: How to drive the robots (differential two-wheels drive and generic nonholonomic) to estimate the fog diffusion. Q3: How to control the robots to eliminate the fog. (optimized with certain criterion) One Problem: Photoresistor (PR): One Problem: Photoresistor (PR) Problem description Large derivative of PR characteristics Max R: 6K~90K Min R: 28 ~120 omega Mapping (by Op Amp analog computation): vo=2( (Rp-5K)/65 ) vi , vi =1.5 Volt Sensor calibration (3 Qs): Sensor calibration (3 Qs) Q4: Calibrate the PRs with the visual information from the camera. After that, the camera is used only for localization. Q5: Very likely, the characteristics of the PR are nonlinear. How to fit? Q6: Reject the background light disturbance effect Optimization : Optimization Q7: What is the relationship between the number of robots and the variance of the sensing errors? Given the cost of a robot and a sensor, together with the sensor characteristics distribution function and the properties of the fog, can you tell me the optimum number of robots and sensors to purchase in order incur the minimum cost? Robust control: Robust control Q8: Infinite dimensional robust control. We have a group of robots to observe an infinite dimensional system (fog). Given a polynomial with interval coefficients as the characteristic of the sensor, what is the minimum number of robots we need? Using the robot control theory to design a H_inf or H_2 controller to observe the system with the minimum number of robots. Interval control: Interval control Q9: Infinite dimensional interval control. Answer the same question in above with the frame work of interval computation theory. Adaptive control: Adaptive control Q10: Infinite dimensional adaptive control. In case the calibration is not possible, for example, a sensor like the camera is not possible, can we design an adaptive controller which does not require calibration at all? If yes, what is the cost of performance? Or, we can assume the calibration is not thoroughly, does the adaptive controller help? The controller need to be adaptive to the (1) slowly changing environment. (2) the assumed time-varying characteristics of each PR. Logic+PDE: Logic+PDE Q11: Since the base station only communicate with MASmote by high-level command, we need to merge logic with PDE at this stage. How to find the minimum set of logics that sufficient for low-level control? What is the minimum sample rate? PDE->Logic Base station Logic->ODE MASmote Communication+real-time: Communication+real-time Q12: (1) Wireless communication collision avoidance. (bad assumptions for CSMA/CA) (2) The max bandwidth for asynchronies delay critical communication. (3) “wireless fieldbus” by CC1000 Comm+Ad-hoc network: Comm+Ad-hoc network Q13: Homogeneous vs. heterogeneous (multi-hop routers) What is the proper ad-hoc network configuration for the best communication performance Routing algorithm: energy+speed Interdisciplinary modeling: Interdisciplinary modeling Q14: robot collusion/exception handling (FSM/DES) + fog estimation (PDE) + robot inverse kinematics (ODE) How about Petri-Net based model fusion? Real-time code automation: Real-time code automation Q15: Like for Gitto, but consider asynchronies ad-hoc network environment. Simulate with “player and stage,” the result should be close to the performance of real hardware platform. (High-level) control algorithm automation: (High-level) control algorithm automation Q16: robocup scenario Strategies learning (centralized or distributed) Run time strategy update at MASmote, or flash memory download update (using XNP) CSP+real-time: CSP+real-time Q17 Port CSP from Java to nesC Automatic (semi-automatic) dead lock, live lock checking (one robot) for nesC Automatic dead lock, live lock checking for heterogeneous robot groups with nesC How to cooperate CSP with ODE/PDE control laws? Fundamental limitations : Fundamental limitations Q18 Characterize the chaos/bifurcation properties of the fog/air flow. What is the limitation of observation? What is the limitation of control? Respect the instability? Ad-hoc network localization: Ad-hoc network localization Q19: rescue robot scenario Unreliable indoor communication Less cost sensors Locate each robot by ad-hoc network. Semi-3D localization. Regional analysis : Regional analysis Regional stability and stabilizability Regional state observer design for DPS (parabolic) Regional detectability Regional gradient observer See A. El Jai and A. J. Pritchard, Sensors and Actuators in Distributed Systems Analysis, Ellis Horwood Series in Applied Mathematics, Ellis Horwood, John Wiley, Chichester, West Sussex: Ellis Horwood, 1988. A. E. Jai, M. C. Simon, E. Zerrik, and A. J. Pritchard, ``Regional controllability of distributed parameter systems,'' International Journal of Control, vol. 62, 1995. M. Amourous, A. E. Jai, and E. Zerrik, ``Regional observability of distributed systems,'' International Journal of Systems Sciences. vol. 25, 1994. Optimal policies: Optimal policies Sensing policy Sensor scheduling Motion planning Actuation policy Actuator scheduling Motion planning Collaborative sensing Collective actuation Research Output so far (08/2003-10/2004): Research Output so far (08/2003-10/2004) Papers published: Kevin L. Moore*, YangQuan Chen, and Zhen Song. "Diffusion-based path planning in mobile actuator-sensor networks (MAS-net): some preliminary results". INTELLIGENT COMPUTING: THEORY AND APPLICATIONS II (OR53). SPIE Defense and Security Symposium 2004. April 12-16, 2004, Gaylord Palms Resort and Convention Center, Orlando, FL, USA. (PDF) SPIE5421-08. (PDF) YangQuan Chen*, Kevin L. Moore, and Zhen Song. "Diffusion boundary and zone control via mobile actuator-sensor networks (MAS-net): challenges and opportunities." INTELLIGENT COMPUTING: THEORY AND APPLICATIONS II (OR53). SPIE Defense and Security Symposium 2004. April 12-16, 2004, Gaylord Palms Resort and Convention Center, Orlando, FL, USA. (PDF) SPIE5421-12. (PDF) Papers published (continued): Papers published (continued) Zhongmin Wang, Zhen Song, Peng-Yu Chen, Anisha Arora, Kevin L. Moore and YangQuan Chen. "MASmote -- A Mobility Node for MAS-net (Mobile Actuator Sensor Networks)". IEEE Int. Conf. on Robotics and Biomimetics (RoBio04), August 22-25, Shengyang, China. (PDF-robio2004-330) Kevin L. Moore* and YangQuan Chen. "MODEL-BASED APPROACH TO CHARACTERIZATION OF DIFFUSION PROCESSES VIA DISTRIBUTED CONTROL OF ACTUATED SENSOR NETWORKS". The 1st IFAC Symposium on Telematics Applications in Automation and Robotics. Helsinki University of Technology Espoo, Finland, 21-23 June 2004. Papers submitted.: Papers submitted. Zhongmin Wang, Zhen Song, Peng-Yu Chen, YangQuan Chen and Kevin L. Moore. "Formation motion control methods in mobile actuator/sensor networks" SPIE Defense and Security Symposium 2005. April 2005 Zhen Song, Pengyu Chen, Zhongmin Wang, Anisha Arora, Yangquan Chen. “MAS-net: a Mobile Actuator-Sensor Network System for Diffusion Observation and Control”, IEEE Communication Magazine. Others: Others Establish a world reputation in sensor-networks with a strong “control”/“closed-loop” flavor IEEE/RSJ Int. Conf. on Intelligent Robotics and Systems. (www.IROS2005.org) Member, Organizing Committee, Invited Session co-Chair Plan: to organize a tutorial workshop on “Task Oriented Mobile Actuator and Sensor Networks” at IROS2005 with other leading players in the field (under planning, going well so far, workshop proposal due March 1, 2005) Invited Talk: Invited Talk 08/17/2004. “Mobile actuator and sensor networks for diffusion boundary determination and zone control”, Invited talk (75 minutes) at the Institute of Intelligent Machines of Chinese Academy of Sciences (IIM of CAS) in Hefei, the capital city of Anhui Province, China. In 2 years: In 2 years CSOIS is the earliest to initiate the research on MAS-net. So far, MAS-net is still unique and novel. CSOIS will still be the leader in this field, specifically: Distributed control of distributed parameter systems using networked moving sensors and moving actuators Dynamic boundary determination/tracking and zonal control using networked moving sensors and moving actuators Regional observation and state reconstruction with networked moving sensors and active formation sampling … my PhD students are working hard on the above theoretical and practical problems. Mind-Storming Session: Mind-Storming Session Demos so far show that pGPS working (yes but) Issues: optimal patterns? Not systematic designs (Lili). Orientation/position accuracy, balanced accuracy? Better lens - $200? LLC servo algorithms (reliable but not accurate) Issue: position loop only. Encoder resolution: 32 sectors. Dan is trying 128. Anisha: better servo motor (with minimum changes, 10/31) Deadzone, quantitative result? (Stiction + PW) Data logging, w/time stamp (send in batch, not on-the-fly) Saturation – (but, integral, we need AW) LFFC helps on servo calibration – (systematic, deterministic, recurrent) – (in need: more automatic calibration procedure). Think about “recalibration state/on demand”. IR (working) Issues: consistency? In need: characterization and then autocalibration. PR (no big confidence now) Issues: ibid. Use pGPS to help on the calibration. Or, use gray-level template. Or buy better PRs (?) GUI commands robots (kind of joystickable) Issues: Real-time grouping, formation nicely. Calibration command (servo, IR, PR), Data Logging etc. Characterization tools. MAS-net Tasks (Demo Scenarios): MAS-net Tasks (Demo Scenarios) Basic Behaviors Obstacle/collision avoidance, E-stop, tracing behavior Collective Behaviors (for what?) Leader-follower, VIP/BG (pattern formation, either static or dynamic – “collective tracing behavior”), formation movement (regulation vs. tracking), … Task-Oriented Behaviors Adaptive spatial sampling, (Anisha: spatial sampling) Task-Oriented Behaviors: Task-Oriented Behaviors “Distributed Measurement for Distributed Control” and/or “Distributed Control for Distributed Measurement”? “Distributed Control for Distributed Measurement”! ? Scenarios: Think about this. Scanning sensor problem in DPS (groups) Periodic scanning sensor problem in DPS (groups) … Task force: Task force Anisha: spatial sampling (open loop) Peng-Yu: pattern formation (static and dynamic) Zhongmin: formation movement (regulatory and tracking), Zhen Song: DPS measurement, system ID and state re-construction using networking mobile sensors. Jinsong: DPS with (networked!) moving sensors and moving actuator. (1D and 2D simulation platforms) Hyosung: TBD.