1. Heterogeneous Teams of Modular Robots for Mapping and Exploration Speaker: Hyokyeong Lee Feb 13, 2001

2. Abstract Design of a team of – Heterogeneous and centimeter-scale robots – Collaborate to map and explore unknown environments Millibots are configured from modular components – Sonar and IR sensors,Camera,Communication,Computation,Mobility For mapping and exploration – Critical to know the relative position of each robot – Novel localization system Sonar-based distance measurements to determine the positions of all the robots in the group Occupancy grid Bayesian mapping algorithm to combine the sensor data from multiple robots

3. Introduction Advantages of a team of robots – Load distribution – Smaller, lighter, less expensive robot – Sensing A team of robot –Perceive environment from multiple disparate viewpoints –Task completion by a team of collaborating robots A single robot –A single point of view – Distributed viewpoints Surveillance, monitoring,demining and plume detection

4. Introduction Traditional robots – Designed with broad array of capabilities – Redundant components to avoid system failure – Large,complex,expensive system Robot teams – “Build simple inexpensive robots with limited capabilities that can accomplish the task reliably through cooperation” – Each robot may not be very capable, but team accomplishes useful task – Less expensive robots that are easier to maintain and debug – Each robot is expendable, reliability in numbers

5. Introduction Size of a robot determines its capabilities Hierarchical robot team (Figure 1) – All Terrain Vehicles(ATVs) – Pioneer robots – Medium-sized Tank robots – Centimeter scale Millibots

6. Introduction

7. Introduction All Terrain Vehicles(ATVs) – Range of up to 100 miles – Completely autonomous – Extensive computational power – Transport and deploy groups of smaller robots Pioneer robots – Port-based adaptive agents Allow the team to dynamically exchange algorithm and state information while on-line

8. Introduction Medium-sized Tank robots – Medium-sized, autonomous robots with infrared and sonar arrays – Swivel-mounted camera – On-board 486 computer – Capable of individual mission – Serve as the leader and coordinator for a team of Millibots

9. Introduction Millibots – Small and lightweight robots – Maneuver through small openings and into tight corner to observe areas that are not accessible to the larger robots – Small so less noticeable

10. Milibots Size – Primary factor that determines what a robot can do and where it can go Small robot – Advantage Crawl through pipes, inspect collapsed buildings, hide in small inconspicuous spaces,dramatic impacts for surveillance and exploration task – Disadvantage Limited mobility range, limited energy availability, reduced sensing, communication and computation ability due to size and power constraints Trade-off

11. Small Robots Khepera Ants FIRA&RobotCup

12. Khepera Small size & computing complexity Significant on-board processing Modular Support addition of sensor and processing modules Work alone or communicate and act with other robots Lack a significant feature: self-localization – Operate in an unknown environment – Combine sensor information – Act as a central, cohesive unit Rely on fixed position global sensor or internal dead-reckoning – Ineffective as a deployable set of robots A pair of centimeter sized wheels – Restricts the robot’s clearance to about 3mm

13. Ants Same scale as the Millibots Designed to be used in groups or teams Limited in sensing – Designed to explore reactive social behaviors Not support a real-time communication link Not exchange information necessary to produce maps or models of the environment Built with a fixed architecture to achieve scale – Propulsion, sensing, processing are combined – Addition of new functionality requires complete redesign Inability to localize – Rely on strong light source for orientation and encoders for dead reckoning – Without a means for determining position, little context in which to evaluate data

14. FIRA&RobotCup Small-scale cooperating robots Coordinate to perform complex actions against a coordinated attack Extremely limited in sensing capabilities – Position sensing via a global camera – Little or no sensors on the robots themselves – Blind and unable to respond to real world event without external camera

15. Specialization Specialization – Achieved by exploiting the nature of a heterogeneous team – Instead of all-equipped robot, build specialized robots for a particular aspect of the task – Scenarios Robot team composed of robots with various range and position sensors but only limited computation capabilities Omitting the unnecessary capabilities –Reduction of power, volume, and weight of the robot Disadvantage – Many different robots need to be available to address the specific requirements of a given task

16. Modular Architecture Specialization through modularity optimizes resource – A robot with only mission specific modules Minimum size and cost of the robot – Reduction of unnecessary payload Less weight, less power – Smaller and cheaper Robots in large numbers to achieve dense sensing coverage, team level adaptability, and fault tolerance – Figure 2 Coordination between modules – Dedicated slots Fixed connections that support up to six sensors or actuator modules – Common I 2 C bus High speed, synchronous clock Two-way data communication line

17. Millibot’s architecture and subsystems

18. Millibot subsystems Seven subsystems – Main processor module – Communication module – IR obstacle detection module – Two types of sonar modules – Motor control module – Localization module

19. Communication Essential in a coordinated team Collaborative mapping and exploration – Detailed and abstract information – Not easily conveyed implicitly To provide two-way communications within group – Each robot is equipped with radio frequency transmitter and receiver – Exchange data at 4800 bps at a distance of up to 100 meters – Units based on size and power considerations

20. Sensors A set of ultrasonic sonar modules – Short-range distance information For obstacles between 0 and 0.5m Ideal for work in tight or cluttered areas – Long-range distance information For obstacles between 0.15m and 1.8m Effective in hallways or open office space Potential complication with ultrasonic based sensor – Interference with similar modules on other robots Sonar elements operate at a fixed frequency determined by mechanical construction – Carry infrared proximity module to overcome this problem Provide an array of five tunable, infrared emitter-detector pairs

21. Sensors Still problem in spite of sensor – Anomalies in real situation – Need high bandwidth information during a mission for analysis by a higher level process or operator Camera module as a solution – External mini camera – A small video transmitter included with the module to transmit the raw video signal to an external processor or remote viewing station – Power circuitry Allows the camera and its transmitter to be switched on and off via control signal from the Millibot Resources minimization –Only one receiving station and associated monitoring device is needed per Millibot group Cannot be used continuously due to limited size of the battery

22. Collaboration To know relative position and orientation of the robots Localization method – Dead reckoning Accuracy problems due to integration error and wheel slippage – Camera-based localization Not feasible in small robots – Global Positioning Systems (GPS) Not appropriate for use in small robots that operate mostly indoors – Landmark recognition, map-based positioning Require excessive local computational power and Sensing accuracy on Millibots

23. Collaborative Localization Millibot localization system – Based on trilateration Determines the position of each robot based on distance measurements to stationary robots with known positions – Localization module Utilizes ultrasound and radio pulses Act as both emitter and receiver Figure 3 – Low-cost ultrasonic transducer To produce and detect beacon signals Coverage of 360 degree, measure distance up to 3m with a resolution of 8mm while consuming 25mW Paramount in achieving a localization system at this scale

24. 343m/s 3X10 8 m/s

25. Localization Algorithm Maximum likelihood estimator – Measurements are noisy and missing – Purely geometric approach is over-constrained, does not yield a solution Assumption – We know the position of an orientation, (x 0,y 0,  0 ) of all the robots at time t 0 How determine the position, (x 1,y 1,  1 ), of the robots at time t 1 after they have moved – Estimate the new position based on informaton Dead reckoning Distance measurements

26. Dead reckoning Millibots always move according to “vector commands”, ( ,d) –  :rotation in place over an angle  – d:forward straight-line motion over a distance d  : angle over which the robot rotates while moving forward – Unplanned rotation due to wheel slippage and calibration errors in the controller

27. Dead reckoning

28. Distance measurements Each robot that moved pings its localization beacon to determine its distance to all the other robots – Resulting measurement data provides accurate data to overcome the drift encountered in localization algorithm based on dead reckoning alone

29. Likelihood Assume that dead reckoning data and distance measurements are normally distributed Dead reckoning – The likelihood that a robot moved over Over an angle Distance given initial position final position

30. Likelihood Distance measurements – The likelihood that the measured distance between two robots i,j is equal to D i,j

31. Likelihood Total conditional likelihood function is the product of all the conditional likelihoods introduced The most likely robot positions are found by maximizing P tot with respect to the new robot positions

32. Implementation Issues Optimization of the conditional probability density function – Formulated as a weighted nonlinear least-square problem BFGS nonlinear optimization algorithm When no prior information about robot positions is available, the BFGS algorithm may get stuck in a local minimum – Dead reckoning data provides a good starting point Only a few iterations are necessary to reach optimum – Taking the best-out-of-five randomly initialized runs never fails to find the global optimum Filter the raw measurement data – Obtain good results with the above algorithm Improvement of accuracy of the algorithm by using more than three robot beacons – Median and mean filtering reduced the standard deviation of the distance measurement

33. Mapping and Exploration A group of Millibots can be equipped with similar sensors to cover more area in less time than a single robot Team leader(or human operator) – Utilize the robot’s local map information Direct the Millibot around obstacles Investigate anomalies Generate new paths – Merge the information from several local maps into a single global map Map aids in path planning for the movement and positioning of the team during exploration

34. Mapping and Exploration Occupancy grid with a Bayesian update rule – Produce maps of the environment – Allows the combination of sensor readings from different robots and different time instances – Occupancy value 1: occupied by an obstacle 0: free cell

35. Experimental Results Task to explore and map as much area as possible before the team failed Possible failures included – Loss of localization, loss of battery power, loss of communications For each experiment – Three Millibots equipped with sonar arrays for collecting map information – Two Millibots equipped with camera modules to aid in obstacle identification and provide a level of fault tolerance – All equipped with localization module CyberRAVE – A central control server and a set of distributed GUI – Operator directs the robots by setting goals,querying maps, and viewing live sensor data

36. Operation of the Team First experiment – Test and verify the team’s ability to localize and collect map data Second experiment – At the center right of the hallway which included a cluster of objects against one of the walls – Detect and avoid the obstacles and remained operational for more than an hour – Loss of a camera robot but mission was continued Third experiment – A large number of obstacles which were small and low to the ground making them invisible to the sonar sensors – Camera modules played a significant role Prior to moving any robot, camera scan the area in front of the robot Reduced the exploration speed

37. Merging result

38. Metrics Evaluate and compare the performance of the team

39. Summary Present the design of a distributed robotic system consisting of very small mobile robots Modular fashion to expand the capabilities Novel ultrasound-based localization system – Does not need require any fixed beacons Using Millibots alternatively as beacons and as localization receivers – Can reposition while maintaining accurate localization estimates at all times

40. Critics Ambiguous in path planning by the operator – By intuition? By which ground, can we say that a deviation of 3% in the distance measurement is good? – Is 3% acceptable? In occupancy grid, how is the homogeneous cell size determined? Suggestion – How about using learning and reasoning algorithm to implement fully autonomous system