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Heterogeneous Teams of Modular Robots for Mapping and Exploration Speaker: Hyokyeong Lee Feb 13, 2001 Abstract Design of a team of Heterogeneous and centimeter-scale robots Collaborate to map and explore unknown environments Millibots are configured from modular components

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Heterogeneous Teams of Modular Robots for Mapping and Exploration

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heterogeneous teams of modular robots for mapping and exploration

Heterogeneous Teams of Modular Robots for Mapping and Exploration

Speaker: Hyokyeong Lee

Feb 13, 2001

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
small robots
Small Robots
  • Khepera
  • Ants
  • FIRA&RobotCup
  • 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
  • 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
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
  • 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
modular architecture
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 I2C bus
      • High speed, synchronous clock
      • Two-way data communication line
millibot subsystems
Millibot subsystems
  • Seven subsystems
    • Main processor module
    • Communication module
    • IR obstacle detection module
    • Two types of sonar modules
    • Motor control module
    • Localization module
  • 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
  • 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
  • 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
  • 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
collaborative localization
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
343m s


localization algorithm
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, (x0,y0,0) of all the robots at time t0
  • How determine the position, (x1,y1,1), of the robots at time t1 after they have moved
    • Estimate the new position based on informaton
      • Dead reckoning
      • Distance measurements
dead reckoning
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
distance measurements
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
  • 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
  • Distance measurements
    • The likelihood that the measured distance between two robots i,j is equal to Di,j
  • Total conditional likelihood function is the product of all the conditional likelihoods introduced
  • The most likely robot positions are found by maximizing Ptot with respect to the new robot positions
implementation issues
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
mapping and exploration
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
mapping and exploration34
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
experimental results
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
operation of the team
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
  • Evaluate and compare the performance of the team
  • 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
  • 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