Heterogeneous teams of modular robots for mapping and exploration
1 / 40

Hyokyeong Lee - PowerPoint PPT Presentation

  • Uploaded on

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

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
Download Presentation

PowerPoint Slideshow about 'Hyokyeong Lee' - Faraday

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Heterogeneous teams of modular robots for mapping and exploration l.jpg

Heterogeneous Teams of Modular Robots for Mapping and Exploration

Speaker: Hyokyeong Lee

Feb 13, 2001

Abstract l.jpg
Abstract Exploration

  • 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

Introduction l.jpg
Introduction Exploration

  • 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

Introduction4 l.jpg
Introduction Exploration

  • 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

Introduction5 l.jpg
Introduction Exploration

  • 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

Introduction6 l.jpg
Introduction Exploration

Introduction7 l.jpg
Introduction Exploration

  • 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

Introduction8 l.jpg
Introduction Exploration

  • 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

Introduction9 l.jpg
Introduction Exploration

  • 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

Milibots l.jpg
Milibots Exploration

  • 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 l.jpg
Small Robots Exploration

  • Khepera

  • Ants

  • FIRA&RobotCup

Khepera l.jpg
Khepera Exploration

  • 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

Slide13 l.jpg
Ants Exploration

  • 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 l.jpg
FIRA&RobotCup Exploration

  • 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 l.jpg
Specialization Exploration

  • 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 l.jpg
Modular Architecture Exploration

  • 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 l.jpg
Millibot subsystems Exploration

  • Seven subsystems

    • Main processor module

    • Communication module

    • IR obstacle detection module

    • Two types of sonar modules

    • Motor control module

    • Localization module

Communication l.jpg
Communication Exploration

  • 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

Sensors l.jpg
Sensors Exploration

  • 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

Sensors21 l.jpg
Sensors Exploration

  • 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

Collaboration l.jpg
Collaboration Exploration

  • 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 l.jpg
Collaborative Localization Exploration

  • 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 l.jpg
343m/s Exploration


Localization algorithm l.jpg
Localization Algorithm Exploration

  • 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 l.jpg
Dead reckoning Exploration

  • 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

Dead reckoning27 l.jpg
Dead reckoning Exploration

Distance measurements l.jpg
Distance measurements Exploration

  • 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

Likelihood l.jpg
Likelihood Exploration

  • 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

Likelihood30 l.jpg
Likelihood Exploration

  • Distance measurements

    • The likelihood that the measured distance between two robots i,j is equal to Di,j

Likelihood31 l.jpg
Likelihood Exploration

  • 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 l.jpg
Implementation Issues Exploration

  • 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 l.jpg
Mapping and Exploration 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 l.jpg
Mapping and Exploration 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 l.jpg
Experimental Results Exploration

  • 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 l.jpg
Operation of the Team Exploration

  • 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

Merging result l.jpg
Merging result Exploration

Metrics l.jpg
Metrics Exploration

  • Evaluate and compare the performance of the team

Summary l.jpg
Summary Exploration

  • 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

Critics l.jpg
Critics Exploration

  • 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