Navigation : EXPO21XX > ROBOTICS 21XX > H22: Mobile Robots Research > University of Michigan
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  • Offer Profile
  • The Mobile Robotics Laboratory develops and prototypes experimental mobile robot systems including innovative mobile robots, obstacle avoidance systems, positioning systems, and robotic aids for the disabled.
Product Portfolio
  • Innovative Mobile Robots

      • MDOF Robot

        • UM's Multi-Degree-of- Freedom (MDOF) vehicle is fully omni-directional (can travel in all directions and rotate at the same time).
        • Unique, patented compliant- linkage absorbs momentary controller errors to avoid wheel slippage.
        • Recovery from actuator failure:
          • Vehicle can be moved and controlled remotely even after a motor, power amplifier, or other critical component fails.
      • CLAPPER

        • UM developed a new method for odometry error correction that was implemented on UM's MDOF vehicle, now named "Compliant Linkage Autonomous Platform with Position Error Correction" (CLAPPER).
        • CLAPPER uses its redundant encoders to constantly measure the relative position and orientation of its two "trucks."
        • Each truck can detect and correct odometry errors in the other truck.
      • OmniMate

        • In 1995 the Oak Ridge National Lab (ORNL) - required a highly accurate mobile robot for their "Mobile Mapper" project.
        • ORNL found that no commercially available robot met the requirements, while UM's CLAPPER came close.
        • HelpMate Robotics Inc. and UM built the first commercial CLAPPER, called OmniMate.
      • OmniTread

      •  The OmniTread serpentine robot is designed to traverse extremely difficult terrain, such as the rubble of a collapsed building.

        The OmniTread can also drive over sand and rocks. It can pass through small holes and climb over tall obstacles.

        • Use of pneumatic bellows for joint actuation. Bellows are powerful, naturally compliant, and take up minimal space.
        • Maximal coverage of all sides of all segments with extra wide moving tracks.
        • Unique pneumatic control method allows simultaneous proportional control of stiffness and joint angles.
        • The "drive shaft spine" is powered by a single electric motor in the center segment. The spine runs through the center of all segments and provides torque to all tracks.
      • Segway RMP

        • We received from the DARPA MARS program a Segway Robotics Mobility Platform (RMP)
        • We equipped the Segway RMP with our precision FLEXnav proprioceptive* position estimation (PPE) system.
          *) "Proprioceptive" means "without external references"
        • We equipped the Segway with obstacle avoidance capabilities.
    • Obstacle Avoidance

        • CARMEL

        • CARMEL is the oldest mobile robot at UM; since 1987.

          Test bed for developing VFH obstacle avoidance method.
          • VFH is widely used.
          • VFH compensates for inaccuracies of ultrasonic sensors.
          • We believe VFH to be the fastest obstacle avoidance method demonstrated to date.
        • EERUF

          • Problem with multiple sonars: One sensor picks up echo from other sensors; registers false reading.
          • • Most systems wait for long time before firing next sonar Þ slow travel speeds.
          • • UM developed patented Error Eliminating Rapid Ultrasonic Firing (EERUF).
          • • With EERUF sonars can fire fast, producing data for obstacle avoidance at travel speed of 4 feet/sec.
        • OmniNav

          • OmniNav is a new method that provides obstacle avoidance for non-point, omnidirectional mobile robots.
          • • Problem is more difficult than obstacle avoidance for point-like robots.
          • • UM is currently investigating the feasibility of a method based on multiple VFH "act-on" points.
        • AisleNav

          • • UM developed a new approach to navigating a mobile robot through narrow aisles.
          • • Key requirement: entry, exit, and travel within the aisles be 100% collision-free
            • • Requirement hard to meet with sonars, because of specular reflections and crosstalk causing false range readings.
          • • UM's solution based on:
            • • optimized location of sensors
            • • only accurate radial range used readings for servoing.
            • • inaccurate range readings used only for "yes/no" decisions.
        • Tacitical Mobile Robots

        • In 1998 we ported our VFH/EERUF obstacle avoidance technology to so-called Tactical Mobile Robots (TMRs).
          • In this DARPA-funded project we developed highly reliable obstacle avoidance methods for mobile robots operating in hostile environments.
        • Micro-controller Interface Board

          • In the course of the TMR project it became apparent that a small-footprint hardware implementation of the EERUF method would be of great benefit to small mobile robots.
          • • Consequently we implemented EERUF and other functions on a board the size of a floppy disk.
          • Because of strong interest from other mobile robotics researchers, we have made the MCIB commercially available.
        • Obstacle Avoidance Testbed

          • Comprises a computer-controlled motor-driven 4 meter linear motion table and a stationary pan/tilt table.
          • Objects (=obstacles) can be hurled toward an obstacle avoidance sensor (e.g., sonar) mounted on the pan/tilt table (or vice versa).
          • Allows for accurate reproduction of dynamic obstacle detection conditions, under fully controlled, dynamic conditions.
          • Unique testbed, likely only one of its kind in the U.S.
      • Mobile Robot Positioning

          • UMBmark

          • UM developed benchmark test for odometric accuracy of mobile robots, called "UMBmark."
            • UM tested six different vehicle configurations with UMBmark:
              1. TRC LabMate, differential drive.
              2. Cybermotion K2A synchro drive.
              3. CLAPPER MDOF vehicle.
              4. Remotec Andros, tracked vehicle.
              5. Andros with encoder trailer.
              6. Smart Encoder Trailer (simulation).
          • Calibration

          • UM developed novel method for calibration of mobile robots
            • Method based on UMBmark.
            • Applicable to differential drive vehicles (e.g., LabMate).
            • Reduces systematic errors by one order of magnitude.
            • Method can be applied fully automatically.
            • Method tested and confirmed by other researchers.
          • Gyrodometry

            • Other problem: bumps or cracks on the ground can cause large errors in odometry.
            • UM is developing new methods to reduce such "non-systematic" errors.
            • Recent UM development: "Gyrodometry." Uses data from gyroscope to detect and correct non-systematic errors.
          • Navigation Book

            • In 1994 ORNL requested a report on mobile robot positioning methods.
            • In response, UM surveyed the literature, talked to companies, and produced the most comprehensive survey of its kind, the "Where am I" report.
            • Because of its general importance, the UM report was:
              • published in book form,
              • published on CD-ROM,
              • and posted, in its entirety, on the Internet.
          • Gyro calibration

          • UM developed method for accurate calibration of
            fiber-optic gyroscopes.
            • Provides 10 times greater accuracy than manufacturer's original gyro.
            Image description:
            • TOP:
              Gyro calibration: Typical gyro output errors with the manufacturer's original calibration
            • BOTTOM:
              Errors are reduced by more than one order of magnitude after applying UM's new calibration method.
          • High-accuracy Dead-reckoning System

          • Key features of HADRS
            • Works on any wheeled or tracked ground vehicle.
            • Works on any terrain, including rugged, steeply inclined terrain.
            • Requires only a power connection and wheel/track encoder data from the host vehicle. Optionally, system can use encoder data from our retrofittable add-on encoders, making it independent of host vehicle encoders.
            • Works without GPS, computer vision, beacons, or any other external references but can accommodated and integrate optional external GPS.
            • Is indifferent to environment: indoors, outdoors, urban, or rural.
            • Is small-sized and fits into 1/3 Packbot payload bay (rev.2, under development, is even smaller: 6.23x2.87x3.25 inches).
            • Is designed for minimal power consumption.
            • Outputs JAUS compatible, time-stamped X, Y, Z, and heading data stream at 10 Hz.
            • Outputs data via Ethernet, WiFi, RF, or long-range RF data modem.
            • Is unaffected by extreme operating temperatures or sudden changes in operating temperatures.
          • Leader-Follower Navigation

            • Purpose:
              • Enable a mobile robot to follow a walking human leader without GPS and without line-of-sight.
            • Principle of operation:
              • Leader wears our Personal Dead-reckoning (PDR) system.
              • Follower has dead-reckoning capability (typically: IMU & Odometry).
              • The two dead-reckoning systems communicate via RF modems.
              • Our Leader-Follower method synchronized both dead-reckoning systems.
            • Expected Performance:
              • Follower tracks leader for unlimited duration in any environment.
              • Follower may lag behind leader by 2-60 seconds. No line-of-sight needed.
          • Precision Indoor Tracking of Tele-operated UGVs

            • Purpose:
              • To facilitate tele-operation of UGVs by providing tele-operator a GPS-like visual trajectory when UGV is driven inside buildings.
            • Principle of operation:
              • Uses low-cost gyro together with UGV�s built-in odometry.
              • Uses our patent-pending Heuristics Drift Elimination (HDE) method to eliminate effects of all gyro errors, including gyro drift.
              • Does not require GPS, RF beacons, or fiducials.
            • Performance:
              • Zero heading errors in runs of unlimited duration!*
              • Average position errors <1% of distance traveled!*
          • NASA Mars Rover 2009

            • NASA-funded project aimed at developing a high-accuracy dead-reckoning system for the Mars Rover 2009 mission.
            • We built "Fluffy," a fully functional 1/2-scale clone of the NASA Fido-class Mars Rovers.
            • We implemented our Fuzzy Logic Expert Rule-based navigation (FLEXnav) method on Fluffy.
            • We optimized the FLEXnav system for the unique wheel slippage conditions on sandy soil.
            • Main Innovation:
              Wheel slippage detection and correction by slippage monitoring
          • Heuristic Drift Reduction

            • Many vehicle tracking systems use GPS as the primary sensor and a gyro plus odometry as a secondary sensor. The latter helps compute the vehicle�s position during GPS outages.
            • The problem with this approach is that low-cost gyros have high drift rates, which result in large heading errors after just a few seconds or minutes.
            • Heuristic Drift Reduction (HDR) estimates the momentary bias drift of the gyro based on tested and proven heuristics, and then subtracts that estimated drift from the gyro's output.
            • The result is a dramatic reduction of the effective drift, which results in up to two orders of magnitude lower heading errors!
            • HDR requires no hardware at all. It is a small software segment that can be added to any existing program code.
            • HDR works with any gyro or IMU; it is most effective with low-cost/high-drift gyros.
        • Pedestrian Tracking

            • Indoor Pedestrian Tracking

            • Principle of operation: Foot-mounted IMU

              • Records and transmits the position of a walking or crawling person.
              • Eliminates effect of accelerometer drift with every step.
              • Uses patent-pending Heuristic Drift Elimination (HDE) algorithm to eliminate effects of MEMS gyrossensitivity to linear acceleration and drift.
              • Works with walking/jogging/crawling/skipping forward/backward/sideways/any direction.
              • Average heading errors near zero in walks of unlimited duration!
              • Average position errors <1% of distance traveled in walks > 30 min
              • Average elevation (Z-axis) errors: < 1 m.
            • Outdoor Pedestrian Trackin

            • Principle of operation: Foot-mounted IMU

              • Records and transmits the position of a walking person.
              • Does not require GPS.
              • Does not require beacons or any other external reference.
              • Eliminates effect of accelerometer drift with every step.
              • Does not require user-specific calibration*.
              • Records and (wirelessly) streams position data in real time.
              • Average position error during the 2-mile ascent: ~0.5% of distance traveled.
              • Largest position error during the 2-mile ascent: < 40 meters.
            • Indoor Segway Tracking

            • Heuristics-enhanced Odometry (HEO) eliminates odometry heading errors when used indoors, in structured environments.

              • Does not require any sensors other than wheel encoders for odometry.
              • Does not require beacons or any other external reference.
              • Works in real time, requires only 20-30 lines of C code.
              • Provides near-zero heading errors in drives of unlimited duration and distance*.
              • Provides position error <1% of distance traveled*.
          • Robotics Aids for the Disabled

              • NavChair

                • UM developed "NavChair" for severely disabled users.
                  • Some users cannot control their wheelchair accurately with a joystick, because of tremor or other limitations.
                • Obstacle avoidance on NavChair overcomes these problems:
                  • User gives general direction of travel with joystick; NavChair follows this direction.
                  • When obstacle is encountered, NavChair steers around it while trying to maintain user-specified direction as closely as possible.
              • NavBelt

                • Similar to NavChair approach, UM developed "NavBelt" for the blind.
                • Uses UM's obstacle avoidance:
                  • • instead of issuing steering signals to the robot controller, NavBelt generates acoustic cues conveyed to the user via headphones.
                • NavBelt's limitations:
                  • Required hundreds of hours of training before users could respond to the acoustic cues in time, even at slow walking speeds.
              • GuideCane

                • Promising solution to guidance of the blind: UM's "GuideCane" with ultrasonic sensor-based obstacle avoidance
                • Steers around obstacles
                  • steering action is immediately felt by the user, who then follows the altered path prescribed by the steered sensor head.
                • Main advantages of the GuideCane:
                  • Fully automatic obstacle avoidance
                  • Completely intuitive operation, requires no training at all.
                  • Maintains position information by combining odometry, compass, and gyroscope data.