This paper presents a method for computing position and attitude of an instrument attached to the human body such as a
handheld or head-mounted video camera. The system uses two Inertial Measurement Units (IMUs). One IMU is part of
our earlier-developed Personal Dead-Reckoning (PDR) system, which tracks the position and heading of a walking person
relative to a known starting position. The other IMU is rigidly attached to the handheld or head-mounted instrument.
Our existing PDR system is substantially more accurate than conventional IMU-based systems because the IMU is
mounted on the foot of the user where error correction techniques can be applied that are unavailable for IMUs mounted
anywhere else on the body. However, if the walker is waving a handheld or head-mounted instrument, the position and
attitude of the instrument is not known. Equipping the instrument with an additional IMU is by itself an unsatisfactory
solution because that IMU is subject to accelerometer and gyro drift, which, unlike in the case of the foot-mounted IMU,
cannot be corrected and cause unbounded position and heading errors. Our approach uses transfer alignment techniques
and takes advantage of the fact that the handheld IMU moves with the walker. This constraint is used to bound and correct
errors by a Kalman filter. The paper explains our method and presents extensive experimental results. The results
show up to a five-fold reduction in heading errors for the handheld IMU.
This paper describes recent advances with our earlier developed Personal Dead-reckoning (PDR) system for GPS-denied
environments. The PDR system uses a foot-mounted Inertial Measurement Unit (IMU) that also houses a three axismagnetometer.
In earlier work we developed methods for correcting the drift errors in the accelerometers, thereby
allowing very accurate measurements of distance traveled. In addition, we developed a powerful heuristic method for
correcting heading errors caused by gyro drift. The heuristics exploit the rectilinear features found in almost all manmade
structures and therefore limit this technology to indoor use only.
Most recently we integrated a three-axis magnetometer with the IMU, using a Kalman Filter. While it is well known that
the ubiquitous magnetic disturbances found in most modern buildings render magnetometers almost completely useless
indoors, these sensors are nonetheless very effective in pristine outdoor environments as well as in some tunnels and
caves.
The present paper describes the integrated magnetometer/IMU system and presents detailed experimental results.
Specifically, the paper reports results of an objective test conducted by Firefighters of California's CAL-FIRE. In this
particular test, two firefighters in full operational gear and one civilian hiked up a two-mile long mountain trail over
rocky, sometimes steeply inclined terrain, each wearing one of our magnetometer-enhanced PDR systems but not using
any GPS. During the hour-long hike the average position error was about 20 meters and the maximum error was less
than 45 meters, which is about 1.4% of distance traveled for all three PDR systems.
In multi-agent scenarios, there can be a disparity in the quality of position estimation amongst the various agents. Here,
we consider the case of two agents - a leader and a follower - following the same path, in which the follower has a significantly
better estimate of position and heading. This may be applicable to many situations, such as a robotic "mule"
following a soldier. Another example is that of a convoy, in which only one vehicle (not necessarily the leading one) is
instrumented with precision navigation instruments while all other vehicles use lower-precision instruments. We present
an algorithm, called Follower-derived Heading Correction (FDHC), which substantially improves estimates of the
leader's heading and, subsequently, position. Specifically, FHDC produces a very accurate estimate of heading errors
caused by slow-changing errors (e.g., those caused by drift in gyros) of the leader's navigation system and corrects those
errors.
This paper introduces a new approach for precision indoor tracking of tele-operated robots, called "Heuristics-Enhanced
Dead-reckoning" (HEDR). HEDR does not rely on GPS, or external references; it uses odometry and a low-cost MEMS-based
gyro. Our method corrects heading errors incurred by the high drift rate of the gyro by exploiting the structured
nature of most indoor environments, but without having to directly measure features of the environment. The only
operator feedback offered by most tele-operated robots is the view from a low to the ground onboard camera. Live video
lets the operator observe the robot's immediate surroundings, but does not establish the orientation or whereabouts of the
robot in its environment. Mentally keeping track of the robot's trajectory is difficult, and operators easily become
disoriented. Our goal is to provide the tele-operator with a map view of the robot's current location and heading, as well
as its previous trajectory, similar to the information provided by an automotive GPS navigation system. This frees tele-operators
to focus on controlling the robot and achieving other mission goals, and provides the precise location of the
robot if it becomes disabled and needs to be recovered.
KEYWORDS: High dynamic range imaging, Gyroscopes, Linear filtering, Global Positioning System, Control systems, Computing systems, Error analysis, Binary data, Attenuators, Distance measurement
The paper pertains to the reduction of measurement errors in gyroscopes used for tracking the position of walking
persons. Some of these tracking systems commonly use inertial or other means to measure distance traveled, and one or
more gyros to measure changes in heading. MEMS-type gyros or IMUs are best suited for this task because of their
small size and low weight. However, these gyros have large drift rates and can be sensitive to accelerations. The
Heuristic Drift Reduction (HDR) method presented in this paper estimates the drift component and eliminates it,
reducing heading errors by almost one order of magnitude.
KEYWORDS: High dynamic range imaging, Gyroscopes, Global Positioning System, Roads, Linear filtering, Error analysis, Control systems, Attenuators, Video, Temperature metrology
This paper pertains to the reduction of measurement errors due to drift in rate gyros used for tracking the position of
moving vehicles. In these applications, gyros and odometry are often used to augment GPS when GPS reception is unavailable.
Drift in gyros causes the unbounded growth of errors in the estimation of heading, rendering low-cost gyros
almost entirely useless in applications that require good accuracy for more than just a few seconds or minutes. Our proposed
method, called "Heuristic Drift Reduction" (HDR), applies a unique closed-loop control system approach to estimate
drift in real-time and remove the estimated drift instantaneously from the gyro reading. The paper presents results
of experiments, in which a gyro-equipped car was driven hundreds of miles on highways, rural roads, and city streets.
HDR reduced the average heading error over all of these drives by one order of magnitude.
KEYWORDS: Laser range finders, Distance measurement, Mobile robots, Sensors, Calibration, Scanners, Reflectivity, Surface properties, RGB color model, Data modeling
This paper presents a characterization study of the Hokuyo URG-04LX scanning laser rangefinder (LRF). The Hokuyo
LRF is similar in function to the Sick LRF, which has been the de-facto standard range sensor for mobile robot obstacle
avoidance and mapping applications for the last decade. Problems with the Sick LRF are its relatively large size, weight,
and power consumption, allowing its use only on relatively large mobile robots. The Hokuyo LRF is substantially
smaller, lighter, and consumes less power, and is therefore more suitable for small mobile robots. The question is
whether it performs just as well as the Sick LRF in typical mobile robot applications.
In 2002, two of the authors of the present paper published a characterization study of the Sick LRF. For the present
paper we used the exact same test apparatus and test procedures as we did in the 2002 paper, but this time to characterize
the Hokuyo LRF. As a result, we are in the unique position of being able to provide not only a detailed characterization
study of the Hokuyo LRF, but also to compare the Hokuyo LRF with the Sick LRF under identical test conditions.
Among the tested characteristics are sensitivity to a variety of target surface properties and incidence angles, which may
potentially affect the sensing performance. We also discuss the performance of the Hokuyo LRF with regard to the
mixed pixels problem associated with LRFs. Lastly, the present paper provides a calibration model for improving the
accuracy of the Hokuyo LRF.
Serpentine robots are slender, multi-segmented vehicles designed to provide greater mobility than conventional
wheeled or tracked robots. Serpentine robots are thus ideally suited for urban search and rescue, military intelligence
gathering, and for surveillance and inspection tasks in hazardous and hard-to-reach environments. One such serpentine
robot, developed at the University of Michigan, is the "OmniTread OT-4." The OT-4 comprises seven segments, which
are linked to each other by 2-degree-of-freedom joints. The OT-4 can climb over obstacles that are much higher than the
robot itself, propel itself inside pipes of different diameters, and traverse even the most difficult terrain, such as rocks or
the rubble of a collapsed structure.
The foremost and unique design characteristic of the OT-4 is the use of pneumatic bellows to actuate the joints.
These bellows allow simultaneous control of position and stiffness for each joint. Controllable stiffness is of crucial importance
in serpentine robots, which require stiff joints to cross gaps and compliant joints to conform to rough terrain for
effective propulsion. Another unique feature of the OmniTread design is the maximal coverage of all four sides with
driven tracks. This design makes the robot indifferent to roll-overs, which are happen frequently when the slender bodies
of serpentine robots travel over rugged terrain.
This paper describes the OmniTread concept as well as its latest technical features, and an extensive Experiment Results
Section documents the abilities of the OT-4.
This paper introduces a positioning system for walking persons, called "Personal Dead-reckoning" (PDR) system. The
PDR system does not require GPS, beacons, or landmarks. The system is therefore useful in GPS-denied environments,
such as inside buildings, tunnels, or dense forests. Potential users of the system are military and security personnel as
well as emergency responders.
The PDR system uses a small 6-DOF inertial measurement unit (IMU) attached to the user's boot. The IMU provides
rate-of-rotation and acceleration measurements that are used in real-time to estimate the location of the user relative
to a known starting point. In order to reduce the most significant errors of this IMU-based system−caused by the
bias drift of the accelerometers−we implemented a technique known as "Zero Velocity Update" (ZUPT). With the
ZUPT technique and related signal processing algorithms, typical errors of our system are about 2% of distance traveled.
This typical PDR system error is largely independent of the gait or speed of the user. When walking continuously for
several minutes, the error increases gradually beyond 2%. The PDR system works in both 2-dimensional (2-D) and 3-D
environments, although errors in Z-direction are usually larger than 2% of distance traveled.
Earlier versions of our system used an impractically large IMU. In the most recent version we implemented a much
smaller IMU. This paper discussed specific problems of this small IMU, our measures for eliminating these problems,
and our first experimental results with the small IMU under different conditions.
This paper describes the design and performance of the OmniTread serpentine robot, developed at the University of Michigan. Serpentine robots are mobile robots that comprise of multiple rigid segments, connected by actuated joints. The segments usually have drive elements, such as wheels or tracks. To date, we have developed two versions of the OmniTread. The larger version, called OT-8, has five rigid segments and four 2-Degree-of-Freedom (2-DOF) joints, and it can drive through an 8-inch diameter opening. The OT-8 is fully functional and this paper documents experimental results for the OT-8. The smaller and newer version, called "OT-4," will have seven segments, six 2- DOF joints, and it will fit through a 4 inch diameter hole. The OT-4 is not yet completely built, but its design is mostly completed and key improvements over the OT-8 have been bench tested. The foremost and unique design characteristic of the OmniTread is the use of pneumatic bellows to actuate the joints. The pneumatic bellows allow the simultaneous control of position and stiffness for each joint. Controllable stiffness is of crucial importance in serpentine robots, which require stiff joints to cross gaps and compliant joints to conform to rough terrain for effective propulsion. Another unique feature of the OmniTread design is the maximal coverage of all four sides of each segment with driven tracks. This design makes the robot indifferent to roll-overs, which are bound to happen when the long and slen-der bodies of serpentine robots travel over rugged terrain.
Most research on off-road mobile robot sensing focuses on obstacle negotiation, path planning, and position estimation. These issues have conventionally been the foremost factors limiting the performance and speeds of mobile robots. Very little attention has been paid to date to the issue of terrain trafficability, that is, the terrain's ability to support vehicular traffic. Yet, trafficability is of great importance if mobile robots are to reach speeds that human-driven vehicles can reach on rugged terrain. For example, it is obvious that the maximal allowable speed for a turn is lower when driving over sand or wet grass than when driving on packed dirt or asphalt. This paper presents our work on automated real-time characterization of terrain with regard to trafficability for small mobile robots. The two proposed methods can be implemented on skid-steer mobile robots and possibly also on tracked mobile robots. The paper also presents experimental results for each of the two implemented methods.
Most mobile robots use a combination of absolute and relative sensing techniques for position estimation. Relative positioning techniques are generally known as dead-reckoning. Many systems use odometry as their only dead-reckoning means. However, in recent years fiber optic gyroscopes have become more affordable and are being used on many platforms to supplement odometry, especially in indoor applications. Still, if the terrain is not level (i.e., rugged or rolling terrain), the tilt of the vehicle introduces errors into the conversion of gyro readings to vehicle heading. In order to overcome this problem vehicle tilt must be measured and factored into the heading computation. A unique new mobile robot is the Segway Robotics Mobility Platform (RMP). This functionally close relative of the innovative Segway Human Transporter (HT) stabilizes a statically unstable single-axle robot dynamically, based on the principle of the inverted pendulum. While this approach works very well for human transportation, it introduces as unique set of challenges to navigation equipment using an onboard gyro. This is due to the fact that in operation the Segway RMP constantly changes its forward tilt, to prevent dynamically falling over. This paper introduces our new Fuzzy Logic Expert rule-based navigation (FLEXnav) method for fusing data from multiple gyroscopes and accelerometers in order to estimate accurately the attitude (i.e., heading and tilt) of a mobile robot. The attitude information is then further fused with wheel encoder data to estimate the three-dimensional position of the mobile robot. We have further extended this approach to include the special conditions of operation on the Segway RMP. The paper presents experimental results of a Segway RMP equipped with our system and running over moderately rugged terrain.
In order to maneuver autonomously on rough terrain, a mobile robot must constantly decide whether to traverse or circumnavigate terrain features ahead. This ability is called Obstacle Negotiation (ON). A critical aspect of ON is the so-called traversability analysis, which evaluates the level of difficulty associated with the traversal of the terrain. This paper presents a new method for traversability analysis, called T-transformation. It is implemented in a local terrain map as follows: (1) For each cell in the local terrain map, a square terrain patch is defined that symmetrically overlays the cell; (2) a plane is fitted to the data points in the terrain patch using a least-square approach and the slope of the least-squares plane and the residual of the fit are computed and used to calculate the Traversability Index (TI) for that cell; (3) after each cell is assigned a TI value, the local terrain map is transformed into a traversability map. The traversability map is further transformed into a traversability field histogram where each element represents the overall level of difficulty to move along the corresponding direction. Based on the traversability field histogram our reactive ON system then computes the steering and velocity commands to move the robot toward the intended goal while avoiding areas of poor traversability. The traversability analysis algorithm and the overall ON system were verified by extensive simulation. We verified our method partially through experiments on a Segway Robotics Mobility Platform (RMP), albeit only on flat terrain.
This paper introduces a new terrain mapping method for mobile robots with a 2-D laser rangefinder. In the proposed method, an elevation map and a certainty map are built and used for the filtering of erroneous data. The filter, called Certainty Assisted Spatial (CAS) filter, first employs the physical constraints on motion continuity and spatial continuity to distinguish corrupted pixels (e.g., due to artifacts, random noise, or the "mixed pixels" effect) and missing data from uncorrupted pixels in an elevation map. It then removes the corrupted pixels and missing data, while missing data is filled in by a Weighted Median filter. Uncorrupted pixels are left intact so as to retain edges of objects. Our extensive indoor and outdoor mapping experiments demonstrate that the CAS filter has better performance in erroneous data reduction and map detail preservation than existing filters.
KEYWORDS: Computer programming, Current controlled current source, Mobile robots, Error analysis, Gyroscopes, Kinematics, Mars, Manufacturing, Motion controllers, Vehicle control
This paper presents an analysis of odometry errors in over-constrained mobile robots, that is, vehicles that have more independent motors than degrees of freedom.
Based on our analysis we developed and examined three novel error-reducing methods. One method, called “Fewest Pulses” method, makes use of the observation that most terrain irregularities, as well as wheel slip, result in an erroneous over-count of encoder pulses. A second method, called “Cross-coupled Control,” optimizes the motor control algorithm of the robot to reduce synchronization errors that would otherwise result in wheel slip with conventional controllers. The third method is based on so-called Expert Rules. With this method readings from redundant encoders are compared and utilized in different ways, according to predefined rules.
In the work described here we implemented our three error reducing methods on a modified Pioneer AT skid-steer platform and compared their odometric accuracy. The results in this paper point to clear advantages of the Expert Rule-based method over the other tested methods.
Many mobile robots use Polaroid ultrasonic sensors for obstacle avoidance. This paper describes the experimental characterization of these sensors using a unique, fully automated testbed system. Using this testbed, we gathered large data sets of 5,000-16,000 data points in every experiment for characterization purposes; in a repeatable fashion and without human supervision. In the experimental characterization reported in this paper we focused on a comparison of the beamwidth of a single sonar with that of a dual sonar phased array. For the single sonar we found that flat walls trigger echo signals up to an angle of +/- 42 degree(s), which is well beyond the traditional assumed beamwidth of +/- 15 degree(s). We determined that these echoes result from the secondary and tertiary lobe of the well known multi-lobed propagation patterns of Polaroid ultrasonic sensors. In contrast, with the dual sonar phased array echo signals were triggered only up to beamwidths of 4-6 degree(s). The results in this paper were obtained for two test targets: a specular surface and a cylindrical object.
This paper introduces a method for measuring odometry errors in mobile robots and for expressing these errors quantitatively. When measuring odometry errors, one must distinguish between (1) systematic errors, which are caused by kinematic imperfections of the mobile robot (for example, unequal wheel-diameters), and (2) non-systematic errors, which may be caused by wheel slippage or irregularities of the floor. Systematic errors are a property of the robot itself, and they stay almost constant over prolonged periods of time, while non- systematic errors are a function of the properties of the floor. Our method, called the University of Michigan benchmark test (UMBmark), is especially designed to uncover certain systematic errors that are likely to compensate for each other (and thus, remain undetected) in less rigorous tests. This paper explains the rationale for the UMBmark procedure and explains the procedure in detail. Experimental results from different mobile robots are also presented and discussed. Furthermore, the paper proposes a method for measuring non-systematic errors, called extended UMBmark. Although the measurement of non-systematic errors is less useful because it depends strongly on the floor characteristics, one can use the extended UMBmark test for comparison of different robots under similar conditions.
KEYWORDS: Mobile robots, Optical character recognition, Kinematics, Computer programming, Control systems design, Mechanical engineering, Robots, Control systems, Pluto, Actuators
Multi-degree-of-freedom (MDOF) vehicles have many potential advantages over conventional (i.e., 2-DOF) vehicles. For example, MDOF vehicles can travel sideways and they can negotiate tight turns more easily. In addition, some MDOF designs provide better payload capability, better traction, and improved static and dynamic stability. However, MDOF vehicles with more than three degrees-of-freedom are difficult to control because of their overconstrained nature. These difficulties translate into severe wheel slippage or jerky motion under certain driving conditions. In the past, these problems limited the use of MDOF vehicles to applications where the vehicle would follow a guide-wire, which would correct wheel slippage and control errors. By contrast, autonomous or semi-autonomous mobile robots usually rely on dead-reckoning between periodic absolute position updates and their performance is diminished by excessive wheel slippage. This paper introduces a new concept in the kinematic design of MDOF vehicles. This concept is based on the provision of a compliant linkage between drive wheels or drive axles. Simulation results indicate that compliant linkage allows to overcome the control problems found in conventional MDOF vehicles and reduces the amount of wheel slippage to the same level (or less) than the amount of slippage found on a comparable 2-DOF vehicle.
This paper introduces HIMM (histogramic in-motion mapping) a new method for real-time map building with a mobile robot in motion. HIMM represents data in a two-dimensional array (called a histogram gjid) that is updated through rapid continuous sampling of the onboard range sensors during motion. Rapid in-motion sampling results in a statistical map representation that is well-suited to modeling inaccurate and noisy range-sensor data. HIMM is integral part of an obstacle avoidance algorithm and allows the robot to immediately use the mapped information in real-time obstacleavoidance. The benefits of this integrated approach are twofold: (1) quick accurate mapping and (2) safe navigation of the robot toward a given target. HIMM has been implemented and tested on a mobile robot. Its dual functionality was demonstrated through numerous tests in which maps of unknown obstacle courses were created while the robot simultaneously performed real-time obstacle avoidance maneuvers at speeds of up to 0. 78m/sec.
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