3.  Description\
\
3a.  Description and State of the Art (SOA)

> The University of Michigan (UM) proposes the development of a highly
> accurate dead-reckoning system for the Mars Rover. This system will be
> implemented through a novel, UM-developed and already successfully
> demonstrated ap-proach called "physics-based sensor fusion." In
> addition to the new sensor fusion approach, other innovative
> improve-ments over the existing state-of-the-art in dead-reckoning are
> proposed. Among these improvements are unique methods for fusing gyro
> and odometry data, accelerometer-compensated odometry, wheel slippage
> detection and correction, and real-time self-calibration for odometry
> parameters.\
> This project will have a total cost of \$499K for the 2.5-year project
> period (8/1/01 - 2/28/04). It will be conducted at the UM Mobile
> Robotics Lab. The Lab is headed by Johann Borenstein, who is also the
> PI and lead researcher for this proposal. Dr. Borenstein has over 17
> years experience with mobile robot dead-reckoning and over 100
> publications on mobile robots, of which over 30 are directly related
> to dead-reckoning.

3b.  Objectives

> The University of Michigan (UM) proposes the development of a
> high-accuracy dead-reckoning system featuring nu-merous innovations
> and unique improvements over the existing state-of-the-art in
> dead-reckoning systems. The overall objective is that at the end of
> the 2.5-year project period UM will have developed, tested, and ported
> to NASA a comprehensive and fully operational dead-reckoning system.
> Accurate dead-reckoning is highly relevant for any mobile robot,
> because it reduces -- although it doesn't eliminate altogether -- the
> frequency at which absolute position updates need to be obtained. For
> the Mars Rover dead-reckoning is particularly important, because, in
> the absence of GPS, absolute position estimates are difficult to
> obtain. The likely alternative, landmark-based absolute position
> measurements, may not be 100% reliable and may require lengthy periods
> of motionlessness.

3c.  Technical Approach

> UM proposes the development of a unique, highly innovative, and
> comprehensive dead-reckoning system for the Mars Rover. The system
> will use commercially available high-quality Inertial Navigation
> System (INS) components, additional accelerometers, and wheel-encoders
> for odometry. While this configuration is common in dead-reckoning
> systems, we believe that the innovative features of our system,
> discussed in more detail below, will make it more accurate than any
> other ground-vehicle dead-reckoning system available to date at any
> price.\
> Many conventional high-end dead-reckoning systems for ground-vehicles
> (typically, mobile robots) appear to be implemented according to a
> common approach: A 6-axes INS is fused with odometry using a Kalman
> Filter technique. Kalman Filters use statistical error models
> \[Tonouchi, et al., 1994; Krantz and Gini, 1996\] to predict the
> behavior of sensor components. We believe that this approach is not
> ideal for odometry, because the statistical models can't represent
> well single "catastrophic" events -- and the encounter of a large bump
> is indeed catastrophic for an odometry-based system.\
> \
> For many years UM's Mobile Robotics Lab has been developing an
> approach that favors in-depth physical understanding of sensors and
> their associated error sources over the statistics-based Kalman Filter
> methods (we do use Kalman Filtering where appropriate, i.e., where the
> best error model is indeed a statistical one, such as in filtering
> noise). Nonetheless, our basic philosophy is that many error
> mechanisms can be defined more specifically and accurately by expert
> reasoning, and experimental data supports this contention \[Chung,
> Ojeda, and Borenstein, 2001\].\
> \
> As an example for the application of our so-called physics-based
> approach consider the already successfully demonstrated Gyrodometry
> method that our lab invented in 1996 \[Borenstein and Feng, 1996\].
> Gyrodometry fuses odometry data with data from a low-cost, high-drift
> gyro, widely thought to be unsuitable for mobile robot navigation. UM
> analyzed this particular sensor combination and found that dominant
> odometry errors, typically caused by driving over bumps or other floor
> irregularities, result in a clearly identifiable signature in the gyro
> data stream. UM then demonstrated that excellent results could be
> obtained by using only odometry (no gyro data) for most of the time,
> and using the (high-drift) data from the gyro only during those brief
> moments of bump-traversal, that would have rendered the odometry data
> erroneous.\
> \
> This example of physic-based reasoning demonstrates the idea of
> capturing and correcting physical problems (here: the problems of
> bump-traversal and high-drift) at their root. It also shows how,
> through careful performance analysis, we can substitute the strength
> of one sensor for the weakness of another. We will highlight this
> approach where applicable in the following discussion of individual
> innovations in our proposed system.\
> \
> 3.1 Physics-based expert system\
> \
> Under a recently completed \$850K DARPA project UM has already built a
> comprehensive dead-reckoning system based in part on the above
> described physics-based approach. This system is fully functional and
> has been demonstrated to provide a typical accuracy of 50 - 100 cm for
> 200 m travel on moderately rugged terrain (an unpaved parking lot).
> This accuracy was achieved with low-cost inertial components (totaling
> under \$3K for the complete system) and odometry with encoders on four
> independent wheels of a small 4-wheel drive/skid-steer mobile
> platform. This system uses a number of our physics-based expert rules
> (i.e., rules derived from the in-depth analysis of each sensor
> component) to fuse readings from redundant sensors. In addition, fuzzy
> logic is used to fuse this data with finer gradation.\
> \
> For the Mars Rover application UM now proposes to upgrade its already
> existing system with high-end, commercially available INS components
> suitable for a Mars mission. UM will then perform further in-depth
> sensor analysis with the goal of defining additional rules for an
> expert system. Extensive experimentation will help us identify and
> define these additional expert rules. The "expertise" or intelligence
> of our current system will be greatly enhanced with additional expert
> rules and we expect to be able to define significantly more expert
> rules than there are in the current system. We will then implement all
> expert rules within the formal framework of fuzzy logic, to allow
> finer gradation among different rules and to express the interrelation
> between different rules. The result from this work will be a reliable,
> carefully tuned expert system that will effectively integrate three
> gyros, accelerometers, and highly optimized odometry.\
> \
> 3.2 Gyrodometry\
> \
> Since we already explained the basic idea of this approach in the
> example above, we will not repeat that explanation here. Of course,
> unlike in the example, for the Mars Rover application we will not use
> a low-cost/high-drift gyro. However, even if high-end gyros are used
> there will be drift and accumulation of errors. This is especially
> true when the Rover travels for extended periods of time. Therefore,
> we propose to develop a modified version of the original Gyrodometry
> method that is effective with a long-duration missions using low-drift
> gyros.\
> \
> \
> 3.3 Wheel slippage detection and correction\
> \
> If more than two encoders can be installed on the wheels of a mobile
> robot, then it is possible to reduce the chance of errors caused by
> wheel slippage. For example, on our modified 4-wheel drive Pioneer AT
> robots we had installed one encoder on each of the four independently
> driven wheels. In such a configuration the odometry software can
> compare data from all four wheel encoders and discard the data from
> the wheel(s) that suffered the greatest odometry error. It is easy to
> determine which wheel(s) produced the most encoder counts during any
> sampling interval because no matter what encounter causes an odometry
> error (e.g., a bump, crack, or wheel slippage) the result is always
> that a wheel encoder measures more wheel turning than what would
> correspond to the actual linear distance traveled.\
> \
> We now propose to develop a comprehensive slippage monitoring and
> correction system. We reason that with four wheel encoders (or even
> more, depending on the final kinematic design of the Mars Rover) it
> will be possible to identify the two encoders that are most likely to
> be error-free in a given sampling interval and use only their readings
> for odometry. In fact, we expect to be able to obtain perfectly usable
> results even if only one single encoder, or two encoders on the same
> side of the platform, produce error free readings, because of the
> fusion with other sensors that can measure vehicle rotation. We will,
> of course, develop this concept for the kinematic configuration of the
> final Mars Rover platform. However, until that configuration is
> decided upon, we will use the 4-wheel/skid-steer configuration of our
> Pioneer ATs.\
> \
> Another improvement to our proposed slippage detection approach is the
> monitoring of motor torques (implemented simply by monitoring motor
> currents). Yet another test for slippage detection is based on
> comparing commanded motor speeds with actual motor speeds. Indeed,
> reliable wheel slippage or bump detection is even more important than
> ex-plained in this section so far because of our novel idea of
> accelerometer-compensated odometry, explained next.\
> \
> 3.4 Accelerometer-compensated odometry\
> \
> The above method is useful if at least one encoder provides good data
> during a sampling interval. However, some cases are possible, in which
> all wheels are slipping. If the amount of all-wheel slippage is large
> then it is still detectable by the above described monitoring of motor
> torques. However, there is no substitute sensor in our existing system
> to provide the missing information on linear displacement.\
> \
> To overcome this problem we want to consider accelerometers as
> substitute sensors. Accelerometers have long been used for computing
> linear displacement of (mostly airborne) vehicles. The well-known
> problem with this approach is that even the slightest bias in the
> accelerometer output will yield large and unbounded drift after
> integration. Attempts to use accelerometers for position estimation on
> ground vehicles have consistently failed to yield satisfactory results
> \[Bar-shan and Kuc, 1995\].\
> \
> We are now proposing to develop a novel method similar to the
> Gyrodometry method explained earlier but applied to accelerometers and
> linear displacement measured by odometry (the Gyrodometry method fused
> rotation measured by odometry with rotation measured by the gyro). Our
> proposed method works as follows: As long as there is no evidence of
> slippage or bump traversal, encoder readings and odometry are used
> exclusively to compute the linear (forward) dis-placement of the
> Rover. Then, when slippage on all wheels is flagged by the slippage
> detection methods explained in Section 3.3 above, odometry-based
> linear displacement estimates are momentarily supplanted by linear
> displacement estimates based on accelerometers mounted in longitudinal
> direction. The result would be the same as that already veri-fied with
> the Gyrodometry method: The drift-problem with the accelerometers
> readings would have virtually no effect because the accelerometer
> readings are used for only a brief period of time.\
> We should emphasize that (just like with the Gyrodometry method) the
> substitution of accelerometer data can com-pensate only for brief
> episodes of all-wheel slippage (i.e., of a few seconds duration).
> Accelerometer compensation will not be effective for prolonged,
> uniform slippage with all wheels at once. Such a condition might exist
> if, for example, the Rover was to travel upward on a sandy, uniform
> slope.\
> \
> 3.5 Real-time self-calibration\
> \
> In earlier work we developed the UMBmark odometry test and calibration
> procedure for systematic odometry errors \[Borenstein and Feng,
> 1996\]. In this work we identified three dominant systematic error
> sources in mobile robots: (1) scaling error, (2) unequal wheel
> diameters error, and (3) uncertainty about the wheelbase error. We
> then developed a calibration procedure that required the mobile robot
> to run repeatedly through a well-defined so-called "bi-directional
> square path." Inserting the return position errors measured after each
> run into a formula yielded correction constants for the three dominant
> systematic error parameters. This calibration procedure for systematic
> errors needs to be done only once, ideally by the manufacturer of the
> vehicle. However, user-introduced changes, such as differing payloads,
> unbal-anced payloads, varying tire pressure, or a collision can render
> the original calibration less accurate.\
> \
> To overcome this problem we are now proposing to develop a novel
> real-time calibration procedure that would re-calibrate all three
> dominant systematic error parameters during each mission. Our proposed
> real-time calibration system would use for reference the absolute
> position information that we expect to be provided periodically by
> absolute position sensors and algorithms, which are yet to be
> developed by other research groups involved in the Mars Rover
> program.\
> \
> One further enhancement to this approach that we will investigate is a
> simple form of terrain classification using accelerometers, to
> distinguish driving surfaces with special characteristics. This
> approach may yield the fully automatic development of a small on-board
> database that stores odometry parameters related to different
> terrains.

3d.  Significance

> The dead-reckoning system proposed here is expected to provide a
> worst-case position accuracy of 20 cm (8 inches) over 100 m travel on
> rugged terrain, thus being significantly more accurate and robust than
> any other dead-reckoning system demonstrated to date. With such high
> dead-reckoning accuracy the Mars Rover could travel over extended
> distances before an new absolute position update is required.