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On-Line Terrain Estimation using Internal Sensors
Debangshu Sadhukhan and Carl A. Moore
Department of Mechanical Engineering
Florida A&M – Florida State University College of Engineering
2525 Pottsdamer Street
Tallahassee, Florida 32310
.
Abstract -The eXperimental Unmanned Vehicle
(XUV) [1] is designed to autonomously navigate over
different types of terrain. The performance of
autonomous navigation improves when the vehicle’s
control system takes into account the type of terrain on
which the vehicle is traveling. For example, if the
ground is covered with snow a reduction of acceleration
is necessary to avoid wheel slip. Previous researchers
have developed algorithms that use vision to categorize
the traversability of the terrain. Others have used
classical terramechanics equations to identify the key
terrain parameters. In this paper we present the
foundation of a novel algorithm, which uses data from
the vehicle’s internal sensors to categorize the type of
terrain being traversed. We believe the algorithm’s
method of “sensing by feeling” will enable us to
qualitatively determine the terrain in real-time without
the use of a vision system.
It is difficult for this algorithm to analyze homogenous
terrains because corresponding artifacts in homogenous
images are hard to detect. Also, since the vision-based
algorithm is dependent on illumination, low light conditions
increase the possibility of incorrect terrain classification
Fig. 1. The Army’s XUV
I. INTRODUCTION AND RELATED WORK
The XUV, shown in Fig. 1, is a Semi-Autonomous
Unmanned Ground Vehicle (UGV) that uses high fidelity
sensors for Reconnaissance, Surveillance, and Target
Acquisition. The goal of current XUV research is to
develop autonomous mobility that enables an UGV to
maneuver over rugged terrain as part of a mixed manned
and unmanned vehicle group. As part of this goal the XUV
must be able to maneuver at speeds higher than traditional
UGVs. High-speed maneuvers necessitate knowledge of the
terrain’s character. Even during low speed maneuvers,
knowledge of the terrain decreases the vehicle’s likelihood
of becoming stuck. Thus the goal of our research is to
provide real-time qualitative knowledge of the terrain.
Other researchers have studied terrain detection for
UGVs. For example, Howard, Seraji, and Tunstel [2,3,4,5]
investigated the use of vision to classify the transversability
of the terrain. They identified roughness, slope,
discontinuity, and hardness as the key terrain traversability
characteristics. Vision algorithms determine the
characteristics based on the brightness levels of
corresponding pixels from subsequent camera images of the
terrain. The algorithm combines the four terrain
characteristics to form a Fuzzy Traversability Index
representing the ease of robot travel over the terrain.
Karl Iagnemma and Steven Dubowsky [6,7] at
Massachusetts Institute of Technology have utilized
Classical Terramechanics equations to perform online
estimation of terrain parameters. The authors identified
cohesion c and internal friction angle φ as the key terrain
parameters. They have developed equations relating these
two parameters of interest to physically measurable
quantities k1 and k2. These physically measurable quantities
are functions of the vehicle’s vertical loading, torque, wheel
angular speed, and wheel linear speed. The equation
relating these physical quantities to the cohesion c and the
internal friction angle φ is of the following form,
 c 
k1 = k2 

 tan φ 
After measuring the physical quantities over a number
of time steps, the above equation is solved for cohesion and
internal friction using the least squares method. Coulomb’s
equation is then used to compute the terrain shear strength,
which the authors state is a good measure of terrain
traversability.
II. PROPOSED ALGORITHM
Our goal is to develop a terrain detection algorithm that
relies on data gathered from the vehicle’s internal sensors.
The algorithm will function in the event of damage to the
robot’s vision system or during low light conditions.
Furthermore, the results from our algorithm can be used as
a crosscheck for the results from the vision based terrain
detection. In our algorithm we use measurements of wheel
slip, vehicle acceleration, and terrain induced wheel noise
as indicators of the terrain type.
As the first step in the algorithm’s development we
performed driving experiments over various terrains using a
consumer SUV. We wanted to observe wheel slip and other
dynamic effects induced by the vehicle-terrain interaction.
As expected, while driving on asphalt we observed
negligible slip; we were able to make steep turns at high
speeds without any gross tire slip. There was considerable
slip while driving on gravel, and we experienced a good
deal of vertical and lateral acceleration as well. We also
heard the distinctive noise of the tires crunching through the
rocks. In the case of grass we observed less slip than on
gravel but more lateral and vertical acceleration. We
believe that this was due to the fact that the grassy surfaces
we chose had not been leveled to the extent that is typical
for surfaces meant for driving such as gravel lots, asphalt,
or concrete. We also identified notable terrain
characteristics while driving over hard packed dirt and
loose pebbles.
Our driving experiments confirmed that besides wheel
slip data, wheel noise and vehicle acceleration are
indicative of the terrain type. We are currently considering
whether a neural network that fuses measurements of these
three reactions can accomplish the terrain detection.
We will implement UGV experimentation using on an
ATRV-Jr mobile robot manufactured by iRobot (Fig. 2). It
comes equipped with a full array of sensors including an
inertial navigation sensor (INS), wheel encoders, sonars,
laser range finder, bump panels, differential GPS, and a
compass.
Fig. 2. IRobot’s ATRV-Jr
We will first consider terrain induced wheel slip. The
longitudinal slip (i) of each wheel is defined as the ratio of
vehicle translation velocity (v) (calculated from INS
measured acceleration) to wheel translation velocity (rw)
(calculated from the wheel encoder signal).
v 

i = 1 −

 rω 
Four slip values (one for each wheel) are averaged to
obtain a single slip value for the vehicle at each time step.
III. SIMULATION RESULTS
A prototype robot was built and tested using the
ADAMS/View vehicle modeling and simulation software
[8]. We conducted a simulation of the robot driving over
four types of terrain including asphalt, dirt, grass, and
gravel. Each type of terrain was represented by different
coefficients of static and kinetic friction. The values chosen
were 1.0 and 0.65 for asphalt, 0.5 and 0.3 for grass, 0.6 and
0.4 for dirt, 0.8 and 0.4 for gravel [9].
Fig. 3. ADAMS/View simulation results: percentage wheel slip on various terrains.
Grass
Sand
Packed Dirt
Gravel
Grass
30 m
Fig. 4. Test bed to measure vehicle-terrain interaction over various terrain
Shown above in Fig. 3 is a plot of the longitudinal
slip verus time on the simulated surfaces. The prototype
model was accelerated from rest to a velocity of 1.25
m/sec in 1.2 seconds. Initially, under high acceleration,
the percentage slip is high. Gradually, as the final speed
is approached, the rate of acceleration decreases and the
corresponding percentage slip values decrease until they
are near-zero at constant vehicle velocity.
IV. CONCLUSION
We have observed in simulation and experimentation
that wheel slip is different for different terrains. We have
also found that both wheel noise, lateral and vertical
vehicle acceleration can be characteristic of the type of
terrain. Our immediate future work will be to develop
computer programs to store sensor data from the
relevant ATRV-Jr sensors. We are in the process of
building a test bed to perform outdoor experimentation,
see schematic in Fig. 4. One thing we hope to learn is
the relationship between robot-terrain interaction and
robot speed. After using slip to classify terrains we will
attempt to make the classifications algorithm more
robust by incorporating data from audio sensors and
accelerometers.
ACKNOWLEDGMENT
We would like to thank E. Collins and P. Hollis of
FAMU-FSU College of Engineering for their support
and assistance. This research was sponsored by the
Collaborative Technical Alliance (CTA) project
sponsored by General Dynamics Robot Systems
(GDRS).
REFERENCES
[1] http://www.arl.army.mil/wmrd/Tech /ugv-both.pdf
[2] A. Howard and. H. Seraji, “Vision-Based Terrain
Characterization and Traversability Assessment”,
Journal of Robotic Systems vol. 18, no. 10, pp. 577587. 2001.
[3] H. Seraji and A. Howard, “Behavior-based robot
navigation on challenging terrain: A fuzzy logic
approach”, IEEE Transactions on Robotics and
Automation, vol: 18, no. 3, pp. 308 –321, 2002.
[4] A. Howard, H. Seraji, and E. Tunstel, “A rule-based
fuzzy traversability index for mobile robot
navigation”, Proceedings of 2001 IEEE International
conference on Robotics and Automation, Seoul,
South Korea, vol. 3, pp. 3067-3071.
[5] E. Tunstel, A. Howard, and H. Seraji, “Fuzzy rulebased reasoning for rover safety and survivability”,
Proceedings of 2001 IEEE International conference
on Robotics and Automation, Seoul, South Korea,
vol. 2, pp. 1413-1420.
[6] Karl Iagnemma and Steven Dubowsky, “Terrain
estimation for high-speed rough–terrain autonomous
vehicle navigation”, Proceedings of SPIE
Conference on Unmanned Ground Vehicle
Technology IV, pp. 256-266, 2002.
[7] Karl Iagnemma, Hassan Shibly, and Steven
Dubowsky, “On-line Terrain Parameter Estimation
for Planetary Rovers”, Proceedings of the 2002 IEEE
International Conference on Robotics and
Automation, Washington, DC, vol. 3, pp. 3142-3147.
[8] http://www.adams.com/
[9] Daniel J. Parkka, Equation Directory for the
Reconstructionist, Institute of Police Technology and
Management, Second Edition, 1996.
[10] J. Y. Wong, Theory Of Ground Vehicles, John
Wiley & Sons, Third Edition, 2001.

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