ETH Library
Terrain Mapping and Control
Optimization for a 6-Wheel Rover
with Passive Suspension
Conference Paper
Author(s):
Strupler, Pascal; Pradalier, Cédric; Siegwart, Roland
Publication date:
2012
Permanent link:
https://doi.org/10.3929/ethz-a-010034720
Rights / license:
In Copyright - Non-Commercial Use Permitted
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Terrain Mapping and Control Optimization for
a 6-Wheel Rover with Passive Suspension
Pascal Strupler, C´edric Pradalier, and Roland Siegwart
Autonomous Systems Lab, ETH Z¨urich, Switzerland
cedric.pradalier@mavt.ethz.ch
Summary. Rough terrain control optimization for space rovers has become a pop-
ular and challenging research field. Improvements can be achieved concerning power
consumption, reducing the risk of wheels digging in and increasing ability of over-
coming obstacles. In this paper, we propose a terrain profiling and wheel speed
adjustment approach based on terrain shape estimation. This terrain estimation is
performed using sensor data limited to IMU, motor encoders and suspension bogie
angles. Markov Localization was also implemented in order to accurately keep track
of the rover position. Tests were conducted in- and outdoors in low and high fric-
tion environments. Our control approach showed promising results in high friction
environment: the profiled terrain was reconstructed well and, due to wheel speed
control, wheel slippage could be also decreased. In the low friction sandy test bed
however, terrain profiling still worked reasonably well, but uncertainties like wheel
slip were too large for a significant control performance improvement.
1 Introduction
Since the first landing of a rover on the moon in 1970 by the Soviet Union,
these semi-autonomous, mobile explorers enjoy an increase in popularity. In
1997, the first successful rover named Pathfinder rolled over the Mars surface.
On Mars, this is still the only possibility to collect scientific data in such a
mobile and interactive manner. Since space rovers are a relatively new way to
explore extraterrestrial terrain, mission durations still vary a lot, but the latest
missions have been brought to an end due to the rover wheels getting stuck
in sand. The two current Mars rovers Spirit and Opportunity were already
able to stay operational for more than 5 years, which is 20 times the originally
planned mission duration. Nevertheless, they occasionally bogged themselves
down in the sand and Spirit was given up and stays immobile because of this
issue.
One way to reduce this problem is to minimize wheel slip. During wheel
slip the wheels don’t move as far as they are supposed to according to their
2 Pascal Strupler, C´edric Pradalier, and Roland Siegwart
rotational speed. On a sandy surface, this can result in wheels digging them-
selves in. One of the cause of wheel slippage is often wheels fighting each
other because of lack of knowledge about the involved terrain shape. In this
paper, we therefore propose a method to adapt the individual wheel speeds of
a rover according to the terrain profile. This leads to reduced wheel slippage
as well as reduced chances of wheels digging into sandy soil. Because of the
complexity of developing new and advanced sensors for space rover wheels,
our method is based on sensor input of commonly used and reliable rover
sensor technology like IMU (Inertial Measurement Unit), wheel encoders and
angle measurements of the bogie suspension system.
1.1 Related Work
Optimizing rough-terrain control for space rovers is a popular field of research.
One approach by Iagnemma et al. proposes to estimate force distribution on
the wheels by using approximated wheel-ground contact angles ([1] and [2]).
By computing the force distribution of a rover, it is possible to optimize
the torques applied on the wheels and therefore reduce wheel slip and power
consumption. The estimation of the wheel-ground contact angles is done using
simple on-board sensors like IMU inclinometer, joint angle sensors and wheel
encoders. Its accuracy strongly depends on dynamic angle measurements and
therefore no estimation can be computed when the rover is still. Furthermore,
wheel slip and smooth terrain profiles also result in poor wheel contact angle
estimation.
Thus Lamon et al. from ETH Z¨urich developed tactile wheels to measure
these wheel-ground contact angles instead of performing an estimation ([3]
and [4]). This method was first implemented on the rovers Octopus [5] and
Solero [6]. Later it was also applied to the 6-wheel Crab rover [7]. Although
the approach shows promising results [8], embedded wheel sensors are still too
complex and unreliable to be used in extraterrestrial environments.
1.2 Goals and Limitations
Our objective is to develop an alternative approach on reducing wheel slip and
optimizing control of space rovers in rough terrain. In contrast to the work by
Iagnemma et al. mentioned above, our control should also yield good results
in smooth terrain. On the other hand, we want to avoid using tactile wheels
and other complex sensor systems in order to deliver a realistic approach
for current space rovers. Our core idea relies on profiling the terrain shape
using commonly used rover sensors such as IMU, wheel encoders and angle
measurements of the bogie suspension system. The terrain shape can then be
used to achieve wheel speed optimization.
Terrain Mapping and Control Optimization 3
2 Simultaneous Mapping and Control (SMAC)
Our approach proposes a velocity controller based on on-line terrain profil-
ing, called SMAC (Simultaneous Mapping And Control). An overview of this
controller is shown in figure 1. In the state estimation part, the terrain shape
and the rover position are estimated. On one hand, terrain shape estimation
highly depends on the rover position, but on the other hand, the rover po-
sition estimate can be improved significantly by accounting for the terrain
shape in a probabilistic filter. Finally, knowing the terrain and rover posi-
tion, a wheel controller can be proposed which optimizes the wheel speed to
minimize theoretic slippage.
Fig. 1. State estimation and controlling (left), rover model (top right) and profiled
wheel path compared to the real terrain (bottom right).
STATE ESTIMATION
Terrain Profiling
Using front wheel as
cantilever
Markov Localization
Estimating new middle
wheel
?
CONTROLLING
Wheel speed controller
Using profiled terrain and
wheel positions
Updated
Terrain
Updated
Positions
Updated Terrain
Updated Positions
Forward driving direction
x
y
In the following, our implementation is explained using a parallel suspen-
sion bogie rover model, as shown in figure 1. However it is possible to adapt the
controller to other suspension systems by modifying the geometry equations
accordingly. Furthermore, we take the following assumptions:
1. We decouple both rover sides from each other and apply our method to
each side independently.
2. We do not actually profile the real terrain, but the path traversed by the
center of the wheels (see figure 1). From now on the term terrain designates
this wheel center path. Note that recovering the real terrain shape is not
possible due to ambiguities in corners.
3. The rover is assumed to drive straight and does not roll sideways. This
allow reducing the profiling problem to 2 dimensions. As another conse-
quence, all the wheel centers will follow the same terrain path which is
included in a vertical plane in 3D space (designated as wheel movement
plane, also take a look at figure 4 in section 3).
In this paper, we focus straight trajectories as a proof of concept for si-
multaneous mapping and control. This also allows applying the 2 dimensional
4 Pascal Strupler, C´edric Pradalier, and Roland Siegwart
modeling to each side of the rover independently. However, it is clear that con-
sidering curved trajectory would require to consider the full 3D complexity of
the problem, for which we cannot propose a solution at this stage.
2.1 Terrain Profiling
Terrain profiling allows us to approximate the terrain shape and - knowing
the rover position - to optimize the wheel speed. Our objective is to use space-
realistic sensors to achieve this goal: IMU, angle measurements of suspension
bogies and motor encoder readings. First of all, we assume that, lacking vi-
sual or other distant sensing devices, there is no possibility to foreknow the
terrain shape. The latter needs to be profiled in the instant the front wheels
are traversing it. Therefore, these front wheels can be seen as cantilever-based
tactile sensors. They can be used as profiling sensors while the two middle
wheels use the former profiled terrain for propagation estimation. During the
next iteration, the propagated middle wheels acts as new reference points for
the front wheels to profile the next terrain points and so on. Hence, the terrain
can be iteratively built up. This procedure is illustrated in the state estima-
tion part of figure 1. However, one can easily observe that errors in profiling
will accumulate since there are no measurements with absolute reference. To
partially mitigate that, the middle wheel position is estimated through a prob-
abilistic filter that reduces the displacement errors along driving direction and
thus also improves the quality of the terrain profile. This is described in the
next subsection 2.2.
The illustration of a parallel bogie rover in an arbitrary configuration is
shown in figure 1. The front and the middle wheels are connected with a
parallel bogie (to be called front left/right bogie). In the rear view, one can
see that the two back wheels are also connected with a parallel bogie (rear
bogie). A simplified model used for the upcoming computations is illustrated
in figure 2. Note that the parallel bogie-wheel connectors can be disregarded
since we only depend on relative wheel positions.
Fig. 2. The Crabli rover and its simplified rover model with bogie angle ϕ and IMU
angle β (crosses represent wheel positions).
β
l
FB
l'
width
l
MidToBack
φ
In order to profile the terrain at the front wheel, the position of the middle
wheel has to be defined first. Assuming its x-position is iteratively propagated,
