Telechargé par meryasm13

carterpillar sinusoidal oscillator

publicité
Hindawi Publishing Corporation
Advances in Mechanical Engineering
Volume 2014, Article ID 259463, 12 pages
http://dx.doi.org/10.1155/2014/259463
Research Article
Analysis and Implementation of Multiple Bionic Motion
Patterns for Caterpillar Robot Driven by Sinusoidal Oscillator
Yanhe Zhu, Xiaolu Wang, Jizhuang Fan, Sajid Iqbal, Dongyang Bie, and Jie Zhao
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150000, China
Correspondence should be addressed to Jizhuang Fan; fanjizhuang@hit.edu.cn
Received 21 February 2014; Accepted 22 April 2014; Published 21 May 2014
Academic Editor: Yong Tao
Copyright © 2014 Yanhe Zhu et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Articulated caterpillar robot has various locomotion patterns—which make it adaptable to different tasks. Generally, the researchers
have realized undulatory (transverse wave) and simple rolling locomotion. But many motion patterns are still unexplored. In this
paper, peristaltic locomotion and various additional rolling patterns are achieved by employing sinusoidal oscillator with fixed
phase difference as the joint controller. The usefulness of the proposed method is verified using simulation and experiment. The
design parameters for different locomotion patterns have been calculated that they can be replicated in similar robots immediately.
1. Introduction
The Almighty God created many chain-type creatures such as
caterpillar, snake, and earthworm. By utilizing their inherent
special structure, they can adapt to various environments
through multiple locomotion patterns [1, 2]. For instance, a
caterpillar can move in transverse-wave motion to run fast or
peristaltic motion to sneak through a narrow hole.
The robotics community has been trying to make artifacts to exploit the movement mechanism of these versatile
animals. Many specially designed caterpillar robots have
been realized. Besides, the modular self-reconfigurable robot
(MSRR) composed of many building blocks can be used to
construct diverse types of robots, including caterpillar robot.
Several architectural groups are classified according to the
geometrical arrangement of MSRR units: chain-type, latticetype, and hybrid-type [3, 4]. Chain-type and hybrid-type
robot fits for application of coordinated locomotion.
Transverse-wave locomotion is realized on many articulated chain-type caterpillar robots, as in [5–11]. In these studies, different kinds of transverse-wave forms are employed,
including sine-based wave. But peristaltic motion is not realized on articulated caterpillar robots. Only robots utilizing
special material like SMA (shape memory alloy) or special
structure that mimic the stretch characteristics of caterpillar
and earthworm [12–17] realize this motion pattern. Besides,
many implemented rolling motions based on chain-type
robot are hand-planned [17] and lack generality.
Transverse-wave and peristaltic motion can adapt to
different environments, but to the best of our knowledge
no researcher has yet achieved both two motion patterns in
articulated chain-type robot. In this paper, simple sinusoidal
oscillator with fixed phase difference is employed as the
joint controller for achieving transverse-wave and peristaltic
motion. Additionally, multipattern rolling motion like ellipse,
triangle, and other polygon rolling is planned using the same
controller. Figure 1 illustrates some motion patterns suggested
in this paper. Motion patterns from top down are transversewave motion similar to caterpillar and millipede; peristaltic
motion like earthworm; and various rolling patterns beyond
the capability of animals like armadillo.
This paper is organized as follows: Section 2 introduces
the caterpillar robot model and its simple controller. Section 3
presents the details of unified planning method for many
bionic motion patterns. The experimental results are shared
in Section 4. Discussion and future work are presented in
Section 5. Finally Section 6 concludes the paper.
2. Caterpillar Robot and Simulator
2.1. Caterpillar Robot. Caterpillar robot, also known as
“worm-robot” or “snake-robot,” is an articulated chain-type
2
Advances in Mechanical Engineering
Figure 1: Multipattern locomotion examples.
Z
J10
P10
···
···
···
···
J00
Y
X
P00
1
2
i
i+1
n−1
n
Figure 2: Kinematics model of UBot module and topology of composed caterpillar robot.
robot. Its joint axes are perpendicular to its motion direction
and parallel to ground. Our investigated caterpillar robot is
composed of UBot modules [18, 19], which are hybrid-type
MSRR. Figure 2 shows kinematics model of UBot module
and caterpillar robot. Each UBot module has two rotary
degrees of freedom. Each joint can rotate ranging from −90∘
to 90∘ . For caterpillar robot, each module uses only one
joint perpendicular to body line from head to tail. It can be
recognized as a planar linkage mechanism for analyzing its
kinematics.
2.2. Controller. Sine-based controller, model-based controller, and CPG (central pattern generation) are often
employed to generate rhythmic locomotion for robots.
Model-based controller relies on careful analysis and is often
piecewise function to keep motion shape. Corresponding
model should be carefully designed for certain motion
patterns. Examples like triangular wave and trapezoidal
wave can be seen in [16, 20]. CPG-based controller is very
useful for smoothing gaits transition which depends on
communication between modules. If just using CPG-signal
without communication coordination, we do not see any
advantage in controlling modular robot compared with sinebased controller.
Sine-based controller is easy to implement and can mimic
lots of rhythmic motion patterns. In this paper, sine-based
controller with certain phase-lag is used on each module; see
(1). Offset, amplitude, and phase difference are the same for
all the modules. Thus only three design parameters need to
be designed: Offset, 𝐴, and Δ𝜙. Consider
𝜃𝑖 (𝑡) = Offset + 𝐴 ∗ sin (𝑤𝑡 + 𝑖 ∗ Δ𝜙) ,
Offset ∈ [−90∘ , 90∘ ] ,
Advances in Mechanical Engineering
3
module is replaced by another module, the robot moves a
distance of 𝑠. There are 𝑚 − 1 replacement processes in a
period of time 𝑇. Consequently, when 𝑚 is the same, the
higher the waveform is, the faster the robot runs. Here the
robot speed can be written as V⃗ = ((𝑚 − 1) ∗ 𝑠)/𝑇. Generally
the greater the amplitude 𝐴 is, the higher the speed is.
The waveform resembles a sine curve if 𝑚 is big. But
considering the geometry and load capacity of actual module,
there should be less than ten modules in a complete waveform
(it varies for different robot). To make the replacement of
grounding modules successful, there should be more than
four modules in a full waveform.
According to the above analysis, motion planning
method for caterpillar robot in sine-waveform can be set as
Parameters
set panel
Figure 3: UBotsim—3D dynamics simulator.
𝜃𝑖 (𝑡) = Offset + 𝐴 ∗ sin (𝑤𝑡 + 𝑖 ∗ Δ𝜙) ,
𝐴 ∈ [0, 90∘ − |Offset|] ,
Offset = 0∘ ,
Δ𝜙 ∈ [0, 𝜋] ,
(1)
where 𝑖 is module ID and ID increases from head to tail.
Offset is oscillatory center of joint angle. 𝐴 is signal amplitude
relative to Offset. Δ𝜙 is the phase difference (or phaselag) between adjacent modules. Thus joint signals are the
same with an identical phase difference. A variety of novel
motion patterns can be achieved by manipulating the three
parameters.
2.3. UBotsim Simulator. To quickly verify the effectiveness
of the proposed strategy, a dynamics simulator is required.
Many leading MSRR researchers have developed their own
simulator to customize the analysis and evaluation. A newly
developed 3D dynamics simulator UBotsim is used to test
the method. It is based on PhysX engine and OGRE (object
oriented graphics rendering engine). Figure 3 shows a screenshot of UBotsim. A parameter panel is designed to tune
planning parameters, which is strictly related to the joint
controller (1).
3. Planning of Multiple Bionic
Motion Patterns
3.1. Wave Patterns. In wave motions patterns, drive signal
vibrates at the joint offsetwhich is always set as 0∘ .
3.1.1. Undulatory Motion Like Transverse Wave. In this pattern, robot shape looks like a sine curve during motion
procedure (as shown in Figures 4(a), 4(b), and 4(c)). The
phase difference Δ𝜙 is determined by the number of modules
in a complete waveform. Suppose there are 𝑚 modules in a
waveform (here count modules on head and tail of a complete
waveform as 2; below is the same); the equation can be
achieved as Δ𝜙 = 2𝜋/(𝑚 − 1). In other words, Δ𝜙 determines
the number of modules in a sine-waveform. If Δ𝜙 is identical,
the greater the amplitude 𝐴 is, the higher the waveform height
will be.
Figure 4(d) shows module has displacements both in
horizontal and vertical directions. When right grounding
(2)
𝐴 ∈ [0, 90∘ ] ,
2𝜋
Δ𝜙 =
, 𝑚 ≥ 4.
𝑚−1
Two types of sine-wave locomotion when 𝑚 takes
different values are discussed below.
Caterpillar-Like Locomotion. This locomotion has obvious
arches. To simulate a caterpillar, there should be certain
number of modules in a full-waveform. In this pattern,
the emergence of crest and trough is inevitable. Figure 5
illustrates the simulation screenshot that a caterpillar robot
composed of 16 modules moves in transverse wave. The
shape of the robot looks like the caterpillar. The parameters
are set as 𝐴 = 30∘ and Δ𝜙 = 𝜋/4 (𝑚 = 9).
Millipede-Like Locomotion. If 𝑚 = 4 (Δ𝜙 = 2𝜋/3), the
number of modules in a complete waveform is fewest. In
this situation, waveform does not resemble the sine curve.
The number of grounding modules is at its maximum. Robot
movement looks like a millipede. As it is unable to shape
a high arch, amplitude 𝐴 can be set to the maximum to
increase motion speed. Figure 6 is a simulation screenshot of
millipede-like locomotion when 𝐴 = 90∘ .
As the amplitude increases, waveform height will also
increase. This may lead to collision between modules. An
instance for 𝐴 = 80∘ can be seen in Figure 7. A question is
what the biggest value of 𝐴 is for specific 𝑚. In numerical
simulation of kinematics, if any distance between module
centers at time 𝑡 is less than the threshold 1.414∗ModuleLength, corresponding 𝐴 is recognized as the biggest amplitude for corresponding 𝑚. This condition guarantees that
collision would not happen throughout motion procedure.
To make the motion process stable, the ratio between
wave-height ℎ and wave-length 𝜆 should be small, as shown
in Figure 8. If the ratio is too small, robot locomotion is not
efficient and robot moves slowly. Combining stability and
speed, amplitudes located in
ℎ
≤ 0.6
𝜆
are set as preferred amplitudes scope.
0.2 ≤ ratio =
(3)
4
Advances in Mechanical Engineering
1 t = t0
2 t = t0 + T/12
𝜃(𝜙)
𝜃(𝜙)
Δ𝜙
Δ𝜙
A
A
𝜙
𝜙
−A
−A
𝜆
𝜆
(a)
(b)
s
3 t = t0 + T/6
𝜃(𝜙)
Motion
direction
Δ𝜙
A
𝜙
−A
𝜆
1
2
3
(c)
(d)
Figure 4: Locomotion mechanism of caterpillar robot in sine-waveform.
Figure 5: Caterpillar-like locomotion of 16-module robot.
Distance between
module centers
Figure 7: Collision example.
Figure 6: Millipede-like locomotion of 16-module robot.
The biggest and preferred amplitudes are calculated using
numerical simulation. This is implemented in MATLAB as
follows. Firstly, set the metrics of corresponding biggest and
preferred amplitudes. Then set coordinates of module ID0
as (0, 0) and compute each module’s coordinate in time
𝑡 (𝑡 ∈ [0, 𝑇]). According to the coordinates information,
we can calculate whether corresponding amplitude fulfills
the metrics condition. Here evaluated amplitude values are
integers from 0∘ to 90∘ for the sake of reducing computation
time.
Advances in Mechanical Engineering
5
90
h
Amplitude (deg)
80
70
60
50
40
30
20
𝜆
Figure 8: Waveform parameters.
4
5
6
7
8
9
Number of modules in a complete wave
Biggest amplitude
Ratio ≤ 0.6
Ratio ≤ 0.5
10
Ratio ≤ 0.4
Ratio ≤ 0.3
Ratio ≤ 0.2
Figure 9: Amplitude design reference in sine-waveform motion.
Figure 9 illustrates the results; red line is the biggest
amplitude. Other lines represent biggest amplitude when
ratio is less than a specific number. Combining the design
formula (2), this can be a reference graph for planning sinewave locomotion for series of caterpillar robot.
3.1.2. Peristaltic Motion Like Longitudinal Wave. If Δ𝜙 = 𝜋,
the robot just expands and contracts its body because joint
angles of adjacent modules are opposite. The robot expands
its body when all joint angles are 0∘ and contracts its body
when all joints are at amplitude (±𝐴). But the whole body
cannot move.
If 2𝜋/3 < Δ𝜙 < 𝜋, segment of adjacent modules
can also expand and contract because adjacent module
angles are approximately opposite. Meanwhile there are
both expanding and contracting segments across the whole
body. As time passes, expanding segment and contracting
segment swap states to make robot move like a longitudinal
wave.
Figure 10 shows joint angle and robot shape states at
two time instants when Δ𝜙 = 11𝜋/12, 𝐴 = 90∘ . When
𝑡 = 𝑡0 , the neighborhood segment of joint angle equal to
0∘ (blue area) is in expanding state, and center distance of
adjacent modules projected in motion direction is large and
shapes the sparse part of longitudinal wave. The area of joint
angle equal to −𝐴∘ (red area) is in contracting state, center
distance of adjacent modules projected in motion direction
is small, and this segment shapes dense part of longitudinal
wave. When 𝑡 = 𝑡0 + 𝑇/4, joint of 0∘ rotates to 𝐴∘ and joint
−𝐴∘ to 0∘ ; expanding and contracting states switch. Dense
part is useful for support robot and sparse part for transfer
modules. This is a little analogous to the peristaltic motion of
earthworm.
The module whose joint angle is at ±𝐴 is the center
of dense part and the module whose joint angle is at 0∘
is the center of sparse part. In a period of 𝑇, a module
can be the center of dense and sparse part twice. Modules
between adjacent dense (or sparse) centers create a complete
longitudinal waveform. The phase difference of two adjacent
dense (or sparse) centers is 𝜋. Suppose there are 𝑚 modules in
a complete waveform. This is accumulated by 𝑚−1 controller
deviation of Δ𝜙 from 𝜋; that is, (𝑚 − 1) ∗ (𝜋 − Δ𝜙) = 𝜋.
Consequently, peristaltic motion design formula is achieved
as
𝜃𝑖 (𝑡) = Offset + 𝐴 ∗ sin (𝑤𝑡 + 𝑖 ∗ Δ𝜙) ,
Offset = 0∘ ,
𝐴 ∈ [0, 90∘ ] ,
𝜋
Δ𝜙 = 𝜋 −
,
𝑚−1
(4)
𝑚 > 4.
The larger the amplitude 𝐴 is, the denser the contracting
part will be. Owing to the wave height is small, amplitude 𝐴
could have a bigger value without concerning about module
collision. To make a robot move in complete waveform, 𝑚
should be less than the robot module number. When 𝑚 is
larger than 6, the longitudinal effect becomes apparent.
Earthworm-Like Locomotion. An earthworm can move forward by stretching its special body structure, as shown in
Figure 11(a) [21]. Using above method, this type of locomotion
can be simulated in articulated chain-type robot, as shown in
Figure 11(b). The simulation screenshots are a half-𝑇 motion
when 𝐴 = 90∘ and Δ𝜙 = 11𝜋/12. It can be seen that the robot
moves by alternately exchanging dense part and sparse part
just like the earthworm.
3.2. Closed-Loop Rolling. Technical artifacts can surpass
locomotion abilities of natural creatures; for example, a
caterpillar robot can roll in a loop. The caterpillar robot
composed of UBot MSRR modules can form a loop by
connecting head and tail module. More than five modules
are needed to make a loop suitable for motion. In a rolling
loop, the sum of exterior angles must be 360∘ . Figure 12
illustrates the exterior angles of a polygon. Joint angle shapes
the exterior angle.
6
Advances in Mechanical Engineering
𝜃(𝜙)
A
Δ𝜙
𝜙
−A
Motion direction
t = t0
𝜆
(a)
𝜃(𝜙)
A
Δ𝜙
𝜙
−A
Motion direction
T
t = t0 +
4
𝜆
(b)
Figure 10: Peristaltic locomotion mechanism of articulated caterpillar robot in longitudinal waveform.
(a)
(b)
Figure 11: Peristaltic mechanism of earthworm [21] and a simulation based on 16-module articulated caterpillar robot.
𝜃4 (t)
𝜃5 (t)
𝜃6 (t)
𝜃3 (t)
𝜃2 (t)
..
.
𝜃n (t)
𝜃1 (t)
Figure 12: Conceptual model of locomotion in closed-loop type.
Advances in Mechanical Engineering
7
(a)
(b)
(c)
Figure 13: Elliptical states correspond to different values of amplitude.
(a)
(b)
(c)
Figure 14: Rolling in polygon shape.
To keep the loop closed during the locomotion, joint
angles must obey the following formula:
Δ𝜙 =
360∘ = 𝜃1 (𝑡) + ⋅ ⋅ ⋅ + 𝜃𝑖 (𝑡) + ⋅ ⋅ ⋅ + 𝜃𝑛 (𝑡)
+ ⋅ ⋅ ⋅ + sin (𝑤𝑡 + 𝑖 ∗ Δ𝜙)
+ ⋅ ⋅ ⋅ + sin (𝑤𝑡 + 𝑛 ∗ Δ𝜙)} .
(5)
The expression value in the braces should be zero all the time.
Then (6) can be drawn as follows:
𝑛
𝑚 = 2, 3, 4, . . . , [ ] ,
2
(6)
∘
𝑛 ∗ Offset = 360 ,
where [𝑛/2] is an integer less than or equal to 𝑛/2. Consequently, design formula of rolling pattern driven by sine
oscillator is:
𝜃𝑖 (𝑡) = Offset + 𝐴 ∗ sin (𝑤𝑡 + 𝑖 ∗ Δ𝜙) ,
Offset =
360∘
,
𝑛
2𝑚𝜋
,
𝑛
𝑛
𝑚 = 2, 3, 4, . . . , [ ] .
2
(7)
= 𝑛 ∗ offset + 𝐴 ∗ {sin (𝑤𝑡 + Δ𝜙)
𝑛 ∗ Δ𝜙 = 2𝑚𝜋,
𝐴 ∈ [0, 90∘ − Offset] ,
(i) If 𝑚 = 2, robot shape looks like an ellipse. In this
pattern, the larger 𝐴, the lower the robot center of
mass. Figure 13 shows an example of motion shape
when 𝐴 increases. Here 𝐴 is 0∘ , 22.5∘ , and 40∘ ,
respectively.
(ii) If 𝑚 > 2, robot shape looks like a polygon of 𝑚 edges.
Maximal 𝑚 is determined by 𝑛. In 𝑚-edges loop, the
bigger 𝐴 is, the more concave the edge will be. But the
robot center of mass almost stays the same. Figure 14
shows a few screenshots of robot in polygon shape.
Here 𝐴 = 33.75∘ . Triangular shape is more realistic
considering the height and touch-ground area of the
robot; that is, triangle is more stable.
To avoid module collision, amplitude cannot have a
bigger value, such as 90∘ -Offset, in some cases. Figure 15(a)
shows an example of collision when 𝐴 is 45∘ (here 90∘ -Offset
= 67.5∘ ). Figure 15(b) is the analysis of linkage kinematics
based on virtual fixed joint. During the motion process,
8
Advances in Mechanical Engineering
4
5
3
6
d15
d14
d16
2
..
d1i
.
1
State in t0 s n
(a)
(b)
Figure 15: Collision example and collision analysis.
Computer:
trajectory discretization and
generation of angle commands
70
Amplitude (deg)
60
50
40
30
20
10
0
6
8
10
12
14
16
Number of Modules
18
20
Relay-board:
send commands and
receive information
Max-Amp for m = 2
Max-Amp for m = 3
Preferred-Amp
Figure 16: Amplitude references for motion in ellipse and triangle.
the distance from joint 𝑥 to joint 1 must satisfy formula (8).
Here 𝑥 is module ID. Consider
Min (𝑑14 , . . . , 𝑑1𝑖 , . . . 𝑑1(𝑛−2) ) ≥ 1.414 ∗ 𝐿,
Composed caterpillar robot
(8)
where 𝐿 is the length of module-edge.
Using numerical simulation, maximum amplitudes correspond to number of modules and polygon edges are
calculated. Here maximum module number is set as 20. If
number of edges 𝑚 ≥ 4, maximum amplitudes is 90∘ − Offset.
Figure 16 shows maximum and preferred amplitudes when
𝑚 ≤ 4. Value of 𝐴 is recommended to have the same value as
Offsetif permitted. That makes the polygon edge similar to a
straight line. If 𝐴 is bigger than Offset, polygon edge shapes
a concave curve; otherwise it forms a convex one. Concave
curve and a straight line are more stable.
4. Experiment
4.1. Hardware. UBot MSRR is employed to verify aforementioned proposed method. As shown in Figure 17, the
system is composed of a computer, relay-board, and basic
modules. UBot has two kinds of basic modules: active and
passive. Active module can attach to passive module using
Figure 17: UBot MSRR experiment system.
mechanical connecting mechanism mounted on link-face.
Diverse configurations may be constructed including simple
caterpillar robot.
In an experiment implementing open-loop gaits, angle
instructions should be generated by discretizing joints trajectory and mapping virtual module ID to real module ID using
the computer. Then the angle instructions are sent via a relayboard through wireless communication. Module joints rotate
by following planned angles. This way, the robot can move in
a specific pattern coordinately.
4.2. Testing of Multipattern Locomotion. This section verifies
the proposed method experimentally for bionic caterpillarlike, millipede-like, and earthworm-like locomotion, as well
as beyond nature elliptical rolling and polygon rolling locomotion. The sine-function period of all the experiments is
set as 𝑇 = 4 s. Other experimental parameters are listed
in Table 1. For rolling pattern, it is easy to fall down when
number of modules is too large. Here we use 8-module loop
for the demonstration. A few simulation and experimental
videos can be seen in the Supplementary Material available
online at http://dx.doi.org/10.1155/2014/259463.
Advances in Mechanical Engineering
9
Z
Y
X
Y-direction (m)
(a)
0.1
0
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
X-direction (m)
(b)
Figure 18: Experimental screenshots and body-center changes of caterpillar-like pattern.
Table 1: Parameters set for experiments.
Motion patterns
Caterpillar-like
Millipede-like
Earthworm-like
Elliptical rolling
Triangular rolling
Number of modules
8
8
8
8
8
Offset
0∘
0∘
0∘
45∘
45∘
𝐴
30∘
80∘
80∘
25∘
40∘
Δ𝜙
𝜋/4
2𝜋/3
5𝜋/6
𝜋/2
6𝜋/8
4.2.1. Caterpillar-Like Locomotion. Figure 18 shows the
experiment screenshots and position changes of body-center.
Solid points in Figure 18(b) represent 𝑋-𝑌 coordinates at time
𝑡 (𝑡 is integer for 0 to 20). Displacement in 𝑌-direction results
from the geometry feature of UBot module and uncertainty
in real world. In 20 s, the robot moves about 0.43 m. It can
be seen that caterpillar robot moves stably and smoothly in
sine-waveform.
dense part of robot exchange in the motion process. This
process is like the stretched motion of earthworm. This
demonstrates that peristaltic motion can be realized in
articulated chain-type robot. This locomotion pattern and
above millipede-like pattern are very suitable to restricted
environment.
4.2.4. Elliptical Rolling Locomotion. Experimental screenshots are shown in Figure 21. In 20 s, the robot rolls about
0.71 m. The robot rolls in a static way. It propels forward
through the deformation of the loop. The locomotion trajectory is not straight due to the geometrical characteristic of
UBot module.
4.2.5. Triangular Rolling Locomotion. For polygon rolling
motion, triangular shape is more stable. Its locomotion
procedure is illustrated in Figure 22. The robot rolls forward
about 0.47 m in 20 s. It can be observed that the shape of
robot resembles a triangle. Same as above static rolling, it
also propels forward by deforming the loop. This pattern and
above rolling pattern are very suitable for fast motion in a
planar environment by enlarging angle speed.
4.2.2. Millipede-Like Locomotion. Figure 19 illustrates the
locomotion procedure. The robot moves forward about
0.46 m in 20 s. Pictures show that the robot has many
grounding modules. Its waveform is low and the motion is
stable. As there are many grounding modules, the locomotion
process is more like a millipede.
5. Discussion and Future Work
4.2.3. Earthworm-Like Locomotion. The experiment screenshots of earthworm-like are given in Figure 20. The robot
moves forward about 0.34 m in 20 s. The sparse part and
All motion patterns, in this paper, are planned in openloop; that is, the shape of the robot during motion procedure
is focused. Testing the performance of different patterns
depending on environmental conditions is very important
10
Advances in Mechanical Engineering
Z
Y
X
Y-direction (m)
(a)
0.1
0
−0.1
0
0.2
0.1
0.3
0.4
0.5
0.6
0.7
X-direction (m)
(b)
Figure 19: Experimental screenshots and body-center changes of millipede-like pattern.
Z
Y
X
Y-direction (m)
(a)
0.1
0
−0.1
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
X-direction (m)
(b)
Figure 20: Experimental screenshots and body-center changes of earthworm-like pattern.
Advances in Mechanical Engineering
Z
11
Y
X
(a)
Y-direction (m)
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
X-direction (m)
(b)
Figure 21: Experimental screenshots and body-center changes of elliptical rolling pattern.
Z
Y
X
(a)
Y-direction (m)
0.3
0.2
0.1
0
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
X-direction (m)
(b)
Figure 22: Experimental screenshots and body-center changes of triangle rolling pattern.
12
to make it applicable. It is also essential to investigate the
control strategies for keeping and switching motion patterns
using sensory information such as angle-sensor and infraredsensor. These studies will enhance the robot’s capability and
adaptability. That will be our future work.
The UBot MSRR module used to compose robots has
two degrees of freedom (DOF). The exploration of its motion
capabilities to realize additional locomotion patterns is also a
meaningful work.
6. Conclusion
A simple scalable sinusoidal oscillator is successfully
employed for implementing diverse bionic locomotion
patterns including caterpillar-like, millipede-like, and
earthworm-like motions. Diverse rolling patterns are also
demonstrated using the same oscillator. The effectiveness of
method is verified through simulations and experiments. The
main contribution of this paper is the broadening of motion
patterns for chain-type robot. Moreover, preferable design
parameters (calculated through numerical simulation) can
be replicated in other analogous robots immediately.
Conflict of Interests
The authors declare that there is no conflict of interests
regarding the publication of this paper.
Acknowledgment
This research is supported by the National Natural Science
Foundation of China (60273316).
References
[1] R. M. Alexander, Principles of Animal Locomotion, Princeton
University Press, Princeton, NJ, USA, 2003.
[2] Z. Juhász and A. Zelei, “Analysis of worm-like locomotion,”
Mechanical Engineering, vol. 57, no. 2, pp. 59–64, 2013.
[3] M. Yim, W. M. Shen, B. Salemi et al., “Modular selfreconfigurable robot systems,” IEEE Robotics and Automation
Magazine, vol. 14, no. 1, pp. 43–52, 2007.
[4] K. Gilpin and D. Rus, “Modular robot systems,” IEEE Robotics
and Automation Magazine, vol. 17, no. 3, pp. 38–55, 2010.
[5] J. Everist and W.-M. Shen, “Mapping opaque and confined
environments using proprioception,” in Proceedings of the IEEE
International Conference on Robotics and Automation (ICRA
’09), pp. 1041–1046, May 2009.
[6] Gonzalez-Gomez, Juan et al., “Locomotion capabilities of a
modular robot with eight pitch-yaw-connecting modules.,” in
Proceedings of the 9th international conference on climbing and
walking robots, 2006.
[7] F. Enner, D. Rollinson, and H. Choset, “Simplified motion modeling for snake robots.,” in Proceedings of the IEEE International
Conference on Robotics and Automation (ICRA ’12), 2012.
[8] K. Støy, W.-M. Shen, and P. M. Will, “Using role-based control to
produce locomotion in chain-type self-reconfigurable robots,”
IEEE/ASME Transactions on Mechatronics, vol. 7, no. 4, pp. 410–
417, 2002.
Advances in Mechanical Engineering
[9] A. Kamimura, H. Kurokawa, E. Yoshida, S. Murata, K. Tomita,
and S. Kokaji, “Automatic locomotion design and experiments
for a modular robotic system,” IEEE/ASME Transactions on
Mechatronics, vol. 10, no. 3, pp. 314–325, 2005.
[10] W. M. Shen, M. Krivokon, H. Chiu, J. Everist, M. Rubenstein,
and J. Venkatesh, “Multimode locomotion via SuperBot reconfigurable robots,” Autonomous Robots, vol. 20, no. 2, pp. 165–177,
2006.
[11] H. Wei, Y. Cui, H. Li et al., “Kinematics and the implementation
of a modular caterpillar robot in trapezoidal wave locomotion,”
International Journal of Advanced Robotic Systems, vol. 10, 2013.
[12] A. Menciassi, S. Gorini, G. Pernorio, and P. Dario, “A SMA
actuated artificial earthworm,” in Proceedings of the IEEE
International Conference on Robotics and Automation (ICRA
’04), vol. 4, pp. 3282–3287, May 2004.
[13] N. Saga and T. Nakamura, “Development of a peristaltic crawling robot using magnetic fluid on the basis of the locomotion
mechanism of the earthworm,” Smart Materials and Structures,
vol. 13, no. 3, pp. 566–569, 2004.
[14] H. Omori, T. Nakamura, and T. Yada, “An underground
explorer robot based on peristaltic crawling of earthworms,”
Industrial Robot, vol. 36, no. 4, pp. 358–364, 2009.
[15] B. Kim, M. G. Lee, Y. P. Lee, Y. Kim, and G. Lee, “An earthwormlike micro robot using shape memory alloy actuator,” Sensors
and Actuators A: Physical, vol. 125, no. 2, pp. 429–437, 2006.
[16] S. Seok, C. D. Onal, K. J. Cho et al., “Meshworm: a peristaltic
soft robot with antagonistic nickel titanium coil actuators,”
IEEE/ASME Transactions on Mechatronics, vol. 18, no. 5, pp.
1485–1497, 2013.
[17] M. Yim, “New locomotion gaits,” in Proceedings of the IEEE
International Conference on Robotics and Automation, pp. 2508–
2514, May 1994.
[18] J. Zhao, X. Cui, Y. Zh, and S. Tang, “A new self-reconfigurable
modular robotic system UBot: multi-mode locomotion and
self-reconfiguration,” in Proceedings of the IEEE International
Conference on Robotics and Automation (ICRA ’11), 2011.
[19] J. Zhao, X. Cui, Y. Zhu, and S. Tang, “UBot: a new reconfigurable
modular robotic system with multimode locomotion ability,”
Industrial Robot, vol. 39, no. 2, pp. 178–190.
[20] W. Wang, K. Wang, and H. Zhang, “Crawling gait realization
of the mini-modular climbing caterpillar robot,” Progress in
Natural Science, vol. 19, no. 12, pp. 1821–1829, 2009.
[21] http://biodidac.bio.uottawa.ca.
Téléchargement
Random flashcards
Le lapin

5 Cartes Christine Tourangeau

aaaaaaaaaaaaaaaa

4 Cartes Beniani Ilyes

découpe grammaticale

0 Cartes Beniani Ilyes

Anatomie membre inf

0 Cartes Axelle Bailleau

Créer des cartes mémoire