AbstractThe methodology of tracing odor plume via a mobile robot is considered. In this research, two typical plume-tracing methods, i.e., a zigzagging method and an upwind method, are tested in four airflow fields with different long-time average wind speeds when the robot is set to possessing four different maximum speeds. According to the simulation results, it can be deduced that the zigzagging algorithms would be efficient when the robot moves faster than the odor plume or airflow, and the upwind algorithms are preferred especially when the robot is slow

Cui, Shi-Gang

Geng, Li-Hui

Li, Ji-Gong

Yang, Jing

Elsevier - Publisher Connector 

Speed limitation of a mobile robot and methodology of tracing odor plume in airflow environments 

Procedia Engineering 15 (2011) 1041 – 1045
1877-7058 © 2011 Published by Elsevier Ltd.
doi:10.1016/j.proeng.2011.08.192
Available online at www.sciencedirect.com
Available online at www.sciencedirect.com
Procedia
Engineering
   Procedia Engineering  00 (2011) 000–000
www.elsevier.com/locate/procedia
Advanced in Control Engineeringand Information Science 
Speed limitation of a mobile robot and methodology of 
tracing odor plume in airflow environments 
Ji-Gong Lia,b, Jing Yangc, Shi-Gang Cuia,b, Li-Hui Genga,b, a*
aSchool of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China 
bTianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin, 300222, China 
cSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, 300222, China 
Abstract 
The methodology of tracing odor plume via a mobile robot is considered. In this research, two typical plume-tracing 
methods, i.e., a zigzagging method and an upwind method, are tested in four airflow fields with different long-time 
average wind speeds when the robot is set to possessing four different maximum speeds. According to the simulation 
results, it can be deduced that the zigzagging algorithms would be efficient when the robot moves faster than the odor 
plume or airflow, and the upwind algorithms are preferred especially when the robot is slow. 
© 2011 Published by Elsevier Ltd. 
Selection and/or peer-review under responsibility of [CEIS 2011] 
Keywords: mobile robot; odor source localization; plume tracing; time-variant flow field 
1. Introduction
Olfaction is widely used by animals in many activities, such as searching for food, finding mates,
exchanging information, and evading predators. Inspired by the olfaction abilities of these animals, in the 
early 1990s, researchers started to try building mobile robots with similar olfaction abilities to replace 
trained animals [1-4]. This research was called odor source localization (OSL) [4, 5]. It is expected that 
the odor source localization will play more and more roles in such areas as fighting against terrorist 
attacks, judging toxic or harmful gas leakage location, checking for contraband (e.g., heroin), searching 
for survivors in collapsed buildings or waters, and exploring mineral resources erupted in deep seas. 
* Ji-Gong Li, corresponding author of this paper. Tel.: +086-022-88688715. 
E-mail address: charles75@163.com. 
Open access under CC BY-NC-ND license.
Open access under CC BY-NC-ND license.
1042  Ji-Gong Li et al. / Procedia Engineering 15 (2011) 1041 – 10452 Ji-Gong Li,et al / Procedia Engineering 00 (2011) 000–000 
The OSL can be classified into behavior-based methods and analytical-model-based methods [6], and 
most work has been done on the behavior-based methods due to original bionic background of the OSL. 
The behaviour based OSL was decomposed into three sub-procedures (plume finding, plume traversal, 
source declaration) by Hayes [5] or four sub-procedures (finding a plume, tracing the plume, reacquiring 
the plume, declaring the source) by W. Li [7]. The plume-finding sub-procedure aims to make the robot 
contact with odor plume [5, 7, 8]. Once the plume is detected, the plume traversal or plume tracing is 
switched on and it would make the robot approach the odor source iteratively. Most methods for this sub-
procedure are biologically inspired, such as the gradient-following-based algorithms [8-10], the 
zigzagging algorithms [4, 7, 8, 11], the upwind algorithms [12, 13] and so on. In the final sub-procedure 
the robot locates the source [7].
A proper plume-tracing method can greatly reduce the time cost and enhance the precision of an OSL 
task. In airflow environment, as we know, the dispersal of the odor mainly depends on the transportation 
of the turbulent airflow, forming an intermittent and time-variant odor plume. A smooth concentration 
gradient is most unlikely to exist in such patchy plumes. Therefore, the gradient-following-based 
algorithms cannot be used in this case. The zigzagging algorithms try to make the robot follow the odor 
plume to arrive the odor source by keeping the robot in the plume and moving partially in upwind 
direction. In contrast, the upwind algorithms try to make the robot approach the source by moving against 
the wind when odor detection events happen.  
This paper tries to provide a suggestion for selection of plume-tracing method considering the speed 
limitation of the robot in time-variant airflow environments. The maximum speed of the robot is 
considered because the plume changes with time in time-variant airflow field and apparently there is a 
requirement of the robot’s speed to make the robot keep contact with the plume when a zigzagging 
algorithm is used. In this research, several airflow fields with different long-time average airflow speeds 
are used to test a typical zigzagging algorithm and a typical upwind algorithm when the robot has 
different speed limitations. It is expected to have a conclusion indicating what kind of plume-tracing 
method should be preferred in what case. 
2. Plume-tracing algorithms 
A typical zigzagging algorithm proposed in [11] and a typical upwind method named spiral-surge 
algorithm [13] are considered in this paper, where the concerned zigzagging algorithm is a designed 
plume-maintaining behaviour with active strategy.  
2.1. Plume-maintaining behaviour with active strategy 
The robot tries to trace the plume by keeping in the plume and moving partially in upwind direction, as 
illustrated in Fig. 1(a) [11], where the black dots represent the odor patches. When the first odor detection 
event happens at time ft , the robot travels at an angle β  relative to upwind direction. The angle β  is 
maintained until another odor patch has not been detected for λ  seconds, at which time the robot 
counterturns to travel at an angle γ  with respect to the wind. During the counterturn, if another odor 
detection event happens in period wT , the robot starts another plume-maintaining as described above; 
otherwise the plume-maintaining is thought to be failed and the robot switches to the plume finding again. 
In this research, 1=λ s, °±= 90γ , s and 4=wT β  changes with time as follows [11].
⎪⎩
⎪⎨
⎧
>−°
≤−≤°
<−°
=
stt
stts
stt
t
f
f
f
5.085
5.02.065
2.010
)(β                                                      (1) 
1043Ji-Gong Li et al. / Procedia Engineering 15 (2011) 1041 – 1045Ji-Gong Li / Procedia Engineering 00 (2011) 000–000 3
Fig. 1. (a) Depiction of the plume-maintaining behavior [11];  (b) Depiction of the spiral-surge algorithm [13]
2.2. Spiral-surge algorithm 
As illustrated in Fig. 1(b) [13], when an odor detection event happens, the robot moves against the 
wind direction, and this upwind movement is also called surge. During the surge movement, if another 
odor-detection occurs, the robot resets the surge distance but doesn’t resample the wind direction. When 
the surge distance reaches the step size, the robot begins a spiral movement to search for another odor 
patch. During the spiral movement, if an odor detection event occurs, the robot re-samples the wind 
direction and performs another surge movement; if no odor detection event happens in a given period 
(called cast time), the spiral-surge plume-tracing is thought to be failed and the robot switches to the 
plume finding again. In this research, the values of the parameters in the spiral-surge algorithm are best 
ones according to [13], i.e., step size is 1.82 m, spiral gap is 0.62 m, and cast time is 96 s. 
3. Simulations 
3.1. Simulation platform setup 
The randomness is a natural characteristic of the turbulent environment airflow, and this feature will 
has effect on the procedure of the plume tracing. Therefore, a plume-tracing algorithm should be 
evaluated in statistical way with large samples which could cost lots of time. This is very difficult in real 
airflow environments. In this research, a simulation platform developed in [14] is used on which plume-
tracing algorithms can be tested in repeatable and controllable environments.  
On our simulation platform, each plume-tracing algorithm is tested in different airflow field (the long-
time average wind speeds are 0.2, 0.5, 1.0, 2.0 m/s, respectively) with the virtual robot having different 
speed limitations (the maximum speed are also set to 0.2, 0.5, 1.0, 2.0 m/s, respectively). Therefore, there 
are totally 4×4=16 groups of simulations. In each group of simulation, the robot is started at different 
location at different time to obtain independent results. The initial locations of the robot are uniformly 
distributed in the form of 10×10 array (the spacing between adjacent rows or columns is 10m) within a 
100 m × 100 m search area. At each initial location of the robot, 20 different initial times are used in 
sequence to start the robot. Thus, a plume-tracing algorithm can be tested for 2000 times in total in a 
group of simulation. In each simulation, the robot starts from the designed position and time to find the 
plume with the plume finding behaviour proposed in [7]. Whenever an odor detection event occurs, the 
plume-tracing process is switched on. There are two cases in which the plume-tracing process has to be 
terminated. One case is that the distance between the robot and the odor source is small enough (<0.5m), 
the other one is when the plume is thought to be lost and the robot has to find plume again. 
1044  Ji-Gong Li et al. / Procedia Engineering 15 (2011) 1041 – 10454 Ji-Gong Li,et al / Procedia Engineering 00 (2011) 000–000 
3.2. Criterion for Algorithm Evaluation 
A criterion named approaching index is proposed and explained in Fig. 2. The red point S represents 
the odor source. The initial location of the robot is at A and the first odor patch is detected at the location 
B where a plume-tracing process starts. C stand for the end location where the plume is lost and a plume 
finding behaviour is launched again. Let  and  denote the linear distances from the position S to B
and C, respectively, and  denotes the approaching index which is defined as follows. The 
approaching index indicates how close the robot approaches the odor source after a plume-tracing process. 
BSd CSd
)(Biapp
BSCSBSapp dddi )( −=                                                                  (2) 
S
C
A
B
Fig. 2. Sketch of a plume-tracing trajectory, i.e., the curve from B to C. The trajectory from A to B belongs to a plume finding. 
3.3. Results and discussion 
Table 1. The average approaching indexes of 32 groups of simulations 
             Maximum speed of  
the robot 
Average wind speed 
Plume-maintaining behaviour with 
active strategy Spiral-surge algorithm 
0.2 0.5 1.0 2.0 0.2 0.5 1.0 2.0 
0.2 0.11 0.17 0.29 0.35 0.48 0.62 0.70 0.72 
0.5 0.06 0.09 0.13 0.16 0.36 0.37 0.41 0.40 
1.0 0.05 0.07 0.07 0.06 0.51 0.62 0.65 0.57 
2.0 0.05 0.07 0.09 0.06 0.70 0.70 0.76 0.78 
* The unit of the speed (of the robot and the wind) is m/s. 
For the two concerned plume-tracing algorithms, the average approaching indexes of 32 groups of 
simulations are shown in Table 1. It can be observed that, in a given airflow field, the average 
approaching index is increasing in general when the maximum speed of the robot is increasing for both 
plume-tracing algorithms. For the plume-maintaining method, when the maximum speed of the robot is 
smaller than or equal to the average wind speed, the robot cannot trace the plume efficiently because the 
plume changes fast with time in the time-variant airflow field and the robot having relative slow speed 
almost can not keep step with the plume so as to maintain in the plume and trace the plume to the source. 
In contrast, for spiral-surge algorithm, it seems that the average approaching index is insensitive to the 
average wind speed whichever speed limitation the robot has. This means that the spiral-surge algorithm 
is robust in a time-variant airflow environment. At the end of this section, it is worthy to denote that the 
average approaching indexes in Tab. 1 cannot be used to make comparisons between the two concerned 
plume-tracing methods because the parameters in both algorithms are not optimized in this research. 
1045Ji-Gong Li et al. / Procedia Engineering 15 (2011) 1041 – 1045Ji-Gong Li / Procedia Engineering 00 (2011) 000–000 5
4. Conclusion 
This paper provides a suggestion for selection of plume-tracing method considering the speed 
limitation of the robot in time-variant airflow environments. In this research, several airflow fields with 
different long-time average wind speeds are used to test a typical zigzagging algorithm and a typical 
upwind algorithm when the robot has different maximum speed. The simulation results show that, for the 
concerned zigzagging algorithm, the larger the maximum speed of the robot, the higher the approaching 
index is in general. When the maximum speed of the robot is smaller than or equal to the average wind 
speed, the robot cannot trace the plume efficiently in a time-variant airflow field. In contrast, for the 
concerned upwind algorithm, the approaching index is insensitive to the average wind speed. It can be 
deduced that the zigzagging algorithms should be used in a plume-tracing procedure when the robot 
moves faster than the plume or airflow, and the upwind algorithms is preferred especially when the robot 
is slow. 
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Speed limitation of a mobile robot and methodology of tracing odor plume in airflow environments

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