Journal of Robotics, Networking and Artificial Life
Vol. 6(2); September (2019), pp. 109–112
DOI: https://doi.org/10.2991/jrnal.k.190828.008; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Tumble Avoidance System for Rescue Robot by Estimating
the Contact Points using a 3D Depth Sensor
Noritaka Sato*, Kotaro Ohshima, Yoshifumi Morita
Department of Electrical and Mechanical Engineering, Nagoya Institute of Technology, Gokiso-cho, Syowa-ku, Nagoya, Aichi 466-8555, Japan

ARTICLE INFO
Article History
Received 03 November 2018
Accepted 20 November 2018
Keywords
Rescue robot
tumble avoidance
normalized energy stability margin
3D depth sensor

ABSTRACT
Rescue robots are expected to perform works in hazardous areas. However, when the robot runs on a rough terrain, fatal rollover
falling sometimes occurs. Therefore, we propose a tumble-risk-assessment system, which calculates a normalized energy stability
margin by using a 3D depth sensor. Moreover, a control system to avoid the tumble situation by moving the sub-crawlers
automatically is proposed. We implemented the proposed systems to a real robot, carried out experiments, and confirmed theeffectiveness of the proposed method.
© 2019 The Authors. Published by Atlantis Press SARL.
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

1. INTRODUCTION
Recently, times of emergency have necessitated rapid searches
for victims in collapsed buildings. However, searches conductedby humans involve risks of secondary disasters. Therefore, it isexpected that rescue robots search for victims before entry byhumans [1].
Most existing rescue robots are controlled by teleoperation, because
fully autonomous control may be dangerous in uncertain disasterenvironments. However, full teleoperation is difficult for a humanoperator, because a rescue robot has multiple degrees of freedom
in general. Therefore, human support control system, “semi-­
autonomous control” in other words, is needed [2].
One of most critical incidents is falling down of the robot. Figure 1
shows a robot which falls down in the RoboCup Rescue Robot
League competition. As shown in Figure 1, the fallen robot cannot
run again without someone’s supports.
Okada et al. [3] proposed a semi-autonomous control system toavoid the falling-down situation by using the normalized energy
stability margin (SNE) proposed by Hirose et al. [4]. However, theauthors assume that the circumference of the robot contacts withthe ground. Therefore, the system often cannot calculate the riskof the fall precisely, when the robot runs on a rough terrain, because
the contact point between the robot and the ground may locate on
the inner part of the robot.
In the existing works, the assumption about the contact points
between the ground and the robot are necessary because SNE is
obtained from the position of the contact points and it is difficult toCorresponding author. E-mail: sato.noritaka@nitech.ac.jp*

get the precise position of the contact point. However, the assumptions of existing studied do not often satisfy when the robot runs
on a rough terrain.
In our previous work, we developed estimating system of the contact points by using a reconstructed 3D map from the point clouddata of a 3D depth sensor, such as KINECT [5]. Therefore, the purpose of this study is to develop a semi-autonomous system to avoid
the falling-down situation. In our system, we use partial differentiation of the SNE. The contact points are estimated by the system
which is developed in our previous work.

2. CALCULATION METHOD OFNORMALIZED ENERGYSTABILITY MARGIN
2.1. Normalized Energy Stability Margin
The normalized energy stability margin proposed by Hirose et al.
[4] is one of the most popular indexes of risk of fall because thecalculation is very simple. The normalized energy stability margin
SNE is obtained by Equation (1)		     SNE = hmax − h ,(1)where hmax is the highest height of the Center of Gravity (COG)of the robot when the robot falls down and h is the current heightof the COG of the robot, as shown in Figure 2. As mentioned inSection 1, to calculate SNE, the positions of the contact points arenecessary because the axis of the fall is obtained by the positions ofthe contact points.

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in Figure 4. A convex polygon is made from the contact points andthe edges of the convex polygon are set as the axes of fall. Afterobtaining the axes of fall, the normalized energy stability margin
SNE is calculated by Equation (1).

3. CONTROL METHOD OF SUB-CRAWLERS
To avoid the tumble situation, SNE should be increased by moving
the sub-crawlers, when the SNE becomes almost zero. Therefore, thesub-crawlers should be moved so thatdSNE			 > 0.(2)dt
Figure 1 | Fallen robot in RoboCup rescue competition.

SNE is a function of the contact points and the angle of the sub-­crawlers. In this study, we assume that the contact points are known
by the proposed estimation system described in Section 2 and SNE is
the function of the angle of the sub-crawlers only. Therefore,dS
∂S dq
∂S dq
∂S dq
∂S dq
   NE = NE FR + NE FL + NE RR + NE RL (3)∂q FR dt
∂q FL dt
∂q RR dt
∂q RL dtdt
The definition of qYX = qFR, qFL, qRR, qRL is shown in Figure 5. Thetarget angle of the sub-crawlers are set as Equation (4) in this study.

Figure 2 | Normalized energy stability margin.	  

∂Sdq YX= K NE (K : Constant value) (4)
dt
∂q YX

Figure 3 | Detection area for contact points.

2.2. Estimation of Contact Points
To estimate the contact points between the ground and the robot,
the 3D map is reconstructed by using a 3D depth sensor. The simultaneous localization and mapping technique is used for the mapbuilding with Robot Operation System (ROS) by Willow Garage.
By using ROS, a developer can easily use someone’s open-source
programs. In this study, we use Real-Time Appearance-Based
Mapping (RTAB-Map) developed by Labbe et al. [6]. And since wealso use ROS, other developers can easily use our system, only putting a 3D depth sensor on the robot.

Figure 4 | Estimation result of contact points and axes of fall (Whitepoints are environment points of 3D map, yellow points are contact points
between ground and robot, and red points are points which configure
axes of fall.).

2.3. Estimation of Axis of FallWe set the detection area along with the shape of the bottom partof the robot as shown in Figure 3. If a point in the 3D map exists
in the detection area, the point is set as a contact point as shown

Figure 5 | Definition of qYX = qFR, qFL, qRR, qRL (Top view).

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By applying Equation (4) to Equation (3), we obtain the following
Equation (5):    ∂S  2  ∂S  2  ∂S  2  ∂S  2 dSNE= K  NE  +  NE  +  NE  +  NE   (5)dt ∂q FR   ∂q FR  ∂q FR  ∂q FR 
Therefore, in the case of K > 0, the right side of Equation (5) is always
positive, i.e., to avoid the tumble situation by satisfying Equation (2),the target angle of the sub-crawlers should be set as Equation (4).
In the proposed control system, the robot is teleoperated by anoperator. However, when the SNE becomes almost zero, the target
angles of the sub-crawlers are used as Equation (4) instead of theinput by the operator to avoid the tumble situation. Figure 6 shows
the control flow chart of the proposed control system.

4. EXPERIMENT
4.1. Experiment Method
The purpose of the experiment is to verify the effectiveness of theproposed method compared with the existing method [3]. The
experiment environment and the motion of the robot is shown inFigure 7. The left side of the robot is on the concrete block and theother side is on the ground. The robot repeats stop and go for 2.5 s.
The evaluation index is SNE.

method, the decrease of the SNE becomes gradual after 9.2 s and theSNE increases after 10 s. On the other hand, in the case of the existing method, the SNE continues to decrease after 9.2 s. Note that thereason, why the SNE was slightly changed at 6.6 s and around 10.5 s
when the robot stops, is that the position of the robot estimated by
RTAB-Map was slightly changed (about 6 cm).

4.3. Consideration
As shown in Figure 8, the large difference between the proposed
method and the existing method is observed after 9 s. Therefore,Figures 9 and 10 show the angle of the sub-crawlers after 9 s
in the case of the existing method and the proposed method,respectively.
As shown in Figure 9, the sub-crawlers did not move because of
the assumption that the circumference of the robot contacts withthe block and the ground. On the other hand, as shown in Figure 10,
in the case of the proposed method, the front- and rear-right
sub-crawlers are moved after 9.2 s, and then the SNE increases,
because the contact points and the axis of the fall are precisely estimated and the motions of the sub-crawlers are correct. The averageof SNE from 11 to 12 s of the proposed method and the existing
method are about 0.049 and 0.016 m, respectively.

4.2. Result
The result of the experiment is shown in Figure 8. The horizontal
axis is time and the vertical axis is SNE. The green and orange line
shows the SNE by the existing method and the proposed method,respectively. As shown in Figure 8, in the case of the proposed

Figure 8 | The result of the experiment.

Figure 6 | Flow chart of the proposed control system.

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Figure 9 | Angle of the sub-crawlers (existing method).

Figure 7 | The experiment environment and the motion of the robot (proposed method).

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CONFLICTS OF INTEREST
The authors declare they have no conflicts of interest.

REFERENCES[1][2]
Figure 10 | Angle of the sub-crawlers (proposed method).

5. CONCLUSION
In this study, we propose a tumble-risk-assessment system,
which calculates a normalized energy stability margin by using a3D depth sensor. Moreover, a control system to avoid the tumble
situation by moving the sub-crawlers automatically is proposed.
We implemented the proposed systems to a real robot, carriedout experiments, and confirmed the effectiveness of the proposed method.

[3]

[4]

[5]

In the situation where it is difficult for the existing to control theproposed method can increase SNE by about three times.
The future works are to carry out the experiment on rough terrains
and to develop a system not to become a dangerous situation by
predicting the SNE.

[6]

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Authors Introduction
Dr. Noritaka Sato

Prof. Yoshifumi Morita

He received Doctor of Engineering
degree from The University of ElectroCommunications in 2009. He was a
Program-Specific Assistant Professorof Kyoto University in 2009–2011 andis an Assistant Professor of Nagoya
Institute of Technology since 2011.
His research interests include humanrobot interaction for rescue robots,rehabilitation-assist robots and industrial robots. He is a memberof the IEEE, RSJ and SICE.

Mr. Kotaro Ohshima
He received Master of Engineering
degree from Nagoya Institute ofTechnology in 2016. His research interests include control system, system integration and teleoperation system for
rescue robots.

He is a Professor in the Department
of Electrical and MechanicalEngineering at Nagoya Institute ofTechnology. He received the PhD
degree from Nagoya Institute ofTechnology in 1998. His current
research interests are rehabilitation
support robot/device and robot
teaching.