Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 191–194___________________________________________________________________________________________________________Feature Extraction for Digging Operation of Excavator
Based on Data-Driven Skill-Based PID Controller
Kazushige Koiwai, Toru Yamamoto

Institute of Engineering, Hiroshima University,
1-4-1, Kagamiyama, Higashihiroshima City, Hiroshima, 739-8527, Japan
E-mail: koiwaik@hiroshima-u.ac.jp, yama@hiroshima-u.ac.jp
http://www.hiroshima-u.ac.jp
Takao Nanjo, Yoichiro Yamazaki

Global Engineering Center, KOBELCO Construction Machinery CO., LTD.,
2-2-1, Itsukaichikou, Saeki-ku, Hiroshima City, Hiroshima, 731-5161, Japan

Abstract
Improvement of the work efficiency is demanded by aging and reducing of the working population in theconstruction field, so that some automation technologies are applied to construction equipment, such as bulldozers
and excavators. However, not only the automation technologies but also expert skills are necessary to improve thework efficiency. In this paper, the human skill evaluation is proposed by the data-driven skill-based PID controller.
The proposed method is applied to the excavator digging operation. As the result, the difference between the novice
operation and the skilled operation is extracted. Moreover, the numerical difference is clarified based on the result.
Keywords: PID Controller, Human Skill Evaluation, Data-Driven.

PID gains, called the skill-based PID controller. The
operation skill has been replicated by the skill-based
PID controller experimentally. Moreover, the skillevaluation by using the skill-based PID controller havebeen performed.4,5 These research focus on the changeof PID gains to extract the feature of the operation skillfor the excavator. However, the evaluated operation isapplied to the simple lever operation such as the swing
operation. In the actual work field, the operation for theexcavator is more complicated. In the previous study5,
CMAC is utilized as the parameter tuner. However,
CMAC doesn’t have extensibility such as thedata-driven method.
In this study, the data-driven skill-based PID controller
is adopted to the actual work operation of the excavator,
i.e. the continuous digging operation. The skill feature
for the excavator of the continuous digging operation isextracted to compare the novice operation with theprofessional operation based on the control engineering
approach. Moreover, the numerical difference of feature
between is verified.

1. Introduction
Japanese Ministry of Land, Infrastructure, Transportand Tourism have started the policy of the integration ofconstruction and ICT (Information and Communication
Technology) for public works such as the land survey,engineering, the construction and the maintenance, thatis called “i-construction”.1 Aims of this policy are theimprovement of the work efficiency and the increasingof productivity. Technologies of IoT (Internet ofThings), Big Data and AI (Artificial Intelligence) will
be applied to some construction equipments based on
the policy. However, human operations for construction
equipments are still required continuously in the field
because skilled workers optimize their operations forhigh productivity in each complicated conditions.
Unfortunately, those skilled operators who have theunique technique are reduced in these days because of
the aging and reducing of the working population.
As the previous studies, skill based controllers havebeen proposed.2,3 These studies considered that thehuman skill described as a controller with the tuner of

Copyright © 2017, the Authors. Published by Atlantis Press.
This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 191–194___________________________________________________________________________________________________________

where the index and the total number of datasets isshown as i and N, respectively.
PID gains that is requested by the query are calculatedby the following procedure. Distances 𝑑(∙) between
the query 𝜙�(𝑡) and 𝜙�(𝑖)in each datasets are calculatedby:𝑑 �𝜙�(𝑡), 𝜙�(𝑖)� =2. Scheme of skill feature extraction
The scheme of feature extraction for the human skill isshown in Fig. 1. Based on the engineering approach, theoperational objective and the operator are considered as
the system and the PID controller with the parameter
tuner that is consist of the data driven method. 𝑦 ∗ (𝑡)and 𝑢∗ (𝑡) are denoted as the system output and thesystem input by the operation.

���𝜙𝑙 (𝑡) − ���𝜙𝑙 (𝑖)����𝑙 − min𝝓���𝑙max𝝓(6)(7)

𝑲𝑷𝑰𝑰 (𝑡) = � 𝜔𝑚 𝑲𝑷𝑰𝑰 (𝑚)𝑚=1

𝑲𝑷𝑰𝑰 (𝑡) = [𝐾𝑃 (𝑡)𝜔𝑚 =The following control law is considered for theskill-based controller:

𝐾𝐼 (𝑡))−1(1 + 𝑑𝑚𝑘∑𝑛=1(1 + 𝑑𝑛 )−1𝐾𝐷 (𝑡)]
.

(8)
(9)

2.2. Skill feature extraction∆𝑢(𝑡) = 𝐾𝐼 (𝑡)𝑒(𝑡) − 𝐾𝑃 (𝑡)∆𝑦 ∗ (𝑡) − 𝐾𝐷 (𝑡)∆2 𝑦 ∗ (𝑡) (1)PID gains are learned by the operational data 𝑢∗ (𝑡)and 𝑦 ∗ (𝑡), which are teacher’s signals for the learning
scheme in Fig. 2. PID controller, which has the changeof PID gains can be expressed as the feature of thehuman skill.
The following steepest descent method (10) is
performed for the learning of PID gains in the database.

where the PID gains, 𝐾𝑃 (𝑡), 𝐾𝐼 (𝑡) and 𝐾𝐷 (𝑡) aretuned by the database. The control error signal 𝑒(𝑡) isdefined as 𝑒(𝑡): = 𝑟(𝑡) − 𝑦(𝑡) and the difference
operator ∆ is defined as ∆≔ 1 − 𝑧 −1 . 𝑦(𝑡) and 𝑢(𝑡)are donated as the system output and the system input
respectively.
The operational objective is considered as thefollowing nonlinear system:= ℎ�𝜙(𝑡 − 1)�.

𝑙=1�𝑘

2.1. Data-driven PID controller𝑦�

where the index of an element in a dataset or query is���𝑙 is a set of the element for the index.
shown as l. 𝝓
Neighbors of data are selected according to theirdistance from query. The number of neighbor dataset,that is the user specified parameter, is donated as k. The
sum of selected PID gains 𝑲𝑷𝑰𝑰 (𝑚) multiplied by aweight is desired PID gains 𝑲𝑷𝑰𝑰 (𝑡) as follows:

Fig. 1 Scheme of Feature Extraction∗ (𝑡)𝑛𝑢 +𝑛𝑦 +1

𝑜𝑜𝑜𝑲𝑛𝑛𝑛
𝑷𝑷𝑷 (𝑡) = 𝑲𝑷𝑷𝑷 (𝑡) − 𝑔(𝑡)

(2)

𝜕𝜕(𝑡)
𝜕𝑲𝑷𝑷𝑷

(10)

where the error criterion 𝐽(𝑡) ,the updating gradient𝑔(𝑡) and the partial differential are expressed asfollows:

Here the ℎ(∙) is the nonlinear function using past data
and the information vector 𝜙(𝑡 − 1) is defined as:𝜙(𝑡 − 1) = �𝑦 ∗ (𝑡 − 1), ⋯ 𝑦 ∗ �𝑡 − 𝑛𝑦 �,(3)𝑢∗ (𝑡 − 1), ⋯ 𝑢∗ (𝑡 − 𝑛𝑢 ), ]𝑔(𝑡) = (𝑐 + 𝑎 ∙ 𝑒𝑒𝑒(−𝑏|𝑢∗ (𝑡) − 𝑢(𝑡)|))−1

where 𝑛𝑦 and 𝑛𝑢 are orders of output and input,
respectively. The query 𝜙�(𝑡) is defined as:𝜙�(𝑡) = [𝑟(𝑡 + 1), 𝑟(𝑡), 𝑦 ∗ (𝑡), 𝑦 ∗ (𝑡 − 1), ⋯𝑦 ∗ �𝑡 − 𝑛𝑦 �, 𝑢∗ (𝑡 − 1), ⋯ 𝑢∗ (𝑡 − 𝑛𝑢 )�. (4)𝐽(𝑡) =𝜖(𝑡)22𝜖(𝑡) = 𝑢∗ (𝑡) − 𝑢(𝑡)The dataset of database, 𝚽(𝑖) , is constructed asfollows:𝚽(𝑖) = [𝜙�(𝑖), 𝐾𝑃 (𝑖), 𝐾𝑃 (𝑖), 𝐾𝑃 (𝑖)],(5)𝑖 = 1,2, ⋯ , 𝑁𝜕𝜕(𝑡)𝜕𝜕(𝑡) 𝜕𝜕(𝑡) 𝜕𝜕(𝑡)=𝜕𝑲𝑷𝑷𝑷
𝜕𝜕(𝑡) 𝜕𝜕(𝑡) 𝜕𝑲𝑷𝑷𝑷

192

(11)
(12)
(13)

.

(14)

Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 191–194___________________________________________________________________________________________________________3. Evaluation Result

only. The acceleration should be ignored in this paper.
The evaluation procedure is shown as follows.
The proposed method is applied to the specified
operation into the continuous digging operation. In thisstudy, the swing operation is selected as the specified
operation because the skill is necessary to stop at theproper position and the reference value 𝑟(𝑡) ofoperation is clearer than another operation. The digging
operation is performed to record 𝑢∗ (𝑡) and 𝑦 ∗ (𝑡). Here,the pilot pressure and the angle of the swing are set as
the input value 𝑢∗ (𝑡) and the output value 𝑦 ∗ (𝑡),respectively. Then, the skill feature is extracted by thecomparison of the professional and the novice operation.
Moreover, the physical quantity that can be measured isevaluated based on the skill feature.

3.1. Experimental equipment

3.3. Application result of proposed method

The proposed scheme is applied to the operation of anexcavator. The evaluated operation is a continuous
digging motion and the excavator, SK200-9 made inKobelco Construction Machinery, is used for theexperiment. The continuous digging motion can beclassified into four parts in Fig. 3. First part is called
digging that is the operation to scoop the earth and sandwith the bucket ①. Then, the load is lifted to thecertain height that assumed a carrier of the truck ②.
Furthermore, the load is dumped on the ground thatassumed the truck bed ③. Finally, teeth of the bucket isrepositioned to the starting point of the digging ④.

The data of the swing motion in the continuous
digging operation is extracted, normalized andresampled by the sampling time of 200ms according to
the human quickness of a response. The proposed
method is applied to the professional and the novice
data. The user specific parameter of the proposed
method is shown as Table 1.
The calculation results of PID parameter for theprofessional operator and the novice operator are shownin Fig. 4 and Fig. 5. As the result, the change of PID
gain at the period of deceleration which needs theoperation skill has the opposite properties to comparewith them. This is the almost same result of the skill
evaluation for the single operation. Refer to thereference 4 in the detail of the single operation.
Especially, derivative gains have the opposite tendency.
It means that the professional predicts the future motion
based on the property of the derivative gain. Therefore,
the professional operation is smoother than the novice
operation.

Fig. 2 The Leaning Scheme of Skill Feature Extraction

Coefficients of a, b and c are the appropriate positive
constants.

3.2. Skill evaluation procedure
The most important operation for actual works is thepositioning at the stop motion because the human skill
is required by the operation. Therefore, the target forthis evaluation is the positioning by the stop operation

Table 1. User Specified Parameter of database
Item
Symbol Value
Number of data yny3
Number of data unu3Number of neighbor
k3Total number of data set
N100
Coefficient of updating gradienta100
Coefficient of updating gradientb1
Coefficient of updating gradientc100Fig. 3 Continuous Digging Motion for Excavator

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Journal of Robotics, Networking and Artificial Life, Vol. 4, No. 3 (December 2017) 191–194___________________________________________________________________________________________________________Stop motion

Stop motion

Fig. 4 Professional Skill Feature Extraction

Fig. 5 Novice Skill Feature Extraction
verified based on PID gains. Especially, derivative gainsof the professional and the novice are different. Thechange of derivative gain shows the future prediction tocreate the fluidity. Moreover, the fluidity of theoperation is verified by the value of the input change. In
the future work, the control row will be developed
focused on operation fluidity.

3.4. Skill numerical evaluation
The operation of the novice operator is different fromthe professional one in fluidity because the professional
operation is smooth as the above explanation. To
compare fluidities, change of input are considered andextracted as the maximum value and the number beyond
the 50% from the 10 cycle of the continuous digging
operation. The result is shown as Table 2. As the result,
it is verified that the novice has the unnecessary input
compared with the professional. It means that the novice
operator operates the excavator not so smoothly.

5. References
1. T.Kakizaki, ’i-Construction (in Japanese)” monthly
Journal Kensetsu, Vol.60, No.1, (2016), pp. 6-9
2. T.Yamamoto, S.Mori and A.Sakaguchi, Data-Driven
Skill-Based PID Control of a Pilot-Scale Helicopter
Model. , Int. J. of Innovative Computing Information andControl, Vol.4, No.12, (2008), pp.3349-3358
3. Y.Liao and T.Yamamoto, Design and Experimental
Evaluation of a Human Skill-Based PID Controller, J. ofRobotics, Networking and Artificial Life, Vol.2, No.3,(2015), pp.140-143
4. K.Koiwai, T.Yamamoto, T.Nanjo, Y.Yamazaki andY.Fujimoto, Data-driven human skill evaluation forexcavator operation, Proc. of IEEE Conf. on AdvancedIntelligent Mechatronics, Banff (2016), pp.482-487.
5. K.Koiwai, Y.Liao, T.Yamamoto, T.Nanjo, Y.Yamazakiand Y.Fujimoto, Feature Extraction for Excavator
Operation Skill Using CMAC, J. of Robotics andMechatronics, Vol.28, No.5, (2016), pp.715-721.

4. Conclusion
The human skill evaluation based on the data-driven
PID controller has been performed to the excavator
operation of the continuous digging motion. The
difference between the novice and professional wasTable 2. Comparison of input change
Professional Novice
Maximum of input change
0.54
0.69
Number beyond 50%
14input change

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