Journal of Robotics, Networking and Artificial Life
Vol. 8(3); December (2021), pp. 211–217
DOI: https://doi.org/10.2991/jrnal.k.210922.012; ISSN 2405-9021; eISSN 2352-6386
https://www.atlantis-press.com/journals/jrnal

Research Article

Selection of Optimal Error Recovery Process using
Evaluation Standards in Automated Plants
Akira Nakamura1,*, Natsuki Yamanobe2, Ixchel G. Ramirez-Alpizar2, Kensuke Harada2,3, Yukiyasu Domae2
Department of Information Systems, Faculty of Engineering, Saitama Institute of Technology, 1690 Fusaiji, Fukaya, Saitama 369-0293, Japan
Industrial Cyber Physical System Research Center, National Institute of Advanced Industrial Science and Technology (AIST) Second Annex,
AIST Tokyo Waterfront, 2-4-7 Aomi, Koto-ku, Tokyo 135-0064 Japan
3
Robotic Manipulation Research Group Systems Innovation Department, Graduate School of Engineering Science, Osaka University,
1-3 Machikaneyama, Toyonaka 560-8531, Japan
1
2

ARTICLE INFOABSTRACT

Article History

Plant automation has become increasingly popular in various industries. However, errors are more likely to occur in difficult
tasks that are often performed in an automated plant. Such tasks are often returned to the previous step, and re-executed in
the event of a large-scale error. Therefore, it is important to decide both the past step to which the task needs to return and therecovery step following its return. In this study, various evaluation standards are used to realize the planning of error recovery,
while considering these two factors.

Received 31 October 2020
Accepted 31 July 2021
Keywords
Error recovery
task stratification
error classification
automation plant© 2021 The Authors. Published by Atlantis Press International B.V.
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
Automated plants are used for production in various industries,
thereby increasing the production efficiency. However, automationof the system also leads to occurrence of errors. Therefore, recently,
it has become important to develop techniques for error recovery;
thus, research on error recovery has been actively conducted [1–5].
However, the recovery techniques that have been considered inthese studies are ad hoc and difficult to apply to real-world plants.
Therefore, it is necessary to develop versatile and systematic error
recovery technologies.
We conducted research on the systematization of the error recovery
theory for several years. We proposed a new error recovery method
based on the sorting concepts of both task stratification and error
classification [6–8]. As shown in Figure 1, the main segment of thismethod comprises a series of fundamental elements: sensing, modeling, planning, and execution. If an error occurred in the main
segment, the process moves to the recovery portion. Subsequently,
the cause of the errors is estimated, the errors are classified basedon the results, the system is modified to be less prone to errors, andthe process is rerun using the modified system with an improved
reliability for achieving tasks.
We have focused on determining the past step to which the process should revert to as well as the recovery process following
the return. Consequently, we proposed a planning method for
Corresponding author. Email: akira-nakamura@sit.ac.jp*

error recovery, which is derived from the decision of these two
factors, considering the cost incurred during task execution [8].
However, in this study, we have proposed a planning method forerror recovery considering various evaluation standards, and not
only the cost.
Section 2 explains the concept of skills that are motion primitives.
Section 3 describes the error recovery techniques that we have proposed thus far. Section 4 discusses the proposed method for recovery planning using multiple evaluation standards, and Section 5
provides a simple sample that is used to examine the influenceof evaluation standards on selecting a recovery process. Finally,
Section 6 presents the conclusion.

2. CONCEPT OF SKILLS
2.1. Skill Primitives
By analyzing a person performing a task, we derived a sequenceof primitives for various behaviors. The behavioral primitives arecalled “skills” in our research [9–11]. As shown in Figure 2, in theassembly sequence, three skills, i.e., move-to-touch, rotate-to-level,
and rotate-to-insert, played an important role. A skill sequence ofactions for a task was obtained by assembling these three skills
and their derivatives as a working sequence. The primitives of thework sequences of machines in a plant can be considered in thesame manner as the skill sequences obtained from the analysis ofhuman actions.

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Figure 3 | Hierarchy of tasks.

Figure 1 | Automated plant system with an error recovery function.

a

b

c

Figure 4 | Fundamental process flow with error recovery.

3.2. Error Recovery based on Classification

Figure 2 | Three fundamental skill. (a) Move-to-touch skill. (b) Rotate-tolevel skill. (c) Rotate-to-insert skill.

2.2. Stratification of Tasks

Immediately following the occurrence of an error, the first step
involves estimating the cause of the error, and the second step
involves proceeding to the error recovery course based on the specified cause [6–8]. The corresponding system parameters are modified in the indicated error recovery course, as shown in Figure 4.
This improves the system, thus making it less prone to the sameerrors.

For tasks in an automation plant, representation using stratification
makes it easier to consider work sequences, as shown in Figure 3[6–8]. Please refer to Nakamura et al. [6] for more information on
this technique.

When the error is small, the process returns to an earlier step in theexecuted sequence, and the task sequence advances again, as shownin Figures 4 and 5. Conversely, when the error is large, the processreturns to the steps in the upper hierarchy composing the task, andthe recovery process then proceeds, as shown in Figure 5.

3. ERROR RECOVERY

3.3. Candidate Processes for Recovery

Errors that occur during the execution of tasks in automation plants
are issues that cannot be ignored. This section describes a methodthat derives the recovery process using error classifications [6–8].

This subsection briefly describes the recovery process after an error
occurs during task execution. It has been assumed that task T ofstart S and goal G comprises n subtasks {subtask1, subtask2, …, subtaskn}, and that an error occurs in subtaskm (m is one of 1, 2, …, n),
as shown in Figure 6. Here, subtaskm is the minimum possible traceable unit, as described in Nakamura et al. [6]. This is the smallest
unit of the skill primitive sequence, which starts with the first nodethat is required to go backward when an error occurs. That is, aminimum traceable unit represents a boundary, where recovery
can be made by going back to a certain node or a step before that
node; however, recovery is not possible beyond that step.

3.1. Error Classification
The errors are classified into the following four types according to
the cause of occurrence: execution, planning, modeling, or sensing.
As shown in Figure 4, the cause of the error is generally estimatedat the time of executing a task [6–8].

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selected the relevant optimum error recovery processes. The following 10 evaluation standards are considered herein.
(i)		 Cost
We consider the cost of executing the recovery process as an evaluation standard. Nakamura et al. [8] considered only cost as a standard, which corresponds to item (i). Here, cost refers to material
charges, parts charges, electricity bills, and other relevant process
costs. Furthermore, expenses such as planning and personnel
expenses may be included. Ultimately, the recovery process withthe lowest total cost is prioritized.
Figure 5 | The expression of task stratification and the process flow ofthe error recovery.

(ii) TimeLet us consider the time involved in the recovery process as an evaluation standard. However, even for a single failure, decisions, suchas how far to return in the previous step and where to go back in theoriginal sequence, depend on the recovery process. Therefore, thecomparison is made from the time when the failure occurs to thatwhen the original task ends.
(iii) Reliability
We will consider the reliability associated with the execution of
the recovery process as an evaluation standard. Consequently, therecovery process that ensured that the goals of the original task are
fulfilled takes precedence.
(iv) Safety

Figure 6 | Various processes of error recovery considered for a failure
occurred in subtaskm.

Here, it is assumed that an error occurs at subtaskm, and the process returns to subtaskj (j is one of 1, 2, …, m). Then, the recovery process begins from subtaskj, and returns to the original work
sequence. However, it is not always possible to make the process
identical to the original work sequence. Owing to various conditions, it may be necessary to use a sequence composed of different
skill primitives, after an error occurs. This is why the substitute task
for the subtask in the recovery process in Figure 6 is shown as “An
equivalent task.”

4. EVALUATION STANDARDSCONCERNING SELECTION
OF A RECOVERY PROCESS

We will consider the safety of the recovery process execution as
an evaluation standard. Hence, the recovery process that provides
the highest level of safety to the surrounding environment during
operation execution is prioritized.
Although reliability and safety may be perceived as similar evaluation standards, there is a difference between the two. Reliability
is an indicator of the degree of achievement of both the recovery
process and the original goal of the task. Conversely, safety is anindicator of the risk of harm to people or damage to materials in
the time period between which the failure occurs and the originaltask is completed.
(v) Finishing
Here, finishing refers to the appearance, state, and condition of
the object at the time of the goal. We will consider finishing after
the execution of the complete task, including the recovery process,as an evaluation standard. In the example of manufacturing andrepair tasks for equipment and household goods, the recovery process that provides a desirable appearance to the finished product isdetermined to be better.
(vi) Recovery data

In the previous section, we demonstrated how the recovery process
after the occurrence of an error is not necessarily limited to one.
In other words, it is often difficult to determine how far back one
should go, and to select the appropriate type of skill sequence forthe composition of the process following the return. Therefore, amethod is required for selecting a suitable recovery process fromthe various available options.

Let us consider recovery data as an evaluation standard. Specifically,
the amount of data related to the error recovery process is used as
an indicator, and the process with abundant data is preferred foruse. This indicator will become ordinary once data-driven AI techniques start being used for error recovery.

In our previous research, we only used the concept of cost to compose the process, when considering error recovery [8]. Instead, in
this paper, we have considered various evaluation standards, andWe will consider the parts used for error recovery as an evaluation
standard. Depending upon the type of failure, it is sometimes morefeasible to replace the parts. In such cases, when alternative parts(vii) Parts

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were readily available, the recovery process of implementing part
replacement takes precedence.
(viii) Tools
We will consider the tools used in error recovery as an evaluation
standard. If there is a dedicated tool to return from an error state toa normal state, it is deemed more suitable to use the same. In such acase, the process of recovering with tools takes precedence.
(ix) Workspace
Let us consider the workspace used for error recovery as an evaluation standard. In many cases, the feasibility of the recovery process
is determined by whether there is space to perform the recovery
operation. When a workspace for performing error recovery issecured, the process that utilized the relevant workspace is prioritized depending upon the type of failure.
(x) Operator skill

Figure 7 | An assembly task in which a hook is stuck to a plate by four
precision screws.

Let us consider the skills of the operator, who performs error recovery, as an evaluation standard. If there is an operator that is efficientin executing a certain recovery operation, the recovery process that
included the concerned operator is prioritized.

5. PRECEDENCE OF RECOVERYPROCESSES BASED ON VARIOUS
EVALUATION STANDARDSIn this section, we will observe the changes in the error recovery
process that are prioritized by different chosen evaluation standards
using a simple example. The error recovery process is derived andcompared based on a single evaluation standard, which is selectedfrom the 10 types of evaluation standards that are described in
Section 4.

Figure 8 | An error in which a screw is dropped at (h) in Figure 7.

5.1. Several Types of Recovery Processes
Let us consider attaching a hook to a vertical flat plate with four
precision screws. It is assumed that the vertical flat plate has fourtapped holes for these precision screws. The task comprised various skill primitives, as shown in Figure 7; however, these primitives
can be divided into four sections. As shown in Figure 7a–7d, thefirst section is a tacking task that temporarily fastens four screws to
a horizontally laid hook. As shown in Figure 7e and 7f, the second
section is an erecting task, wherein a horizontally inverted hook ismade to stand vertically. As shown in Figure 7g and 7h, the third
section is a touching task for moving the hook to the mounting
position on the vertical plane plate. As shown in Figure 7i–7m, thefourth section is an installation task for fixing the hook to the vertical plane plate by tightening the four screws.
Now, let us consider the recovery processes for the failure in thestep shown in Figure 7h, where one screw that was temporarily
fixed comes off and falls (Figure 8). Here, we have discussed three
types of recovery processes.
•• Recovery Type I (RT-I)
The first recovery type (RT-I) is the process of returning to starting
step S of the original task and starting over (Figure 9). Concurrently,
the hook with three screws and the one screw that fell off when the

Figure 9 | Recovery Type I (RT-I).

error occurred are abandoned, and the task is rerun with new parts,that is, a new hook and four new screws.
•• Recovery Type II (RT-II)
The second recovery type (RT-II) is the process of returning to thestep that is shown in Figure 7d in the tracking task and starting
over from this step, as shown in Figure 10. After returning to thetracking task, a screw is temporarily fastened at the missing hole
once again; consequently, two types of methods occurred depending upon the screw used at said time. One involves abandoning

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Figure 11 | Recovery Type III (RT-III).
Figure 10 | Recovery Type II (RT-II).

the fallen screw and using a new one in the parts box; conversely,
the other involves finding the fallen screw and using it again. The
former is called process [RT-II(N)], and the latter is called process[RT-II(F)].
•• Recovery Type III (RT-III)
The third recovery type (RT-III) is the process of returning to theskill primitive in subtaskm, where the failure occurred and started
over from, as shown in Figure 11. This method has a shorter
return process; however, unlike (RT-II) in the vertical direction, it
requires the insertion and temporary fastening of the screws in thehorizontal direction. Mistakes are likely to occur in the temporary
fixing process, owing to which the difficulty level is high. As in thecase of (RT- II), there are two schemes depending upon the screw
used in the relevant step. The method of discarding the fallenscrew and using a new screw in the parts box is called process[RT-III(N)]; meanwhile, the method of finding the fallen screw
and reusing it is called process (RT-III(F)]. Immediately after anerror occurs, the first step is to estimate the cause of the error, andthe second is to proceed to the error recovery course based on thespecified cause.

5.2. Suitable Process in Each
Evaluation Standard
In the previous subsection, we considered five types of error recovery processes—(RT-I), [RT-II(N)], [RT-II(F)], [RT-III(N)], and[RT-III(F)]—for screw fallout errors in the task of fixing a hook toa vertical flat plate with four precision screws. Here, the priorities ofthese five recovery processes have been derived for each evaluation
standard that is described in Section 4. For simplicity, priorities are
explained qualitatively, rather than quantitatively.
(i)		 Cost
Priority order = {(RT-II(N)), (RT-III(N)), (RT-II(F)), (RT-III(F)),
and (RT-I)}
The priorities of the five error recovery types under the cost based
evaluation standard have been described in detail in our previous
research [8]. Here, material charges, parts charges, electricity bills,
and planning expenses are considered as costs. The (RT-I) process
is costly, as it discards the entire unit in which the error occurs andstarts over from the beginning. In contrast, the [RT-II(N)] process

is not costly, as there is no search operation or difficult operation
involved.
(ii) Time
Priority order = {[RT-III(N)], [RT-II(N)], (RT-I), [RT-III(F)], and[RT-II(F)]}The (RT-II(F)) process requires a search operation, and must return
to much earlier steps, which is time consuming and unsuitable.
Alternatively, the [RT-III(N)] process does not involve a search
operation, and returns only a few steps; hence, it is the optimum
choice.
(iii) Reliability
Priority order = {(RT-I), [RT-II(N)], [RT-III(N)], [RT-II(F)], and[RT-III(F)]}The (RT-I) process is the most reliable process, as it runs the original sequence from the beginning.
The [RT-II(N)] and [RT-III(N)] processes are the second most reliable after the (RT-I) process, because they do not have search operations and only perform almost planned operations. Conversely,
the [RT-II(F)] and [RT-III(F)] processes are not suitable in termsof reliability, because they require search operations. Moreover,[RT-II(N)] and [RT-II(F)] are more suitable than [RT-III(N)] and[RT-III(F)], respectively, because there are no difficult operations
involved.
(iv) Safety
Priority order = {(RT-I), [RT-II(N)], [RT-II(F)], [RT-III(N)], and[RT-III(F)]}The (RT-I) process is the most desirable in terms of safety,
because it executes the original sequence from the beginning. The[RT-II(N)] and [RT-II(F)] processes are the second safest options,because they do not have difficult operations or become unstable postures. In contrast, the [RT-III(N)] and [RT-III(F)] processes are not suitable, because they require difficult operations.
Additionally, [RT-II(N)] and [RT-III(N)] are more suitable than
[RT-II(F)] and [RT- III(F)], respectively, because they do not
involve a search process.
(v) Finishing
Priority order = {Fundamentally the same for all five}
There is no significant difference in the completion of the sample
task for any of the processes. However, operations that may
adversely affect the target item or its surroundings, such as scratching or scattering, should be avoided.

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A. Nakamura et al. / Journal of Robotics, Networking and Artificial Life 8(3) 211–217(vi) Recovery data
Priority order = {Process with a significant amount of data isprioritized}
The quantity of the dataset is important in deriving the error recovery process, using data science techniques. Even when the operation is considerably difficult to perform, considerable amounts ofdata allow the task to be accomplished successfully.
(vii) Parts
Priority order = {Process of using dedicated parts is given priority}
If dedicated parts are prepared for replacement, it is desirable toprioritize the process of using those parts. For example, suppose
there is a special replacement unit that corresponds to a hook
temporarily fastened with four screws, as shown in Figure 7a–7d.
When an error occurs, the use of the replacement unit simplifies
the recovery task.
(viii) Tools
Priority order = {Preference is given to a process with a dedicatedtool}
Here, we will consider a case in which there is a dedicated tool
for recovery. If a process that uses the exclusive recovery tool isincorporated, it is highly likely that the recovery will be quick andreliable. If there is a special tool that can temporarily fasten thehook with four precision screws in any posture, the [RT-III(N)] or[RT-III(F)] process is given the highest priority.
(ix) Workspace
Priority order = {Process of using the workspace takes precedence}
If there is a dedicated space prepared for replacement work, it isdesirable that the process that utilizes this area be prioritized. In
this assembly task, if there is a space for replacement work near theplace where the failure occurs, the process that uses that region will
be given priority. For example, in the RT-II process, it is efficient touse a dedicated workspace for the process of temporarily fixing thescrews, as shown in Figure 10d, e, r.
(x) Operator skill
Priority order = {Efficient recovery process of the operator isprioritized}
We will consider a recovery process using a teaching operator. It ispreferable that a process, which is the forte of an operator regarding recovery implementation, is prioritized. However, there may bedifferences in the level of efficiency of each operator. Hence, theprocesses suitable for skilled operators may differ from those suitable for unskilled operators.
We have examined the appropriate process by selecting only oneout of the 10 evaluation standards at a time. In practice, however,it may be desirable to derive the optimal process using a combined
evaluation of multiple standards.

6. CONCLUSION
If an error occurs during execution of a task in an automatedplant, the process that executes the main portion moves to therecovery portion. In general, it is possible to obtain various candidates for this recovery process. In this study, we have proposed amethod for selecting optimal candidate based on a type of evaluation standard. Furthermore, 10 different types of evaluation standards have been specifically considered. However, the choice ofindicators was left to the operator. As a specific example, we considered the recovery process in case of the failure of a temporarily
fastened screw falling out during the hook installation process.
In this study, for simplicity, the recovery process was selected individually for each type of evaluation standard. However, it is often
desirable to make selections using multiple types of evaluation
standards, simultaneously. A future study will determine the error
recovery process according to multiple standards.

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

REFERENCES[1] B.R. Donald, Planning multi-step error detection and recovery
strategies, Int. J. Robot. Res. 9 (1990), 3–60.
[2] T. Niemueller, G. Lakemeyer, S.S. Srinivasa, A generic robot database and its application in fault analysis and performance evaluation, 2012 IEEE/RSJ International Conference on Intelligent
Robots and Systems, IEEE, Vilamoura-Algarve, Portugal, 2012,
pp. 364–369.
[3] E.D. Lello, M. Klotzbücher, T.D. Laet, H. Bruyninckx, Bayesiantime-series models for continuous fault detection and recognition in industrial robotic tasks, 2013 IEEE/RSJ International
Conference on Intelligent Robots and Systems, IEEE, Tokyo,
Japan, 2013, pp. 5827–5833.
[4] E. Krabbe, E. Kristiansen, L. Hansen, D. Bourne, Autonomous
optimization of fine motions for robotic assembly, 2014 IEEE
International Conference on Robotics and Automation (ICRA),
IEEE, Hong Kong, China, 2014, pp. 4168–4175.
[5] A.S. Wang, O. Kroemer, Learning robust manipulation strategies with multimodal state transition models and recovery
heuristics, 2019 International Conference on Robotics andAutomation (ICRA), IEEE, Montreal, QC, Canada, 2019,
pp. 1309–1315.
[6] A. Nakamura, K. Nagata, K. Harada, N. Yamanobe, T. Tsuji, T.
Foissotte, et al., Error recovery using task stratification and error
classification for manipulation robots in various fields, 2013
IEEE/RSJ International Conference on Intelligent Robots andSystems, IEEE, Tokyo, Japan, 2013, pp. 3535–3542.
[7] A. Nakamura, K. Nagata, K. Harada, N. Yamanobe, Techniqueof recovery process and application of AI in error recovery using
task stratification and error classification, J. Robot. Netw. Artif.
Life 5 (2018), pp. 56–62.
[8] A. Nakamura, N. Yamanobe, I.R. Alpizar, K. Harada, Y. Domae,
Cost-oriented planning for error recovery in an automation
plant, J. Robot. Netw. Artif. Life 6 (2020), 225–230.
[9] T. Hasegawa, T. Suehiro, K. Takase, A Robot System for
Unstructured environments based on an environment modeland manipulation skills, Proceedings. 1991 IEEE International
Conference on Robotics and Automation, IEEE, Sacramento, CA,
USA, 1991, pp. 916–923.

A. Nakamura et al. / Journal of Robotics, Networking and Artificial Life 8(3) 211–217[10] A. Nakamura, T. Ogasawara, T. Suehiro, H. Tsukune, Fine motion
strategy for skill-based manipulation, Artif. Life Robot. 1 (1997),
147–150.

217[11] A. Nakamura, K. Kitagaki, T. Suehiro, Using simplified geometric models in skill-based manipulation, Adv. Robot. 18 (2004),
835–858.

AUTHORS INTRODUCTION
Prof. Akira Nakamura
He received the PhD degree in ElectricalEngineering from Keio University in 1991.
From 2021, he has been working as a Professor
at Faculty of Engineering of Saitama Institute ofTechnology. His research interests include robot
planning, vision and control system.

Dr. Natsuki Yamanobe
She received the PhD degree from the Universityof Tokyo in 2007. She is currently a SeniorResearch Scientist with Artificial Intelligence
Research Center of Advanced Industrial Scienceand Technology (AIST).

Dr. Ixchel G. Ramirez-Alpiar
She received her PhD degree in MechanicalEngineering from the Graduate School ofEngineering, Osaka University, Japan, in
2013. Since 2019, she has been working as asenior researcher at the Artificial Intelligence
Research Center of AIST. Her research interests
include robot learning from demonstration,
robotic manipulation and human–robot collaboration. She is amember of IEEE and RAS.

Prof. Kensuke Harada
He received his Doctoral degrees in MechanicalEngineering from Kyoto University in 1997.
From 2016, he has been working as a Professor
at Graduate School of Engineering Science,
Osaka University.

Dr. Kazuyuki Nagata
He received his PhD degree from Hokkaido
University in 2014. He is currently a teamleader with Artificial Intelligent ResearchCenter (AIRC), National Institute of Advanced
Industrial Science and Technology (AIST).