collision detection algorithm in a linear motionclassification problem, the random forest performed...

Collision detection algorithm in a linear motion for electrical height adjustable tables

MAIN SUBJECT AREA: Computer Engineering AUTHOR: Jimmi Åhnström, Ramsen Nissan SUPERVISOR: Adam Lagerberg JÖNKÖPING 2021 August

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This final thesis has been carried out at the School of Engineering at Jönköping

University within Computer Engineering. The authors are responsible for the presented

opinions, conclusions and results.

Examiner: Rachid Oucheikh

Supervisor: Adam Lagerberg

Scope: 15 hp (first-cycle education)

Date: 2021-08-15

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Abstract

The current collision detection system (CDS) that ROL Ergo AB is using for their

height adjustable tables can be improved. The manufacturing process of the legs makes

today's CDS not accurate enough to detect collisions, because the metal that is used in

manufacturing has slight variations between them. This causes frictions across the leg,

which leads to current surges and that could trigger a false positives collision.

This study proposes different methods to solve this problem and methods that could be

used as a new CDS for a height adjustable table, or any other linear moving object. The

proposed methods are: one traditional algorithm with two different thresholds, and two

different AIs.

The two thresholds for the traditional algorithm where one based on a calibration phase

that is static, and one based on shifting all values and comparing against itself. The

algorithm that used the static threshold could not find light collisions in the middle of

the table, but could find all heavy collisions across the table. The algorithm that used

the shifted threshold could find all collisions across the table, both light collisions and

heavy collisions.

The second method based on AI was decision tree and random forest. For this binary

classification problem, the random forest performed better. Both AI algorithms could

find all collisions, however, it had a few false positives that might result in the table

stopping when there is not a collision. While both the traditional algorithm and AI

manage to find all simulated collisions, more testing is required to determine which of

the solutions performs best.

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Table of content

1 Introduction 7

1.1 PROBLEM STATEMENT 7

1.2 PURPOSE AND RESEARCH QUESTIONS 8

1.3 SCOPE AND LIMITATIONS 9

1.4 DISPOSITION 9

2 Theoretical framework 10

2.1 LINK BETWEEN RESEARCH QUESTIONS AND THEORY 10

2.2 RELATED RESEARCH 10

2.3 Current measurement with shunt resistor 10

2.4 HALL EFFECT SENSOR 11

2.5 SIGNAL PROCESSING AND FILTERS 11

2.6 ALGORITHM FOR RQ1 12

2.6.1 Static Threshold 13

2.6.2 Shifted Threshold 13

2.7 DATA MINING 13

2.7.1 Binary classification with Decision trees 14

2.7.2 Binary classification with Random forest 15

3 Method and implementation 16

3.1 LITERATURE STUDIES 16

3.2 EQUIPMENT 16

3.3 DATA COLLECTION 17

3.3.1 Dataset 18

3.3.2 Collision Simulation data 19

3.4 DATA ANALYSIS 20

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3.4.1 Analysis RQ1 20

3.4.2 Analysis RQ2 20

3.5 VALIDITY AND RELIABILITY 21

4 Results 22

4.1 RQ1 – SINGLE POLE 22

4.1.1 Data Collection 1 22






4.2 RQ1 – SMA 28





4.3 RQ2 32

4.3.1 Decision tree data collection 33

4.3.2 Random forest data collection 34

5 Discussion 36

5.1 RESULT DISCUSSION 36

5.1.1 Research question 1 36

5.1.2 Research question 2 37

5.2 METHOD DISCUSSION 38

6 Conclusions and further research 39

6.1 CONCLUSIONS 39

6.1.1 Scientific implication 39

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6.2 FURTHER RESEARCH 40

7 References 41

8 Appendixes 44

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Abbreviations

CDS Collision detection system

𝑈e Electrical energy in Joule

Up Potential energy in Joule

M Mass in kg

t Time in seconds

g Gravity constant

P Power

L Length in meter

U Voltage

I Ampere

I2C Bus communication

CSV Comma separated values file

AI Artificial Intelligence

SMA Simple moving average

RQ1 Research question one

RQ2 Research question two

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1 Introduction

ROL Ergo AB has since 1985 developed and produced office equipment and store

interiors to offices and stores across the world. ROL Ergo AB mainly manufactures

height adjustable tables which have a lift capability of 120kg. That force alone is

enough to injure a human or an object if it is in the way when the table is moving.

Figure 1: ROL Ergo AB, Height adjustable table

To prevent an accident, a collision detection system (CDS) is used to stop the table from

injuring or damaging a human or an object. When a collision is detected, the table stops

moving and moves a couple of centimeters in the opposite direction from where the

collision was detected.

The CDS has two different methods to detect a collision. First method is by measuring

the current that is drawn by the motor. If there is a current surge, the table will detect it

as a collision and move in the opposite direction from where it detected the collision.

The second method is by using a piezo sensor that is mounted in the middle of the table

under the tabletop. The data from the piezo sensor are the movement in the tabletop

with a reference to the table stand. This makes the collision easier to detect than the

current sensing, as no disturbance from moving the table or friction is generated.

1.1 Problem statement

The table legs are made of metal tubes. These metal tubes could have slight variations

between them and could be slightly curved at different locations. This could cause

different variations in friction over the length of the metal tube due to small changes in

dimensions between the inner and outer tubes. In the process of manufacturing a leg,

there is a welding process that could contribute to making the tubes more tight, which

leads to more power usage to drive the legs due to that metal can shrink in the welding

process. All of these friction points could trigger the collision detection because of the

rapid power changes between different points in the leg.

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The CDS that is used today can be improved so it takes the manufacturing process into

consideration. When the CDS is active it could trigger false positive collisions. This is

because every leg that is manufactured is unique, and friction varies between every leg.

When a collision occurs in the center of the table, the force of the collision is divided

over both legs which makes it more difficult to detect a collision. That is why the system

needs to be improved so that a collision can be detected even if there is variation of

friction in the legs.

This study aims to develop a system for collision detection in a linear moving object.

The proposed CDS purpose is to find anomalies in the sampled data and make a binary

decision. This method is used across various other systems and not just for a CDS in a

table. According to Oucheikh et al., (2020) anomaly detection is used in several fields,

for example transport, healthcare, banking fraud and in industrial anomalies detection.

However, when creating the CDS it is difficult to look at other collision-avoidance

algorithms since the CDS for a linear moving object is not similar to the CDS created

for automobiles and robotics (Duy et al., 2020).

1.2 Purpose and research questions

Drawing on the problem statement, it is evident that the collision detection system that

is currently used is not accurate enough to find collisions at different heights. Further,

it is evident that the algorithm that is used today does not consider the manufacturing

process of the legs and the deviations on the raw material. Consequently, the purpose

of this study is:

To find a solution to improve the sensitivity and accuracy of the collision detection

system in a linear motion of an electric height adjustable table.

The collision detection algorithm that is used today is insufficient to find true collisions

in a linear motion, therefore the first research question is:

[1] What kind of algorithm could be used to improve the detection of collisions

in a linear motion?

The raw material that is used in manufacturing could have different variations, therefore

the power usage could vary in the linear motion of the leg. An AI could be used to learn

how the power usage varies across the electrical height adjustable, therefore the second

research question is:

[2] Is there any difference between a binary classification AI and the selected

algorithm from question [1] when used to detect collisions in a linear motion?

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1.3 Scope and limitations

This study will focus on improving and finding true collisions in a linear motion through

software development and by using existing sensors in the leg by measuring power

usage from the motors.

The study will not use any other type of sensors in order to find collisions in a linear

motion. This is because using more sensors will make the end product more expensive.

Using less parts makes it easier for the end customer to set up the table. The table is

capable of loading 120kg but in our test we will only put a realistic load.

This thesis will not take friction into account in the CDS, since the table is constructed

to have a dynamic load. Therefore, it is hard to determine the friction of the table when

changing the load on the table. There are also several points of friction to take into

consideration which is outside scope for this thesis to calculate. The formula will work

by measuring the power usage from the motor which correlates to the friction.

1.4 Disposition

This study has six additional chapters that will describe the thesis in more detail.

Chapter two is about theoretical framework and gives basic knowledge for the relevant

theories for understanding the technical aspects. Chapter three goes into detail on the

method and implementation, here the studies work process, software, equipment, data

gathering, data analysis and trust ability is described. Chapter four is the results, and

the collected data is presented in the chapter. Chapter five discusses the results and

implications of the study and limitations. Chapter six describes the Conclusions of the

study is presented and suggestions for further research.

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2 Theoretical framework

2.1 Link between research questions and theory

The following theories were described to get a theoretical foundation for the research

questions and are about DC motors, hall effect sensors, data filtering, data mining

techniques, current measurement, and related research. All chapters up to 2.5 are more

specific for the first research question but could also be applied for the second. Chapter

2.7 is more specific for the second research question and describes data mining and the

chosen binary classification method.

2.2 Related research

The related research papers describe the same problem to improve the CDS without

adding external sensors to keep the costs low. They describe how their model will work

for robotic arms and the collision detection is based on the friction model on the joints

(Lee et al., 2016; Xiao,.al, 2018; Gylling, 2019). This research paper will utilize the

same concept, but the problem statement is that a power usage model is needed for a

linear motion instead of a robotic arm joint.

The related research papers compared a sensorless solution with an external sensor to

get a high validity on their solution (Lee et al., 2016; Xiao,.al, 2018; Gylling, 2019).

Their conclusion was that a sensorless algorithm performs just as good as a solution

with an external sensor. But none of them tried an AI solution for the CDS. In the

conducted research for related work for an AI CDS, no related research was found for

the specific problem.

For the AI solution there are several research papers that utilize different binary

classification methods to solve other kinds of problems. But no papers regarding

something similar to a CDS in a linear motion. Allberg (2018) compares different

frequently used algorithms, and it suggests that the performance is similar when used

for binary classification. It also mentions that large datasets improve the accuracy and

precision of the AI used for classification.

2.3 Current measurement with shunt resistor

A shunt is an electrical device that creates a low-resistance path for electrical current

and is commonly used to measure electrical current (A Guide to Shunt Resistors, n.d.).

When current passes through the shunt resistor, it creates a voltage drop over the shunt

resistor which can be measured, and with ohm’s law U=I*R calculates the current

passing through the shunt resistor (Teschler, 2018). This thesis will utilize a module

with an INA260 current measurement IC (INA, n.d). The IC has an integrated shunt

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resistor. The current and voltage measurement was used to calculate the power used by

the motor with the following formula: 𝑃 = 𝑈 ∗ 𝐼

2.4 Hall effect sensor

The hall effect sensor is the most common sensor for measuring magnetic fields (Lenz.,

2006). The hall effect sensor is an integrated circuit that varies its output by measuring

the magnetic field passing through the sensor and outputs a logic level signal. The paper

from Forsberg (2016) explains that the hall effect is a manifestation of the Lorentz

force, where the charge carriers in a conductor are sensitive to an external magnetic

field. The Lorentz force is an integration of electric and magnetic force on a point

charge because of electromagnetic fields according to the equation (Britannica, 2020).

F = qE + qv × B

Figure 2: Illustration of the hall effect in a conductor

From the figure 2, you can see a rough scheme of a Hall sensor and its working

principle. As it states in Forsberg (2016) “Negative charge carriers accumulate on one

side of the conductor, and the absence of such on the opposite side results in

accumulation of positive charges. This gives rise to a measurable voltage drop across

the conductor.”. The hall sensor is used in the motors of the table to count pulses. The

pulses are used as a measurement to know at which height the table is at.

2.5 Signal processing and Filters

Before gathering data from electrical components, the data is often required to be

processed in some ways before it can be of any use (Ivanic, 2008). This is called signal

processing and is used in the field of electronics to analyze, modify, or synthesize a

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signal to improve transmission or subjective quality. Typical devices that help with this

are samplers, signal compressors, digital signal processors and filters.

This study will utilize a first order filter to reduce noise after the data has been collected.

The filter algorithm used is a low pass single pole filter to smooth out the data and is

computationally efficient (Roelandts, 2016). The low pass singe pole algorithm works

by using the following formula:

𝑦[𝑛] = (1 − 𝑑) ∗ 𝑥[𝑛] + 𝑑 ∗ 𝑦[𝑛 − 1]

Where the decay value is d, x[n] is the input, and y[n] is the output of the filter.

The second filter that this study will utilize is a simple moving average (SMA) filter

that calculates the mean of average values (Fernando, 2021). The moving average

formula works by using the following formula, A is measured value in period n, n is

number of periods:

𝑆𝑀𝐴 =𝐴1 + 𝐴2+. . . +𝐴n

𝑛

2.6 Algorithm for RQ1

The algorithm was utilized to detect if there is a collision or not. All values above a

threshold will count as a collision. To set a value suitable as a threshold, the table will

have to collect data (see Appendix 1) while moving up and down without load or

collisions. It can be compared to a calibration phase the first time the table is used.

From the conducted literature research, there was an algorithm from this related study

(Lee et al., 2016). But in this study it is difficult to calculate the friction on the table

because the load can vary which makes it more difficult to determine the friction of the

table. Therefore, this study will use this formula based on potential energy without

taking the friction into account compared to the related study (Lee et al., 2016).

The algorithm for the first research question is modelled by calculating the mass by

using the potential energy and the energy required to move the motor. This was done

using the parameters from the data table described in chapter 3.3.1. The calculation was

based on the following potential energy formula: 𝑈p = 𝑀 ∗ g ∗ 𝐿

Formula to calculate how much energy is needed to move the motor in Joule:

𝑈e = 𝑃 ∗ 𝑡

With these formulas it is possible to calculate the mass that the motors are lifting:

𝑃 ∗ 𝑡 = 𝑀 ∗ 𝐿 ∗ g

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The algorithm uses this formula to calculate the mass with the collected data. Only

absolute values from the formula, since moving down will result into negative energy:

𝑀 = |𝑃 ∗ Δ𝑡

g ∗ ΔL|

2.6.1 Static Threshold

In chapter 3.3.1 it is described what kind of data that will be collected for the analysis,

and that the datasets with no collisions is used to set a threshold value. The algorithm

implemented uses the threshold value as a reference and if the newly collected data is

greater that the threshold value, it will count that data as a collision.

2.6.2 Shifted Threshold

The shifted threshold is a dynamic threshold based on previous values. It first stores 10

incoming values, and every value after that will be compared against the value that was

stored 10 points behind.

Example of 12 values in an array:

In this case, value 11 will compare its value against value 1, and value 12 will compare

its value against value 2 etc. It works similarly to how a shift graph is implemented

(Erdös et al., 1968). To not let small spikes count as a collision, the threshold is also

increased by 5%.

2.7 Data mining

Data mining should not be confused with the term mining, since it does not have

anything to do with the extraction of data, but rather how you handle data that has been

extracted, and gain new knowledge from it (Han., 2012). Similarly to signal processing,

the goal of data mining is to extract useful information from data sets, but while using

intelligent methods (Clifton, 2019).

The simplified process of extracting useful information are as follows:

1 Pre-processing

2 Data mining

3 Results Validation

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The pre-processing of raw data includes filtering out noise, merging different data

together and removing duplicates or missing values. The purpose of this is to make it

into an appropriate format for the selected data mining (Tan et al., 2019).

In this report the data mining techniques that will be included are predictive tasks with

classification as its main focus.

2.7.1 Binary classification with Decision trees

A decision tree can be used for both regression and classification problems. The

classification can be divided into binary classification and multi-class classification, but

in this report, only binary classification is used.

The decision tree algorithm can be described as a hierarchical collection of decisions.

It ranks all observations based on relevance and then divides it into successively smaller

groups of observations. The idea is that with each division, the observations in the

groups become increasingly homogeneous (Gordon et al., 2000). The observations in

our case are all different signals or parameters collected, see chapter 3.3.1 for

explanation.

The process of dividing can be compared to a series of questions. The first node or

question is the most relevant, and after each true or false answer, a follow-up question

is asked. This keeps occurring until the tree reaches one conclusion about which class

the observation in question belongs to (Tan et al., 2019).

Figure 3: Overview of how a decision tree could look like and its data set.

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2.7.2 Binary classification with Random forest

Random forest can also be used for classification. It is an ensemble of decision trees,

therefore the name forest. Meaning the same principle from decision trees applies here.

But to get a slight difference for each decision tree in the forest, some variables or

true/false statements will be slightly modified (randomized). A large part of the

algorithm's accuracy lies in the fact that the randomly generated trees analyze how

common the different labels are (Breiman 2001).

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3 Method and implementation

Initially a pre study with literature research of previous research is done to get a

comprehensive theoretical foundation. The literature research is used to find and

compare different existing CDS using algorithms and AI/Machine learning techniques,

and it will also make future software development iterations more effective.

The method used in this research is design science. The design science method

describes that an artifact is created first. The artifact in this case is an algorithm for an

electrical height adjustable table, that should be able to detect true collisions in a linear

motion. To succeed with this, an algorithm will be implemented in a Raspberry Pi, that

will collect data at different heights of the leg. The software development will be

implemented iteratively. The different algorithms will later be compared with each

other to check which of them has highest accuracy in finding most true collisions.

3.1 Literature Studies

The information was collected from various sources as below:

• Literature from the university's library database and DIVA database

• Google Scholar for related articles and reports.

• Datasheets for components, articles and explanations from the manufacturer.

Although most of the sources were articles or application notes taken directly from

manufacturers, they were compared with other sources to confirm that the information

was reliable.

3.2 Equipment

An electric height adjustable table was used in this study and is provided by ROL Ergo

AB see figure 4. The height adjustable table used in this study has a main unit that is

used to control the height of the table. It controls the motors in the legs and has a port

to connect a device to move the table up and down. handset is used to control the table

up and down.

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Figure 4: ROL height adjustable table

The software within the Raspberry pi system is implemented using Python. The

software was used to collect data. The raw data were saved for further analysis on a PC.

The I2C bus described in figure 5 is used to communicate with the current sensing

modules that are connected inside the ECS+ control unit. The current sensing module

utilizes a INA260 IC with integrated shunt resistor (INA, n.d).

Figure 5: Overview of the system

3.3 Data collection

The following data collecting method were used in both research questions, but the data

is analyzed differently. The main function of the raspberry pi is to collect data from

ECS+ box and current sensing modules.

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The Raspberry pi uses UART communication to interface with the ECS+. The data

collected from the ECS+ are the following:

• Leg one pulses.

• Leg two pulses.

• Moving direction.

To measure the voltage and current drawn, an INA260 module is used for each leg. The

information is then sent by an I2C bus to the raspberry pi. The INA260 is connected in

series to ground for the motor controller and measures the current drawn on the low

side see figure 6. The INA260 is connected inside the ECS+ that has all the logic to

control the legs.

Figure 6: Overview of how the INA260 is connected.

A button is connected to a GPIO of the raspberry pi. The button is used as a control

variable for the algorithms to know if there was a collision there or not.

3.3.1 Dataset

The collected dataset is in CVS format. Each row in the dataset were from the following

attributes: Epoch time, current leg 1, current leg 2, voltage leg 1, voltage leg 2, pulse

leg 1, pulse leg 2, direction and the collision button; that will serve as a control variable

(see Appendix 1).

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Parameter Explanation

Epoch time Time stamp

Current Leg 1,

Current Leg 2

The current measured from the table's motors. Is an integer

datatype.

Voltage Leg 1,

Voltage Leg 2

Voltage measured across the motor. Is an integer datatype.

Pulse Leg 1,

Pulse Leg 2

The height the leg is currently on.

Direction Moving direction of legs

Collision button A control variable for the algorithms to know if there was a

collision there or not.

Several datasets will be collected, and each dataset will be within the following

categories:

• No collisions moving up.

• No collisions moving down.

• Collisions when moving down.

• Collisions when moving up.

3.3.2 Collision Simulation data

To simulate collisions in the datasets that will be used to test the different algorithms,

obstacles and physical pushes from humans was performed on the table. Both when it

moves upwards and downwards. The weights are placed on different predefined spots

see figure 7 below. When the table is moving upwards, weights are placed of 5 kg and

10 kg. When moving down, a newton meter measuring tool is used to measure

accurately how much force is pressed upwards against the table when it is moving

down.

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Figure 7: Drawing where obstacles were placed when collecting data, top

view of table

3.4 Data analysis

3.4.1 Analysis RQ1

The data is parsed by reading in the CSV file and processing the data with the algorithm

from 2.6. The output from the algorithm is then filtered through the low pass single pole

filter or SMA filter. It is then compared against the static threshold or shifted threshold.

All the values above the set threshold will count as a collision.

The different filter methods have some parameters that need to be set before they can

be used to filter the raw data. Both the decay parameter and average parameter to the

SMA filter was selected through an iterative process. The chosen values that worked

best to smooth out the data on the graph were selected.

Last step is to present the output on a graph to help with the analysis and show on the

graph where collision occurred. The values shown represents how the algorithm would

behave if implemented practically. For every “X” on the graph the CDS would have

decided that it was a collision, see figure 8 for an example.

The data analysis for RQ1 is implemented in Python programming language.

3.4.2 Analysis RQ2

This method will use the collected data tables and train a decision tree and random

forest algorithm described in chapter 2.7.1 and 2.7.2. The algorithms will decide if it is

a collision or not. The method represented was written in Python programming

language. Using the Jupyter environment (Jupyter, 2021), with libraries from scikit

(scikit, 2021).

The AI will use a large dataset consisting of multiple smaller datasets from the different

experiments mentioned in dataset and collision simulation chapters. The large dataset

will hence contain both collision and non-collision data.

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The method for training and testing were done using the “train_test_split” function from

scikit. It works by randomly taking different rows from the big dataset and putting it in

four smaller datasets that are used for training and testing. Two datasets are used for

training and two datasets are used for testing.

One dataset for each category will contain all parameters mentioned in chapter 3.3.1

except the collision button parameter. Another dataset for each category will only

contain the collision button parameter. The datasets with the collision button were used

as a correcting sheet, to validate how well the AI performed.

The AI was first tested with standard scaling on, meaning all values from the data table

was converted to a value between 0 and 1. Then tested with standard scaling off. Since

it had better accuracy without standard scaling. All tests and results are from non-

standard scaled values only.

3.5 Validity and reliability

To ensure that the sensors are measuring according to their specification from its

datasheet, they were tested in a lab environment before the data collection started. This

was done to get a higher validity of the data.

The method that was used to collect the data is well documented and the sensors used

in this thesis are accessible. The data processing that is used in this thesis is documented

and choice of data processing is documented for a higher reliability.

By following the methods documented, together with the information from the

theoretical framework. An outsider should be able to reproduce the algorithms and tests,

while also get the same results that is shown in this report.

The algorithm used in this study for RQ1 does not take friction into account. This

makes the output to not be accurate (the absolute value translation of a true event).

Having a friction variable in the calculation would give the accurate result. But for

this problem statement, having an absolute true value would not make the collision

detection perform much different.

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4 Results

4.1 RQ1 – Single Pole

The following figures for RQ1, chapter 4.1 and 4.2 are displayed as following

Graph Threshold Value Line Color

First graph Threshold Value for leg 1

Collected data for leg1

Threshold leg 1 – Black line

Value leg 1 – Green line

Second graph Threshold Value for leg 2

Collected data for leg2

Threshold leg 2 – Black line

Value leg 2 – Blue line

Third graph Collected data for leg 1

Collected data for leg 2

Collision button

Value leg 1 – Green line

Value leg 2 – Blue line

Button – Black line

The threshold value on the static thresholds, is from a data set without any collisions,

similarly to the ones shown in figure 12. The different values (Value leg 1 and Value

leg 2) shown on the y-axis of the graphs, are the filtered output value from the algorithm

explained in chapter 2.6. All graphs in chapter 4.1 are presented using the single pole

filter, and all graphs in chapter 4.2 are presented using the SMA filter. On the x-axis of

all graphs is the pulses, which represents the height the table is currently on. Using a

time comparison, it takes about 2.5 to 3 seconds to move 100 pulses.

4.1.1 Data Collection 1

The principle from chapter 3.3.2 was used here, and a 5kg weight was placed for a short

period of time from left to right in a slight diagonal. At the same time, the collision

button would be pressed as shown in the last graph of figure 8.

As seen in Figure 8 the x-axis starts at pulse 200 and ends at pulse 1600, meaning the

table is going upwards (higher pulse is higher height). The y-axis starts at 110 KG and

ends at 200 KG. The KG is the output from the algorithm for RQ1, and it was used to

calculate all values from both “Value leg X” and both “Threshold LX”.

The first graph shows the values from leg one, in this case, that is the left leg. The

second graph shows values for leg 2 (right leg), and the third graph is values from both

the legs, but without the threshold.

the threshold value starts around 150 KG and ends at around 140 KG.

As seen the second graphs threshold is slightly higher, meaning that the right leg in the

table that was used, had slightly higher friction across its leg.

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Figure 8: +5% increase on the threshold value and 5kg collision when table is moving

up, single pole filter


This time, a 10kg weight was put on the table for a short period of time from right to

left in a slight diagonal. With the same collision threshold as in the chapter 4.1.1 dataset.

Here all three collisions would have been detected by the algorithm.

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Figure 9: Threshold +5% increase, and 10kg collision when table is moving up, single

pole filter


This data collection is collected according to chapter 3.3.2 where a 5kg weight was used

as collision. Instead of using a static threshold like the previous data collection this time

a shifted threshold was used described in 2.6.2.





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Figure 10: Shifted +5% increase, and 5kg collision when table is moving up, single

pole filter


This data collection is collected according to chapter 3.3.2 where a 10kg weight was

used as collision. Instead of using a static threshold like the previous data collection

this time a shifted threshold was used described in 2.6.2.



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Figure 11: Shifted +5% increase, and 10kg collision when table is moving up, single

pole filter


The no collision data was used as a threshold value to detect collision. In figure 12 there

are two datasets with no collision when moving up. Test 1 was one of the first tests

done in the day and the data shows that the threshold is around 140-150 kg. Test 2 was

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done later in the day, after many other tests made for data collection and the data shows

that the threshold is around 130-140kg. As seen in Figure 12 the x-axis starts at pulse

200 and ends at pulse 1600, meaning the table is going upwards (higher pulse is higher

height).

Figure 12: Overview results of test 1 and test 2 when the table is moving up, single

pole filter


Example table with collisions on a table moving downwards (from pulse 1600 to pulse

200). This was made with 4 simulated collisions with a push force of around 200N from

underneath. A digital Newton meter was used to measure the push force.

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Figure 13: Simulated 200N collisions during downward movement, single pole filter

4.2 RQ1 – SMA

The following graphs are made with the same algorithm, threshold, and datasets as in

chapter 4.1. The only difference is that the SMA filter is used instead as described in

chapter 2.5.


The principle from chapter 3.3.2 was used here, and a 5kg weight was placed for a short

period of time from left to right in a slight diagonal. At the same time, the collision

button would be pressed as shown in the last graph of figure 8.







the legs, but without the threshold. The threshold value starts around 150 KG and ends

at around 140 KG. As seen the second graphs threshold is slightly higher, meaning that

the right leg in the table that was used, had slightly higher friction across its leg.

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Figure 14: Static threshold +5% increase, and 5kg collision when table is moving up,

SMA





30

Figure 15: Static threshold +5% increase, and 10kg collision when table is moving up,

SMA


This data collection is collected according to chapter 3.3.2 where a 5kg weight was used

as collision. Instead of using a static threshold like the previous data collection this time

a shifted threshold was used described in 2.6.2.



31






Figure 16: Shifted threshold +5% increase, and 5kg collision when table is moving

up, SMA





32

Figure 17: Shifted threshold +5% increase, and 10kg collision when table is moving

up, SMA

4.3 RQ2

The datasets used in RQ2 is the same data used for RQ1 for collisions when the table

is moving up. The following chapters displays the result for RQ2, and they are presented

using a confusion matrix.

The confusion matrix shows the total values used on the right side. The value here

represents one row from the dataset, as explained in chapter 3.4.2. The row with all its

parameters was either a collision or not. The actual collisions are presented on the

horizontal while the predicted collisions are presented on the vertical.

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4.3.1 Decision tree data collection

The figure below (figure 18) has 1051 rows with no collisions, and 61 rows with

collisions. The AI then predicted 1039 of the no collisions as no collisions, and 50 true

collisions as collisions. Meaning 12 of the not collisions was wrongly guessed, and 11

of the collision was wrongly guessed.

Figure 18: Overview of the results from decision tree

Figure 19 is the console output when running the AI algorithms from the scikit library.

F1-score is a measurement of the test’s accuracy, and its value is a combination of the

precision and recall values combined. The precision is calculated by having all true

positive results and divided it by the number of all positive results (it also includes the

wrongly identified positives). The recall is calculated by having all number of true

positive results and divided it by the number of all samples that should have been

identified as positive. The support only states how many values of each type there is, in

this case, 1051 no collisions, and 61 collisions. However, this thesis will only discuss

the tests accuracy, and in this particular test it had an accuracy of 98%.

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Figure 19: Statistics for figure 14.

4.3.2 Random forest data collection

Since the random forest used the same dataset as decision tree, the total number of

collisions and no collisions was the same, meaning 1051 rows with no collisions, and

61 rows with collisions. This time the AI predicted 1047 of the no collisions as no

collisions, and 53 true collisions as collisions. That means that 4 of the not collisions

were wrongly guessed, and 8 of the collision was wrongly guessed.

Figure 20: Overview of the results from random forest

This is the same console output when running the AI algorithms from the scikit library,

but for the random forest test. As explained for figure 19, this thesis will focus on the

test’s accuracy. For the random forest test, its overall accuracy was 99%.

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Figure 21: Statistics for figure 16.

36

5 Discussion

5.1 Result discussion

The purpose of this study was to find a solution to improve the sensitivity and accuracy

of the collision detection system in a linear motion of an electric height adjustable table.

5.1.1 Research question 1

Analysis of research question 1: What kind of algorithm could be used to improve the

detection of collisions in a linear motion?

The implemented algorithm is inspired from the formulas that was used in the related

research (Lee et al., 2016; Xiao,.al, 2018; Gylling, 2019) for robot arms. However, their

algorithm measures motor torque, but the proposed algorithm for a linear motion

measures mass instead. The reason for measuring mass is because a table or other linear

moving objects is usually made for loading with different weights. Compared to a robot

arm that will not be loaded, and therefore have a similar weight all the time.

By using a similar collision detection using a friction model, as described in related

research (Lee et al., 2016; Xiao,.al, 2018; Gylling, 2019) is difficult to implement. This

is because the friction in table legs were much more varied compared to the friction in

a robot arm. Another thing is that a robot arm has a set weight, but a table is meant to

be loaded with different weights, which also impacts friction.

The reason the related studies (Lee et al., 2016; Xiao,.al, 2018; Gylling, 2019) wanted

to take friction into account is to be able to put a static threshold very close to the signal

values. However, the used motors power varies greatly. This could be due to the

efficiency of the leg, and that the leg heating up makes the efficiency higher. This

happens when moving the table up and down several times without letting the legs cool

down. For this reason, a static threshold is a poor solution. The first test in the figure 12

was collected at the beginning of the day, and the second test was done at the end of the

day. Both, without any weights on them. This shows that the efficiency increases as the

legs heats up and that the data is not consistent.

With about 20-25% motor efficiency, a 5kg will require power to lift 20-25kg. And

with a 10kg, it will require power to lift 40-50kg, like seen in the graphs. If the motor

efficiency was 100%, then adding 5 kg load would result +5kg in graph.

From the results when moving down, see chapter 4.1.6. The algorithm could not detect

a collision when moving down and simulating an obstacle of 200N. Simulating a higher

force would result in the table legs lifting from the floor, meaning no higher power

usage could be achieved and there would be no changes in the output.

As described in the problem statement, the current CDS had troubles detecting

collisions in the center of the table because the load splits up equally between each leg.

But finding collisions at the edges was easier. In figure 8 the light collision was not

37

detected in the center, but the collisions on the side was. If a collision test was done

right after the threshold would have been set, then all collisions would have been

detected.

The shifted threshold was made with the varying efficiency in consideration. What

makes this alternative better is that it does not matter how high or low efficiency the

motors have. Since it compares with itself, rather something pre-defined like the static

threshold. It also has no problem finding collisions in the middle of the table. The

counterpart of this alternative however, is that it will have a slight delay of collision

detection. The finetuning of the increased value and amount shifted must be tested more

thoroughly. But in the example above, it is a delay of 2.0 mm of height. In practice, this

means that the collision occurs, and the table will keep moving 2.0 mm before the

collision is detected.

Comparing the two threshold methods, the shifted threshold has a set delay of 2.0 mm

where the static threshold has a varying delay. This depends on how hard the collision

was. Looking at figure 8 and 14 (5kg collision), the first collision happened around

pulse 380, but it took to pulse 400 before the value got above the threshold, and was

counted as a collision.

Comparing the output from the two different filter algorithms SMA and single pole,

showed no big difference. Both could detect the same collisions. But single pole would

technically be better in practice since it only needs to save a single point in memory.

This is due to how that filter works. The SMA however, needs as many points in

memory as used as “N” value (see chapter 2.5). This will result in a more memory heavy

computation and the first outputs will not be reliable since it does not have enough

points to make a smooth averaging yet.

The proposed solutions that this study has conducted will improve the current solution

used today for detecting collisions in a linear moving object. The solution that is used

only looks at the current draw of the motor without using any type of filtering or

combing it with how fast the table moves. By using filtering and combing how fast the

table moves, the proposed algorithm to detect collisions is more reliable to detect

collisions in a linear moving object compared to the old solution.

5.1.2 Research question 2

Analysis of research question 2: Is there any difference between a binary classification

AI and the selected algorithm from question [1] when used to detect collisions in a

linear motion?

In the confusion matrix (figure 18 and 20) there were a few falsely predicted collisions

and falsely predicted non-collisions. However, if the AI was used practically in a linear

moving object. It would be enough to find one collision point, for each collision. With

an accuracy of 98 and 99 percent, it is safe to say that the AI would find the collision.

38

In the literature studies that were conducted, Allberg (2018) mentioned that an AI

performs similarly when used for binary classification. From the figures 18 to 21 the

results shown in the matrix and statistics align with Allberg’s results. That their

performance is similar. But for this specific classification problem, the random forest

performed slightly better than the decision tree algorithm.

Something to consider is that the AIs train data and test data was based on the collision

button that was human clicked. In the last graph from figure 8 to 17 the collision button

is not precise on where the collision actually occurred. A better reference or answer

sheet for the AI to train on, would likely lead to even better results.

5.2 Method discussion

The conducted research used design science as a research method, design science

describes that an artifact is created. In this case an algorithm that combines the collected

data to set a threshold, which every value above it would count as a collision. The

created artifact was developed iteratively.

Data collected was used to test the algorithm, and the data analysis helped to see how

well the collision detection algorithm worked. Thus, the methods that were chosen

provided the necessary tools to answer the research questions.

The collision button data was both used as a control variable to test how well the

developed algorithm worked and to train the AI to determine if the data correlated with

a collision. Meaning, if the button is not pressed precisely when a collision starts and

ends, could lead to making the AI learn wrong if there is a collision or not. A better way

to train the AI would be to use additional sensors. This could improve the AI to detect

collisions more accurately.

39

6 Conclusions and further research

6.1 Conclusions

In the problem statement it was mentioned that the current solution that exists has

troubles finding collisions in the middle of the table. The created algorithm also had a

hard time to find collisions in the middle during lighter collisions. However, when

implementing the shift threshold version, it could find collisions across all places on

the table.

Since both the created algorithm and the AI could find all collisions, it might not be

necessary to use an advanced AI for problems with few variables and less uncertain

elements.

It is hard to tell if there are any performance differences of the algorithms since the data

was not accurate enough to make a fair judgement. Both algorithms performed good,

and all collisions would be detected from both the created algorithm and the AI.

The AI did classify a small amount of data wrong. However, in a practical scenario, it

would be enough to find one collision point, for each collision. With an accuracy of 98

and 99 percent, it is safe to say that the AI would find the collision. Before testing it in

a practical environment, there is not enough data to judge if it would make any “false

positives” judgements during table usage. The AI would likely be 100% accurate if the

button data were pressed precisely when a collision occurred and ended.

From the results the proposed CDS from this study could only detect collisions when

the table moved up. This is because when placing an obstacle under the table during

downwards movement, the results would be that the table legs would lift. Either the

table would be balancing on the obstacle, or it would cause the table to topple.

Therefore, additional sensors are needed to detect collisions when the table is moving

down.

6.1.1 Scientific implication

The implication of the conducted research shows that it is possible to improve the

collision detection in an electric linear moving object. Both with a regular algorithm

and an AI, without adding any extra sensors that can make the end product more

expensive. This could improve other types of equipment that moves linearly to prevent

collisions or other accidents. Example of a linear moving object could be an elevator or

a scissor lift.

40

6.2 Further research

Both the created algorithm and the AI should be implemented in a linear moving object.

Then tried in an experimental study with practical tests and more precisely made

collisions.

Another thing could be to collect more data at different loads and heights and use it for

testing. Then compare that results to conclude if the algorithms perform better or worse

at specific loads.

This study's goal was to create a CDS without adding more sensors. But further research

could include more sensors and compare its results with the results from this study. Or

use the proposed algorithms from this study together with other sensors to see if it is

possible to improve the collision detection in a linear moving object. By adding other

types of sensors, the CDS could be improved to find collisions when the table is moving

down.

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8 Appendixes

Appendix 1 Example of collected data in CSV format

collision detection algorithm in a linear motionclassification problem, the random forest performed...

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