TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke

Assignment 2: RTLS

Total Plant Automation

1 Introduction

In order to automatically detect and track targeted people or objects within a building or some

defined area in real time, a real-time location system (RTLS) can be utilised. Fixed reference

points receive wireless signals from active RTLS tags attached to people or objects to determine

their position. An RTLS differentiates itself from a GSP in terms of local positioning due to its

better accuracy and has therefore other applications such as asset management and patient track-

ing, predominately in manufactories, warehouses and healthcare.

First of all, as described in Chapter 2, an RTLS was configured for a defined area in the lab to

track three objects - an operator, a tool, and a car. Next, spatial relationships were analysed. To

dispose the interference of the various metal objects in the defined area, different filters were

designated to the tags. Those filters were then examined by tracking a fixed trajectory. These

analyses are discussed in Chapter 3 whereas conclusions are articulated in Chapter 4.

2 Setup

A Ubisense RTLS was used comprising four antennas and three active tags, seen in Figure 1.

Moreover, three software applications of Ubisense are utilised to configure the system: (i) Site

Manager; (ii) Map View; (iii) and Location Engine Configuration. The first section will describe

the Site Manager, section two will further handle the Map View. Section three will go over the

Location Engine Configuration.

(a) antenna

(b) active tag

Figure 1 - Ubisense RTLS system components

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 2

2.1 Site Manager

Various characteristics, such as types, objects, representations, areas, cells, object locations, and

geometry, must be determined in the Site Manager. Types refer to categorical traits, objects repre-

sent the items - persons, machines, equipment, goods - accompanied with tags one wants to

track, and representations can be assigned to the objects for visualisation purposes, as seen on

Figure 2. The area in which one wants to track the items must be defined in the Areas tab - Fig-

ure 3. The locations of the antennas are set in the Cells tab - Figure 5. Figure 4 depicts the object

locations.

In order to examine whether objects are in close proximity or enter an alarm zone, spatial rela-

tionships can be assigned between multiple objects and between objects and areas in the Geome-

try tab - Figure 6. Here, roles are utilised which represent what part the object takes in the rela-

tionship, such as object or zone. Each role is assigned with an area, i.e. shape, referring to what

extent the object takes part in the relation - Figure 7. For example, a person has a small area

whereas an alarm zone can be large. Dependent on the type of role, the area is absolute or rela-

tive. Specifically, a persons area is relative whereas an alarm zone has an absolute area. There are

two methods to designate the relation to the roles. A role can either contain another role via a

contains relation, or can be contained by another role object via a contained by relation. For

example, in case a person is not allowed to enter a particular area (alarm zone), the alarm zone

has a contains relationship with the person whereas the person has a contained by relation with

the alarm zone. The objective of this experiment was to determine when an assembly operator

was adding value to the product. It was assumed that the operator only adds value when he

stands at a product (i.e. car) with a tool (i.e. screwing device). Therefore, in the configuration of

this experiment, the tool had a contained by relation with the operator while the operator had a

contains relation with the tool. On the other hand, the operator had a contained by relation

with the car while the car had a contains relation with the operator.

2.2 Map View

The visualisation is rendered in Map View. Both the objects and the shapes of the roles are de-

picted. The shapes of the roles colour green when the spatial relationship holds, see Figure 8,

indicating a valid spatial relationship. In case of an invalid spatial relationship, the shapes colour

red. Note that the shape of the containing object must be completely inside the shape of con-

tained by object for the spatial relationship to be true.

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 3

(a) Types tab

(b) Objects tab

(c) Representations tab [1]

Figure 2 - Types and Objects tab in Site Manager

Figure 3 - Areas tab in Site Manager

Figure 4 - Object locations

Figure 5 - Cells tab in Site Manager

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 4

Figure 6 - Roles configuration in the Geometry tab of Site Manager

Figure 7 - Shape configuration Figure 8 - Spatial Relationships view in Map View

2.3 Location Engine Configuration

Tags must be assigned to objects in the Location Engine Configuration. First tags are added. In

the software application, this is conducted via ownerships, i.e. tags own an object. This is illus-

trated in Figure 9. The three objects that were considered in this experiment were (i) a person

entitled Minion; (ii) a vehicle entitled Lamborghini; and (iii) a tool entitled screwing device.

Table 1 depicts the ownerships.

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 5

Figure 9 - Ownership tab in Location Engine Configuration

Table 1 - Ownerships of the tags

Tag Owner

020-000-118-230 screwing device

020-000-118-224 Lamborghini

020-000-118-228 Minion

The Sensor and Cells tab illustrates the positions of the tags in the area, see Figure 10. Here, the

trail can be tracked as well, see Figure 11.

Figure 10 - Real-time positioning of the tags in the Sensor and Cells tab

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 6

Figure 11 - Example tracked trail of a tag in real-time

In the area, a lot of metal objects were present and interfere with the localisation system. There-

fore, filters have been applied, see Figure 12, to examine the accuracy of the system by tracking

the trajectory of a straight line with and without the use of filters. For this experiment, the trajec-

tory was first tracked using no filter. Then, the four predefined filters as displayed in Figure 12

were applied. Thereafter, the parameters of the resulting preeminent filter were compared with

the parameters of the inferior filters. By altering a few parameters of the preeminent filter and

visually inspecting the tracked trail in the Sensor and Cells tab, a best practices filter was defined.

The tag entitled with Minion was put into a railway car to travel in a straight line and on a flat

surface, so as to control the variations in both the x- and z-direction. The trajectory was tracked

explicitly by the Location Engine Configuration application and implicitly by an additional soft-

ware tool CoordinatenLoggen.exe. The latter provides the coordinates of the trail which will be

used to establish a linear regression model per filter. Then, the coefficient of determination, R-

squared, will be utilised to define the accuracy of the filter as it represents the proportion of vari-

ability in the observed response variable that is explained by the linear regression model. Im-

portant to note, here, is that in the data sets of information filtering and best practices filter

several lines of data representing either impracticable or erroneous coordinates at the beginning

of the tests, were deleted but are still perceptible on the tracked trajectories.

3 Analysis

This chapter outlines and discusses the results. The first section presents the visualisation of the

spatial relationships between roles. Section 2 examines the different effects of the filters on a

tracked trajectory.

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 7

Figure 12 - Available filters in the Location Engine Configuration

3.1 Spatial relationships

Figure 13 and Figure 14 respectively depict the situation where the operator is inside and outside

the range of the car. The shades of both the operator and the car did not colour red, representing

an invalid spatial relationship. It appeared that the vertical height difference was not violated in

this activity, by which the system did not perceive the relation as invalid.

Figure 13 - Operation inside the range of car

Figure 14 - Operator outside the range of car

3.2 Accuracy and Filters

In this section, the trail and the linear regression model is presented per filter. Table 2 gives an

overview of the effects of the various filters in terms of the linear model and the corresponding

R-square coefficient. Following filters present a vertical near-linear path: (i) fixed height infor-

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 8

mation filtering; (ii) information filtering; (iii) static information; and (iv) a best practices1 combi-

nation of the filters.

Remarkably, here, is that the linear regression model computed for the situation where fixed

height information filtering was conducted, indicates a large variance whereas the observed tra-

jectory shows a primarily linear line. The plot of the tracked x- and y-coordinates and the small

R-squared coefficient fosters this large variance.. A reasonable cause for this bias is probably a

malfunction of the additional software tool. In contrast, the tracked trajectories where infor-

mation filtering and where the best practices filter were utilised, provide preeminent results.

Their linear models and their corresponding R-squared coefficient profoundly comply with their

tracked trajectories. Furthermore, in spite of the three outer data points, the trajectory of the case

where static information filtering was applied is fairly straight. Those outlying data points justify

the lower R-squared coefficient. Once more, a probable malfunction of the additional software

tool can be assigned as a reasonable cause. Despite the moderately smooth trajectory of the case

when no filter was used, the corresponding linear model has a slope and unexplainable variance

comparable to the four aforementioned near-linear models - apart from the R-squared coefficient

of the linear model associated with the fixed height information filter. Finally, in case of static

fixed height information filtering, the trajectory shows a rather curved line in the upper half of

the trajectory. The latter is also visible on the plot of the x- and y-coordinates, and resulted in a

slope almost twice as large as any other regression coefficient. The proportion of total variation

of outcomes explained by the model, however, relates to the R-squared coefficients of the other

near-linear models - also apart from the R-squared coefficient of the linear model associated with

the fixed height information filter.

Table 2 - Overview linear regression models of the trajectories using various filters

Filter Linear model R Reference

No filtering x = 0.0253y - 942.62 0.520 Figure 15

Fixed height information filtering x = 0.0205y - 954.52 0.146 Figure 16

Information filtering x = 0.0164y - 942.44 0.549 Figure 17

Static fixed height information filtering x = 0.0488y - 961.11 0.460 Figure 18

Static information filtering x = 0.0210y - 945.71 0.386 Figure 19

Best practices combination x = 0.0271y - 950.66 0.659 Figure 20

1 Of the preeminent fixed height information filtering, the vertical positioning std dev was set to zero, and the tag height above cell floor was set to 0.7.

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 9

(a) (b)

Figure 15 - Tracked trail (a) and linear model of tracked coordinates(b) using no filter

(a) (b)

Figure 16 - Tracked trail (a) and linear model of tracked coordinates(b) using fixed height information filtering

x = 0.0253y - 942.62R = 0.5196

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

x = 0.0205y - 954.52R = 0.1459

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 10

(a) (b)

Figure 17 - Tracked trail (a) and linear model of tracked coordinates(b) using information filtering

(a) (b)

Figure 18 - Tracked trail (a) and linear model of tracked coordinates(b) using static fixed height information filtering

x = 0.0164y - 942.44R = 0.5493

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

x = 0.0448y - 961.11R = 0.46

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 11

(a) (b)

Figure 19 - Tracked trail (a) and linear model of tracked coordinates(b) using static information filtering

(a) (b)

Figure 20 - Tracked trail (a) and linear model of tracked coordinates(b) using a combination of best practises filter

4 Conclusions

An RTLS was first configured for a defined area in the lab to get acquainted with the basic opera-

tions. Three objects - an operator, a tool, and a car - were tracked and their spatial relationships

were analysed. Due to insufficient vertical height difference, the shades of the objects did not

depict an invalid spatial relationship. Next, different filters were administered to the tags to dis-

x = 0.021y - 945.71R = 0.3855

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

x = 0.0271y - 950.66R = 0.659

300 500 700 900

-1,000

-975

-950

-925

-900

x co

ord

inat

e

y coordinate

TPA - Assignment 2 Korneel Melkebeke, Pieter-Jan Steenbeke 12

card the interference of the various metal objects in the defined area. Finally, the filters were ex-

amined by tracking a fixed trajectory by means of linear regression models and corresponding

coefficients of determination. It was discovered that using filters showed a predominately linear

path, despite some ambiguous results such as a high unexplainable variance in the response vari-

able or a high slope of the linear model. Still, when no filter was used, a fairly preeminent linear

regression model with a rather small unexplainable variance was attained. Therefore, it can be

stipulated that filters enhance the performance of the system when used properly.

References

[1] How to create a representation using Site Manager, Ubisense, [Online]. Available: https://download.ubisense.net/howto/SiteManagerRep_article/SiteManagerRep.html. [Accessed 22 March 2015].