integration of robotic platforms in a communicating ...disi.unitn.it/~iova/files/robots.pdf · 2.2...

Integration of Robotic Platforms in a

Communicating Environment with

Application in the Aid of Elderly

Oana-Teodora IOVA

supervised by

Jean-Pierre MERLET

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Table of Contents

1 Introduction .................................................................................................................................... 3

1.1 A Short History of Robots ........................................................................................................ 3

1.2 The COPRIN Team ................................................................................................................... 4

2 Constructing and Programming the Robots ................................................................................... 6

2.1 Lynxmotion Aluminium 4WD1 Rover ...................................................................................... 6

2.2 Lynxmotion AL5A Robotic Arm ............................................................................................... 6

2.3 PobBot Golden Pack ................................................................................................................ 7

2.4 SoccerBot ................................................................................................................................ 7

3 Integration of the Robots in a Communicating Environment ......................................................... 8

3.1 Hardware ................................................................................................................................ 8

3.2 Geometrical Model ................................................................................................................. 9

3.3 Software ................................................................................................................................ 10

3.3.1 Robot Controllers .......................................................................................................... 10

3.3.2 Algorithm ...................................................................................................................... 10

4 Conclusions ................................................................................................................................... 15

References: ........................................................................................................................................... 16

Annex 1: ................................................................................................................................................ 17

Annex 2: ................................................................................................................................................ 18

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1 Introduction

1.1 A Short History of Robots

Since the beginnings of civilization man wanted to make things that would assist him. After

discovering mechanics and the means of creating complex mechanisms that would perform

repetitive functions, they created objects such as waterwheels and pumps. Technological advances

were slow, but there were more complex machines, generally limited to a very small number, which

performed more grandiose functions, such as those invented by Hero of Alexandria (steam-power

device, wind wheel).

In 1495 Leonardo da Vinci designed a mechanical device that looks like an armoured knight. The

mechanisms inside made the knight to sit up, wave its arms and move its head via a flexible neck

while opening and closing its jaw.

The word robot comes from the Czech word robota, meaning drudgery or slave-like labour. It was

first used to describe fabricated workers in a fictional 1920s play by Czech author Karel Capek called

Rossum’s Universal Robots. In the story, a scientist invents robots to help people by performing

simple, repetitive tasks. However, once the robots are used to fight wars, they turn on their human

owners and take over the world.

In 1941 the science fiction writer Isaac Asimov first used the word robotics to describe the

technology of robots and predicted the rise of a powerful robot industry. Next year, Asimov wrote

Runaround, a story about robots which contained the Three Laws of Robotics:

1. A robot may not injure a human, or, through inaction, allow a human being to come to

harm.

2. A robot must obey the orders given it by human beings except where such orders would

conflict with the First Law.

3. A robot must protect its own existence as long as such protection does not conflict with the

First or Second Law.

But real robots don’t become possible until the 1950’s and 60’s, with the invention of transistors and

integrated circuits. Compact, reliable electronics and a growing computer industry added brains to

the brawn of already existing machines. In 1961 the first industrial robot, Unimate (universal

automation) (Fig. 1a), was installed in the General Motors automobile factory in New Jersey. In 1963

the first artificial robotic arm to be controlled by a computer was designed. The Rancho Arm (Fig. 1b)

was intended as a tool for the handicapped and its six joints gave it the flexibility of a human arm.

Nowadays, a robot is a machine able to extract information from its environment and use

knowledge about its world to move safely in a meaningful and purposive manner. Currently, there

are many types of robots, based on their use:

- industrial robots: they usually consist of a jointed arm (multi-linked manipulator) and an end

effector (frequently a gripper) that is attached to a fixed surface. Typical applications include

welding, assembling, pick and place, packaging and palletizing, product inspection, and

testing, all accomplished with high endurance, speed, and precision.

- military robots are autonomous robots or remote-controlled devices designed for military

applications, such as: taking surveillance photographs, launching missiles at ground without

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Fig. 1a Unimate Robot Fig. 1b Rancho Arm Robot

a pilot, patrolling around a military base, or even use small arms weapons by remote control

(Fig. 2a).

- medical robots that can be used in surgery (Fig. 2b), lifting and moving patients, assisting

patients in recovery etc.

- Automated Guided Vehicles (AGVs): these are used for transporting material inside large or

oversized buildings like hospitals, container ports, and warehouses, using wires or markers

placed in the floor, or lasers, or vision, to sense the environment they operate in. An advanced

form of the AGV is the SGV, or the Self Guided Vehicle, which can be taught to autonomously

navigate within a space.

- service robots: used in house cleaning, care for the elderly, or cleaning hazardous waste. In this

category, we can include also the humanoid robots, such as ASIMO (Fig. 2c), originally developed

to assist people. It can walk, climb stairs, run, but is currently not capable of operating

autonomously in any real work environment.

Fig. 2a The SWORDS Robot Fig. 2b A laparoscopic Fig. 2c ASIMO

robotic surgery machine

1.2 The COPRIN Team

The COPRIN Team (Constraints solving, OPtimisation, Robust INterval analysis) has the centre at

INRIA Sophia Antipolis – Méditerranée. The head of the team is M. Jean-Pierre Merlet, who was also

my supervisor. Sophia Antipolis is a technology park northwest of Antibes and southwest of Nice,

France, created in 1970-1984. Several institutions and companies in the fields of computer-science,

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electronics, biotechnology, mathematics are located here, along with the European headquarters of

the W3C.

The research topic of the COPRIN team is solving the system of constraints using both consistency

methods and interval analysis. Furthermore, symbolic computation will systematically be used to

specialize the solving algorithms according to the structure of the problem in view of a better

efficiency.

The second major research axis of the project is robotics, especially the design of new structures

that must satisfy stringent performance requirements, while taking into account uncertainties that

are unavoidable for robotized systems. The mathematical tools that are developed as first research

axis of the project are especially useful for this kind of problems.

There are two years since the team started a strategic move towards assistance robots. The long

term goal is to provide robotized devices for assistance, including smart objects that may help

disabled, elderly and handicapped people in their personal life. These devices will be adapted to the

end-user and to its everyday environment, so they should be affordable and able to be controlled

through a large variety of simple interfaces.

One of the projects that the COPRIN team is involved right now is the Large Scale Initiative Action -

Personally Assisted Living (LSIA Pal) project. The objective of this project is to create a research

infrastructure that will enable experiments with technologies for improving the quality of life for

persons who have suffered a loss of autonomy through age, illness or accident. In particular, the

project seeks to enable development of technologies that can provide services for elderly and fragile

persons, as well as their immediate family, caregivers and social groups.

One of the crucial problems addressed in this project is the prevention and detection of falls and the

activity monitoring. Existing telehomecare systems cause many false alarms and therefore became

unusable in a real world [16]. As a result, a great amount of experimental analysis and validation are

needed to ensure a robust data and video analysis to detect risky situations and reduce false alarms.

Other projects [5, 10] that addressed the problem of detecting the falls used an omni-directional

camera (map cam), which is easier to use than having to manipulate multiple traditional cameras.

Still this is not accurate enough because when turning the lights on and off results in leaving over

static abandoned objects that let the impression of multiple targets in the environment. The MAIA

team [9] is working on a new device based on intelligent tiles, which can detect a person falling on

the ground. This is a non-intrusive approach that uses sensors placed on the floor. The Ivy Project [2]

takes another approach in detecting falls by creating a sensor network in the environment, which

detects when a person has sustained a fall. Besides the sensors used in the surroundings, there is

only one more sensor used: an accelerometer place on the person’s waist.

The COPRIN team chose to solve this task in two ways. First, the elder person will be using a

motorized walking aid, which provides help with mobility, but also assistance in case of fall. Second,

a small mobile robot, equipped with a camera and a first aid kit, moves towards the place where the

person has fallen and takes appropriate measures according to the information received.

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2 Constructing and Programming the Robots The first part of the internship consisted in constructing and programming a set of robots which will

be later integrated in an environment dedicated to assisting the elderly. All these robots came in a

kit, more or less mounted, and needed to be assembled and programmed in C, under Linux, in order

to be compatible with the other robots in the project.

2.1 Lynxmotion Aluminium 4WD1 Rover

The Lynxmotion Aluminium 4WD1 Robot Kit [7] is a robust, modifiable, and expandable chassis for

autonomous robot experimentation. It has excellent traction due to its RC truck tires and wheels.

The chassis is made from heavy-duty anodized aluminium structural brackets and laser-cut Lexan

panels.

In order to mount the robot we had to solder the wires and the capacitors to the motors, place the

motors into the chassis side brackets, attach the bottom Lexan panel, put the motor shafts and the

tires. For controlling the motors we used two SyRen 25A regenerative motor drivers [17], one for the

left-side motors and the other one for the right-side motors, obtaining a vehicle with differential

drive steering, just like a tank. The onboard switches allowed us to set one of the four operating

modes: analog input, RC input, simplified serial or packetsized serial. In order to make the SyRen

easy to interface to a microcontroller, we chose the RC input, which takes one or two standard RC

channels and uses those to set the speed and direction of the motor.

The last steps in mounting the robot were to attach the battery, the power switch and the top Lexan

panel. The result can be seen in Fig. 3.

Fig. 3 The Lynxmotion 4WD1 Rover

After constructing the robot, it followed the programming part. Using a SSC-32 servo controller from

Lynxmotion and a RS-232 serial port, we connected the robot to the computer and we created

functions in order to control it.

2.2 Lynxmotion AL5A Robotic Arm

The AL5A [8] from Lynxmotion is a robotic arm that has four degrees of freedom. It delivers fast,

accurate and repeatable movement and has base rotation, single plane shoulder, elbow, wrist

motion and a functional gripper.

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The construction of the arm was the most complicated from all the robots, because it has a lot of

components: black anodized aluminium brackets, aluminium tubing and hubs, custom injection

moulded components, precision laser-cut Lexan components and five different servos.

After finishing mounting all the components (Fig. 4a), we connected the arm to the SSC-32 servo

controller, and using a RS-232 serial port for the PC connection we were able to control it.

Fig. 4a The Lynxmotion AL5A Arm Fig. 4b The PobBot Golden Pack Fig. 4c The SoccerBot

2.3 PobBot Golden Pack

The PobBot Golden Pack is a mobile robot from Pob Technologies [14] equipped with a 2 axis

motorized gripper that can take up and move objects. It also has a mechanical base, an intelligent

colour camera, an LCD graphical screen, an I/O module and a distance sensor (Fig. 4b). The camera

(PobEye) is the eyes, the heart and the head of the robot and controls all the other components.

The connection to the PC is done using a serial port located on the PobEye. Being the centre of the

application, all the commands for the servos, the mechanical base or any other component of the

robot, must be sent to the PobEye.

The robot can be programmed in C/C++, Java or Basic, but only using the provided software –

PobTools – because at the end, the program will be transformed in a .hex file and uploaded on the

camera. Even if in the documentation it is said that the software is compatible with Windows,

MacOS and Linux, we didn’t manage to make it work under any of the Linux distributions available in

the lab. After many discussions and emails exchanged between us and the support team from Pob

Technology, they finally delivered us a working software and we were able to program the robot.

2.4 SoccerBot

The SoccerBot kit from QFix Robotics [15] is a good platform for robots supposed to move to an

arbitrary direction. The mounting of the kit was really easy, having just to put together the three

omni wheels, the motor bearings, the gear motors and the stable aluminium base plate (Fig. 4c). The

SoccerBot is a controller board that has 8 analog inputs, 8 digital inputs, 8 power outputs and 6

motor outputs and can be used with any DC source from 7V to 12V.

The connection to the PC is done using an USB cable. The kit also contains a CD with a C++ class

called SoccerBoard that was very useful in creating the functions that we needed in order to control

the robot.

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3 Integration of the Robots in a Communicating Environment The second part of the internship consisted in integrating the constructed robots in an environment

dedicated to assisting the elderly. As said before, the COPRIN team has an ongoing project for

improving the life quality of the persons who have suffered a loss of autonomy through age, illness

or accident. So, the task of the robot was to follow a wall and take the first aid to the person who

has fallen.

3.1 Hardware

In order to solve this task, we decided to build a mobile robot using the Lynxmotion aluminium

4WD1 rover, the AL5A robotic arm, the PhidgetAdvancedServo [12], the PhidegetSBC Interface Kit

[13], two infrared sensors and one ultrasonic sensor. For providing enough energy to the motors and

to the onboard electronics, we used two 12V battery packs. The resulted robot can be seen in the

figure below.

Fig. 5 The Robot

The PhidgetAdvancedServo 8-Motor (Fig. 6a) allows us to control the position, velocity, and

acceleration of up to 8 RC servo motors. In our case, we used it to control the two SyRen motor

drivers and the five servos from the arm. Instead of sending the desired position immediately, the

PhidgetAdvancedServo sends a series of progressive positions according to acceleration and velocity

parameters, which dramatically smoothes the operation of the servo, and allows reasonably precise

control of position, velocity and acceleration.

Fig. 6a PhidgetSBC Interface Kit Fig. 6b PhidgetAdvanceServo

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For making the robot autonomous, we used a PhidegetSBC (Fig. 6b), which is a fully functional single

board computer running Linux with Java and C libraries. This allows the PhidgetSBC to operate

autonomously, without the need for a graphical interface or a remote connection at all times.

We used two IR sensors, mounted on the right side of the robot, for measuring the distance from

the wall, because they are simple, commonly employed, and low-cost sensing modalities to perform

the wall-following task. They were preferable to ultrasonic sensors due to their faster response time

and narrower beam width. The used IR sensors were Sharp GP2D12 sensors, with IR Distance

Adapter Boards, which prevents the possibility of overcurrent. They measure distances from 10 cm

to 80 cm and produce values from 80 to approximately 500. The formula to translate sensor’s values

into distance is:

Distance (cm) = 4800 / (SensorValue - 20) (1)

Still, we used an ultrasonic sensor, placed in front of the robot, for detecting if there are obstacles

and try to avoid them. This is also a LV-MaxSonar-EZ0 sensor and it’s capable of detecting objects

situated at 6.45 meters distance from it.

We can observe the command structure of the robot in the figure below:

Fig. 7 The command system of the robot

The signals given by the ultrasonic sensor and the two IR sensors (FS and BS) are transmitted to the

PhidgetSBC Interface Kit and after computing the calculations the results are used for speed control

of the motors LM and RM.

3.2 Geometrical Model

The robot can be seen as a nonholonomic system, being implicitly dependent on parameters. In

mobile robotics, a car-like robot has three degrees of freedom: surging (moving forward and

backwards), swaying (moving left and right) and yawing (turning left and right). But, to describe its

pose, at any point, the robot can move only by a forward/backwards motion and a steering angle.

So, because it has three degrees of freedom, but they depend on constraints, the robot is a

nonholonomic system.

The constraint that allows us to control three degrees of freedom but using only two commands is:

�� sin θ - �� cos θ = 0 (2)

Where �� is the rate of change in horizontal position, �� the rate of change in vertical position, and θ

the angular position of the robot with respect to the horizontal distance.

IR sensors Motors

LM

Inputs Output

RM

FS

BS

US

PhidgetSBC PhidgetAdvancedServo

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3.3 Software

In order to follow a wall, the robot may be equipped with a camera and image processing can be

used, like in [1, 3]; the downsides of this method is that cameras are expensive and they fail to work

when there is insufficient light. In [6] the authors have constructed a two-link sensorized antenna;

the robot receives feedback about the environment from it and sends proper commands to the

motors. This is an interesting idea, bio-inspired from cockroaches, but during experiments, the

results for convex walls weren’t too satisfactory. A Fuzzy Logic Controller is used in [11] to drive a

robot, equipped with ultrasonic sensors (two on a side and one in the front), parallel to the wall. In

[4] the authors used two IR sensors, cross mounted in front of the robot, but during tests, errors

were registered because some of the signals emitted from one sensor were received by the other

one.

3.3.1 Robot Controllers

The control strategies for mobile robots can be divided into open loop and closed loop strategies. In

open loop control, the inputs such as distance or speed, are calculated beforehand, from the

knowledge of the start and end positions. This technique cannot handle disturbances (e.g. different

traction from the wheels) or model errors, nor corrects one of the parameters that could go wrong.

One the other hand, the closed loop strategies can compensate for the errors occurred in real time,

since the inputs are based on the actual conditions, and not on the predicted ones. Because of this,

the disturbances causing deviations from the initial state can be compensated by the input data.

One widely used closed loop controller, is the PID (proportional – integral – derivative) controller.

This calculates the difference between a measured variable and a predefined point as the error of

the process, and then tries to minimize it by adjusting the inputs. The proportional value determines

the reaction to the current error, the integral, calculates the reaction based on the sum of the

previous errors and the derivative determines the reaction based on the change rate of the error.

3.3.2 Algorithm

The first version of the algorithm doesn’t take into consideration the PID controller and reacts just at

the current error (it could also be seen as just a Proportional controller). As we can observe in Annex

1, the algorithm calculates the distance from the wall and the angle between the robot and the wall.

The angle, θ, can be calculated thanks to the two IR sensors, by using the following formulas:

θ (radians) = arctan ((front_value – back_value) / 14) (3)

θ (degrees) = 360 * arctan ((front_value – back_value) / 14) / (2 * π) (4)

Where front_value and back_value are the values returned by the two IR sensors, and 14 is the

distance between them, in centimetres.

In this algorithm we calculate the mean of every three consecutive values returned by the sensors,

and then the error, which is the difference between this mean and a predefined setpoint (20 cm in

our case). For taking into account the error of the values returned by the sensors, we have also set a

margin of error (marge) of 2 cm. After all these variables being set, we can distinguish three cases:

- The error is greater than the

has to move away from the wall, so it goes left; if the error is

too far, so it has to go right. If the angle is between these two values, then it can go forw

- The error is smaller than –marge

angle is greater than 15˚, it will move to the right and if it’s smaller than 5˚ it will move to

the left, otherwise it will go forward.

- The error is between –marge

still, if the angle is greater than 5

correct this, the robot will go a little faster to the right. Also, if the angle is smaller

the robot will go to the left.

We have tested the algorithm in two cases:

- The robot is following a straight wall of 5.70 meters, at a distance of 20 cm

- The robot is following a straight wall of 11.50 meters

20 cm.

As we can observe in Fig. 8, the trajectory of the robot tends to be sinusoidal. This happens because

each time it is too far from the wall and tries to go closer, it arrives too close, and then it moves

away, but it goes too far, and so on… This behaviour is more evident in the sec

(Fig.9), when the wall is longer, and has three doors. Even if the doors are closed, they are disturbing

the robot’s trajectory, being sensed by the sensor as farther away

Fig. 8 Distance between the robot and the wall

The error is greater than the marge: if the angle is smaller than -15˚, it means that the robot

has to move away from the wall, so it goes left; if the error is greater than -5

too far, so it has to go right. If the angle is between these two values, then it can go forw

marge, which means that the robot is too close to the wall: if the

˚, it will move to the right and if it’s smaller than 5˚ it will move to

the left, otherwise it will go forward.

marge and marge: in these case, the robot should go forward, but

still, if the angle is greater than 5˚, it means that it will tend to go to left, so in order to

correct this, the robot will go a little faster to the right. Also, if the angle is smaller

the robot will go to the left.

We have tested the algorithm in two cases:

a straight wall of 5.70 meters, at a distance of 20 cm.

a straight wall of 11.50 meters with three doors, also at a distanc

, the trajectory of the robot tends to be sinusoidal. This happens because


away, but it goes too far, and so on… This behaviour is more evident in the second experiment

the wall is longer, and has three doors. Even if the doors are closed, they are disturbing

the robot’s trajectory, being sensed by the sensor as farther away.

Distance between the robot and the wall

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˚, it means that the robot

5˚, the robot is

too far, so it has to go right. If the angle is between these two values, then it can go forward.

the robot is too close to the wall: if the

˚, it will move to the right and if it’s smaller than 5˚ it will move to

: in these case, the robot should go forward, but

˚, it means that it will tend to go to left, so in order to

correct this, the robot will go a little faster to the right. Also, if the angle is smaller than -5˚,

with three doors, also at a distance of

, the trajectory of the robot tends to be sinusoidal. This happens because


ond experiment

the wall is longer, and has three doors. Even if the doors are closed, they are disturbing

Fig. 9 Distance b

Even if the chassis is designed carefully to be balanced, after loading it with the battery packs and all

the electronics, the chassis has become unbalanced. This caused a veering of the robot: it tends to

go farther away from the wall, and has troubles returning closer. In order to compensate this, we

put a greater speed in the left wheels when the robot is moving forward, but still this wasn’t enough.

In order to correct all these errors, we decided to implem

two inputs: the error (the difference between the setpoint value and the actual position of the

robot), and the angle θ between the robot and the wall. The output is a changed in the speed of the

wheels. For correcting the position of the robot the output is calculated with the formula:

output = Kp � error + Ko

Then, the speed in the left wheels is modified in concordance with the

right wheels, with -output. When the robot is moving forward, it keeps a constant speed of 30% of

the maximum speed.

The gains of a PID controller (in our case Kp, Ko and Kd) are very important for this kind of a system.

If these are chosen incorrectly, the system becomes unstable, or the steady error is too large. In

order to find the suitable gains, we did step by step

output.

Distance between the robot and the wall with three doors



farther away from the wall, and has troubles returning closer. In order to compensate this, we


these errors, we decided to implement a PID controller. The PID controller gets



rrecting the position of the robot the output is calculated with the formula:

error + Ko � θ + Kd � ��_��

∆�

Then, the speed in the left wheels is modified in concordance with the output, and the speed in the

. When the robot is moving forward, it keeps a constant speed of 30% of



order to find the suitable gains, we did step by step changes in the input, while measuring the

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farther away from the wall, and has troubles returning closer. In order to compensate this, we


The PID controller gets



rrecting the position of the robot the output is calculated with the formula:

(4)

, and the speed in the

. When the robot is moving forward, it keeps a constant speed of 30% of



changes in the input, while measuring the

The implemented PID controller could work very well if the received information from the sensors

would be trustworthy. We found that the IR sensors return strange values if the robot is too closed

to the wall or too far away, so additional tuning to the algorithm had to be done. We continue to

calculate the mean of consecutive values returned by the sensors, but this time every five values.

Moreover, we manually set the speed of the wheels if the robot is

(more than 50 cm) from the wall.

Another tuning that we had to do was that if the resulted speed was too small, the motors wouldn’t

have enough power to rotate the wheels, or in the contrary, if the resulted speed was to

motors would run the wheels too fast. In these cases we also decided to set the speed manually.

The full algorithm, which includes the PID controller and all the other adjustments, can be found in

Annex 2.

After implementing the PID controller

observe in Fig. 10 that the task of following the wall is performed much better, the trajectory of the

robot being almost a straight line. Also, in the case of the longer wall with three doors we

observe a big improvement (Fig. 11).

anymore, but also after passing the doors, we can see how it becomes more stabilized.

Fig. 10 Distance between the robot and the wall



wall or too far away, so additional tuning to the algorithm had to be done. We continue to


Moreover, we manually set the speed of the wheels if the robot is to close (less than 9 cm) or to far


have enough power to rotate the wheels, or in the contrary, if the resulted speed was to



After implementing the PID controller and tuning the algorithm we repeated the same tests. We can

the task of following the wall is performed much better, the trajectory of the

robot being almost a straight line. Also, in the case of the longer wall with three doors we

improvement (Fig. 11). Not only doesn’t the trajectory have a sinusoidal form



13



wall or too far away, so additional tuning to the algorithm had to be done. We continue to


to close (less than 9 cm) or to far


have enough power to rotate the wheels, or in the contrary, if the resulted speed was too big, the



and tuning the algorithm we repeated the same tests. We can

the task of following the wall is performed much better, the trajectory of the

robot being almost a straight line. Also, in the case of the longer wall with three doors we can

Not only doesn’t the trajectory have a sinusoidal form


Fig. 11 Distance betw

After succeeding in making the robot following the wall, we concentrated on the obstacle avoidance

and cornering, making use of the ultrasonic sensor. So, if the robot is following the wall and the

ultrasonic detects an object in front of it, we have the following cases:

- The distance to the object in front of it it’s between 50 and 70 cm: in this case, the robot

slows down, in order for it to take proper measures

- The distance is smaller than 50 cm, but greater than 25 cm, which me

just in front of it (an obstacle or another wall): the robot turns left until there is nothing in

front of it and then starts going forward. This manoeuvre has proved to be very useful in

corners.

- The distance is smaller than 25 cm,

there is something in front of it, so the robot starts going backwards until it reaches one of

the above distances.

- Because the robot is following a wall, the

also verify the angle θ. If the distance is between 40 and 70 cm and |θ|

distance is greater than 70 cm and |θ|

(the object that the ultrasonic




front of it, we have the following cases:

The distance to the object in front of it it’s between 50 and 70 cm: in this case, the robot

slows down, in order for it to take proper measures

The distance is smaller than 50 cm, but greater than 25 cm, which means there is an object



The distance is smaller than 25 cm, which means the ultrasonic didn’t detect earlier that


llowing a wall, the ultrasonic can return wrong values. That’s

also verify the angle θ. If the distance is between 40 and 70 cm and |θ|

distance is greater than 70 cm and |θ| � 45˚, it means that there is no obstacle in front of it

ultrasonic has detected is, in fact, the wall the robot is fallowing).

14



The distance to the object in front of it it’s between 50 and 70 cm: in this case, the robot

ans there is an object



didn’t detect earlier that


can return wrong values. That’s why we

also verify the angle θ. If the distance is between 40 and 70 cm and |θ| � 45˚, or the

˚, it means that there is no obstacle in front of it

the robot is fallowing).

15

4 Conclusions During this internship, we constructed a differential drive mobile robot that uses two IR sensors and

one ultrasonic for moving in the environment and the arm for taking the first aid to the person that

has fallen. The algorithm implemented uses the feedback from the sensors and transmit it to the PID

controller, which make use of it to maintain a constant distance from the wall.

As we can see from the experiments, the robot successfully follows a wall, even if it’s not a straight

one. Also, the use of a PID controller has proven to be very useful, the results being better that in

the case of a simple controller.

However, the results could be improved if the values returned by the sensor were more accurate,

especially when the distance between the robot and the wall is less than 10 cm. This also depends

on the surface reflectance properties, which could differ from one wall to another, or even in the

same wall, if it has some doors, for example.

Further research could be done with the use of better sensors and by adding some more features,

like knowing the map of the room and going in a given place. This could be very useful in the case of

taking the first aid to the person who has fallen.

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References: [1] R. Carelli, C. Soria, O. Nasisi, E. Freire, Stable AGV corridor navigation with fused vision-based

control signals, Proceedings of the IEEE Industrial Electronics Society, IECON, Sevilla, Spain,

November 2002, p. 2433 – 2438

[2] J. Chen, K. Kwong, D. Chang, J. Luk, R. Bajcsy, Wearable Sensors for Reliable Fall Detection,

Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th

Annual Conference, China,

September 2005, p. 3551 – 3554

[3] A. Dev, B. Krose, F. Groen, Navigation of a mobile robot on the temporal development of the optic

flow, Proceedings of the IEEE/RSJ/GI Int. Conf. on Intelligent Robots and Systems IROS’97, Grenoble,

September 1997, p. 558-563

[4] I. Gavrilut, V. Tiponut, A. Gacsadi, L. Tepelea, Wall-Following Method for an Autonomous Mobile

Robot using Two IR Sensors, Proceedings of the 12th WSEAS International Conference on Systems,

Greece, 2008, p.205 – 209

[5] Y.C. Huang, S.G. Miaou, T.Y. Liao, A Human Fall Detection System Using an Omni-Directional

Camera in Practical Environments for Health Care Applications, IAPR Conference on Machine Vision

Applications, Japan, May 2009, p. 455 – 458

[6] A.G. Lamperski, O.Y. Loh, B.L. Kutscher, N.J. Cowan, Dynamical wall-following for a wheeled robot

using a passive tactile sensor, , Proceedings of the IEEE Int. Conf. of Robotics and Automation, April

2005, p. 3838 - 3843

[7] Lynx 4WD1: http://www.lynxmotion.com/c-119-auton-combo-kit.aspx

[8] Lynx AL5A: http://www.lynxmotion.com/c-124-al5a.aspx

[9] MAIA: http://maia.loria.fr

[10] S.G. Miaou, P.H. Sung, C.Y. Huang, A Customized Human Fall Detection System Using Omni-

Camera Images and Personal Information, Proceedings of the 1st Distributed Diagnosis and Home

HealthCare (D2H2) Conference, USA, April 2006, p. 39 – 42

[11] V.M. Peri, A. Simon, Fuzzy Logic Control for an Autonomous Robot, Fuzzy Information Processing

Society, NAFIPS, June 2005, p. 337 – 342

[12] PhidgetAdvancedServo: http://www.phidgets.com/products.php?product_id=1061

[13] PhidgetSBC: http://www.phidgets.com/products.php?product_id=1070

[14] Pob Technology: http://www.pob-technology.com

[15] QFix: http://www.qfix-robotics.de

[16] K. Roback, A. Herzog, Home Informatics in Healthcare: Assessment Guidelines to Keep Up

Quality of Care and Avoid Adverse Effects, Technology and Health Care, March 2003, p. 195 – 206

[17] SyRen 25A: http://www.dimensionengineering.com/SyRen25.htm

17

Annex 1: Algorithm without PID

BEGIN

SET error to real_distance - setpoint

SET marge to 2

SET θ to 360 * arctan ((front_value – back_value) / 14) / (2 * π)

IF error > marge THEN

IF error > 100 THEN

there is nothing in the right side

ELSE IF θ > -5˚ THEN

go right with 50% speed

ELSE IF θ < -15˚ THEN

go left with 30% speed

ELSE

go forward with 30% speed

END IF

ELSE IF error <- marge THEN

IF θ > 15˚ THEN


ELSE IF θ < 5˚ THEN


ELSE


END IF

ELSE IF |error| <= marge THEN

IF θ > 5˚ THEN


IF θ < -5˚ THEN


ELSE


END IF

END IF

END

18

Annex 2: Algorithm with PID

BEGIN

SET error to real distance – setpoint

SET θ to 360 * arctan ((front value – back value) / 14) / (2 * π)

SET output to Kp × error + Ko × θ + Kd × ��

∆�

SET Mean to front value - back value

IF Mean <= 9 THEN

SET Speed to 60%

ELSE IF Mean > 9 AND mean < 50 THEN

SET Speed to output%

ELSE IF Mean >= 50 THEN

SET Speed to 20%

END IF

IF Speed < 20% THEN

SET Speed to 20%

ELSE IF Speed > 60% THEN

SET Speed to 60%

END IF

IF Mean >= 80 THEN

IF US value < 25 cm THEN

go backwards with 20% speed

ELSE IF US value <= 40 AND US value >= 25 THEN

turn left with 20% speed

END IF

IF US value > 40


END IF

ELSE IF Mean > 6 AND mean < 80

IF US value < 25 cm THEN

go backwards with 20% speed

ELSE IF US value <= 50 AND US value >= 25 THEN


ELSE IF US value > 50 AND US value < 70 AND θ <= 45˚ THEN

there is an obstacle close, go slower

ELSE IF US value > 70 AND |θ| <= 45˚ THEN

there is no obstacle, move with Speed% of the maximal speed

ELSE IF US value > 40 AND |θ| > 45˚ THEN

IF |θ| > 0 THEN

turn right with 20% speed

ELSE


END IF

END IF

END IF

END

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