It can move in any direction without changing its orientation.forward ,backward ,diagonally ,rotate in a place

That is actually a very good question, and the second part of the answer is simply “yes”.

The first part is a bit more complex. Probably the most lucid clarification I’ve ever seen comes from Professor Alan Winfield and appears in his book “A Very Short Introduction to Robotics”. He says:

A robot is:

an artificial device that can sense its environment and purposefully act on or in that environment;

an embodied artificial intelligence or;

a machine that can autonomously carry out useful work.

The third of these clearly states that a robot should have output parts and therefore would be a machine. The other two are more ambiguous. Purposefully acting on the environment would seem to indicate that the robot has some sort of output that performs a perceptible function. Now, in the living room, we have an air-freshener squirting thing. It counts to the time for a squirt, activates a solenoid, which causes the squirter on a canister to be depressed and it squirts a whiff from the canister and then the controller resets itself. Is this a robot? According to Professor Winfield’s first line it is not. The reason that it is not is that it does not sense the environment. It doesn’t know whether the room needs a squirt or not. It just knows it is time to squirt, so it does.

Some time ago I made an alarm system. It has eight zones, each zone has a PIR device to sense movement and an SnO2 detector for smoke. There are also two carbon monoxide detectors, some open window sensors done by a reed switch and magnet and two door sensors done the same way. The panel has a number keyboard and by an LCD screen, you can see in which zone an alarm is activated (there’s a quartet of big sirens) and enable or disable the intruder sensors. The whole thing is watched over by an Arduino Mega. Is this a robot?

It has no moving mechanical parts but it can sense its environment and produce a (very) audible and a visual output. So, the first criterion is passed.

Does it have an embodied artificial intelligence? Well, we can argue about this until the cows come home. It has a set of instructions that it follows that tell it if it sees a signal at all, it activates the sirens. Then it discriminates against which signal it has seen and where and reports that on the screen in human-readable text. It can follow instructions well enough to ignore any signals we’ve told it to. That would indicate a level of intelligence that is not high, but nevertheless present.

Does it autonomously carry out useful work? It certainly does. If there’s a burglar or smoke or carbon monoxide it lets me know.

It’s certainly a machine and by Professor Winfield’s definition it’s a robot, though it’s entirely solid state and has no moving mechanical parts; the latter being the case it’s not many people’s idea of a robot.

So we find the lines blurred. We can say that robots are either electronic or electro-mechanical machines provided that they can act autonomously. If they can’t act autonomously, they are not. But here again we are on sticky ground. Your car if it is modern can autonomously alter brakes, suspension and engine tuning. But you have full control over the speed, direction and deceleration of the vehicle. Is it a robot?

Professor Winfield, if you are wondering, is, or at least was when I read his book, Professor of Electronic Engineering at. the University of the West of England and Honorary visiting Professor at the University of York and conducts research into swarm robotics. Given that, I feel pretty confident that he knows what he is talking about.

This is my favorite simple walking machine. It is from U.S Patent No. 1,363,460 to J. A. Ekelund (1920). An electric motor and batteries can be put in the wagon to drive the legs. It will move nicely in a straight line.

There are several companies that have names that sound Indian but are not Indian companies. Here are some well-known examples:

1. Hindustan Unilever Limited (HUL)

• Sounds Indian, but it is a British-Dutch company.
• Owned by Unilever, headquartered in the UK and the Netherlands.

2. Bata

• Many people think it's an Indian brand, but it is actually a Swiss company.
• Founded in the Czech Republic in 1894 and later moved to Switzerland.

3. Nestlé India

• Sounds Indian, but it is a Swiss company.
• Owned by Nestlé, headquartered in Switzerland.

4. Maruti Suzuki

• "Maruti" is an Indian name, but it is owned by Suzuki, a Japanese company.

5. Larsen & Toubro (L&T)

• Sounds Indian, but founded by two Danish engineers, Henning Holck-Larsen and Søren Kristian Toubro.
• However, now it is an Indian multinational.

6. Colgate-Palmolive India

• Sounds local, but it is owned by Colgate-Palmolive, a U.S. company.

7. Renault India

• Renault sounds Indian, but it is a French automobile company.

8. Hyundai India

• Popular in India, but it is a South Korean company.

9. Sony India

• "Sony" is a global brand but is actually from Japan.

These companies have Indian operations and branding, which often makes them appear Indian.

A robot is a machine designed to execute one or more tasks automatically with speed and precision. ... Industrial robots, for example, are often designed to perform repetitive tasks that aren't facilitated by a human-like construction. A robot can be remotely controlled by a human operator, sometimes from a great distance.

The most common robots look something like this and perform a single function over and over with extremely high precision.

So what is in these robots?

1. skeleton or frame. It needs a mechanical support that connects and supports the electronics.
2. Propulsion. Motors/ servos/actuators. Each has different strengths depending on the robots purpose. Stepper motors for accuracy, service for cost effectiveness, actuators for strength.
3. Sensors (possibly) these could be as simple as a microswitch, like 3d printers use (technically fits the definition of a robot) which tells the machine when it reaches the limits of its reach. Or it could be more sophisticated like lidar which builds up a map of the surrounding environment.
4. H bridge or similar controller. This controls the motors speed and how far they travel.
5. Processor. Effectively a programmable circuit board (possibly a computer or microcomputer) which opens and closes switches that the h bridge translates into pulses that control the motors.

If there is anything I’ve missed let me know in the comments below.

I know a little about the industrial robot, sometimes called a 'robot arm'. A robot arm has 2 subsystems that work together: a mechanical system and a computer system.
Mechanically, an arm is a series of metal bars, called links, that are held together by joints that can rotate. The rotation is similar to your elbow joint in that the rotation is in 2 dimensions. You can't swivel your elbow any which way - you can only move your forearm in line with your upper arm. An elbow joint can rotate almost 180 degrees, but robot joints can usually rotate much farther, like 350 degrees, and robots arms often have 4 or more joints. As a result, robot arms are much more flexible than human arms. Also, the first link of the robot arm must be fastened to some solid surface by another rotating joint, and the last link is usually attached to a device that can 'effect' some useful action. This might be a pair of 'fingers' that can grip an object to move it, or maybe a a screwdriver for fastening parts together. The generic term is 'end effector'.

Each joint has a motor that powers the rotation, clockwise and counter/anti-clockwise. Each joint also has a measuring device called an encoder. This sends an electronic signal to a computer that indicates the current joint rotation angle. These are all the mechanical parts to a robot arm.

The role of the computer system is to figure out how much power to send to each joint motor to cause it to rotate so that the end effector is in the right position to do its work. This is tricky because a position in space is most easily described as coordinates such as "10 units up, 25.23 units sideways to the right, and 33 units away" from some convenient point (for example, where the robot arm is attached to its surface). But how do you translate those 3 coordinates into 4 or more joint rotations? There are sophisticated mathematical techniques called 'transforms' that convert units in Cartesian space (up, right, away) to 'rotation' space. The computer performs its transforms many times per second, all the while reading the encoder angles, until the motors put the end effector into position.

Please do this. Extend you arm (either one) away from yourself. Close your eyes. Move your arm so that you can touch the top of your head. Very good. Now, figure out how you knew how much to rotate each of your arm joints to compete this most simple human task.

Did your brain perform a series mathematical transforms?

I don't know either.

In simple words, all robotic systems are mechatronic systems. But, all mechatronic systems aren't robotic systems.

Mathematically, robotics is the subset of mechatronics set.

Robotics is a specific branch of mechatronics that requires the knowledge of,

• Dynamic system modeling and analysis
• Control engineering
• Signal conditioning
• Sensors and actuators
• Hardware interface
• Path programming
• Kinematics and so on...

As you can see, even a mechatronic system requires the above mentioned elements. However, it differs from a robotic system in terms of functionality and the use case. For example, coffee wending machine, airplane, washing machine, automobile etc. are all mechatronic systems, which belong to different use cases. However, robots are unique in terms of their functionality and use case.

Once you have returned to the dark ages where you’ve come from, ponder this: what if my work is done by something artificial?

But only after you have returned.

Even India isn’t where it was once.

Society has left you behind.

Make sure enough nutrients reach your brain through normal blood flow.

A robot is a sort of automated machine that can do specified jobs quickly and precisely with little or no human assistance. Robotics, which deals with the design, engineering, and operation of robots, has come a long way in the last 50 years.

The following are some of the things that robots excel at over humans:

1. Incorporate automation into manual or repetitive tasks incorporate or industrial environments.

2. To detect risks such as gas leaks, work in unpredictable or dangerous conditions.

3. Process and deliver enterprise security reports.

4. Prepare IVs and fill out medication prescriptions.

5. During an emergency, provide internet orders, room service, and even food packets.

6. Assist in surgical procedures.

7. In addition to making music, robots can also monitor shorelines for harmful predators, assist with search and rescue, and even assist with food preparation.

The terms "static", "dynamic", and "semi-dynamic" refer to different types of robots and machines based on their functionality and capability to move. Here is a brief overview of each category:

1. Static robots: These are robots or machines that are fixed in place and do not have the ability to move or change position. Examples of static robots include assembly line machines, conveyor systems, and industrial robots used for welding or painting.
2. Dynamic robots: These are robots or machines that have the ability to move and change position. Dynamic robots are typically used for tasks such as navigation, exploration, and manipulation. Examples of dynamic robots include mobile robots, aerial drones, and humanoid robots.
3. Semi-dynamic robots: These are robots or machines that have some level of mobility, but are limited in their movement compared to dynamic robots. Semi-dynamic robots are typically used for tasks that require a combination of mobility and stability, such as robots used in agriculture, manufacturing, or healthcare. Examples of semi-dynamic robots include industrial robots with limited mobility, exoskeletons, and rehabilitation robots.

In general, the choice between static, dynamic, and semi-dynamic robots will depend on the specific task or application. For example, a static robot may be sufficient for an assembly line task that does not require mobility, while a dynamic robot may be necessary for a task that requires exploration or navigation. Similarly, a semi-dynamic robot may be a good choice for a task that requires both mobility and stability, such as in rehabilitation or agriculture.

A machine is anything that helps us do work more easily. It can be a computer, car, robot, or even a simple tool like a hammer. Machines are built to follow instructions and do tasks faster and more accurately than humans.

🔍 How Are Machines Different from Humans?

🤖 Machines:
✅ Follow commands – They only do what they are programmed to do.
✅ Work non-stop – No need for sleep, food, or rest.
✅ Process data quickly – AI and computers can solve problems in seconds.
✅ No emotions or creativity – Machines can’t think, feel, or make decisions like humans.

🧠 Humans:
✅ Think & learn – We can solve new problems and adapt to situations.
✅ Have emotions – We feel happiness, sadness, and empathy.
✅ Make creative decisions – Unlike machines, we can imagine, innovate, and create.
✅ Need rest – Unlike machines, we get tired and need food, sleep, and breaks.

🚀 Can Machines Ever Be Like Humans?

AI is getting smarter, but machines still don’t have real thoughts, emotions, or creativity. They can copy human behavior, but they don’t truly understand or feel like we do.

💡 What do you think? Will machines ever be just like us? Let’s discuss! ⬇️

👉 Follow me for more interesting tech topics! 🚀

Atlas 2, formally known as the new electric Atlas platform, is a major leap for Boston Dynamics' humanoid robots [1]. Here's what sets it apart:

• Electric Powered: Unlike its hydraulic predecessor, Atlas 2 is fully electric. This makes it quieter, lighter, and potentially more efficient for real-world applications [1, 3].
• Enhanced Mobility and Dexterity: Atlas 2 boasts impressive agility and maneuverability. It can walk, run, and even perform complex maneuvers like jumping and twisting [4, 5]. Its improved dexterity allows it to handle objects with surprising precision, from delicate tasks to lifting heavy loads [4].
• Humanoid Design for Human Environments: The bipedal form factor allows Atlas 2 to navigate spaces designed for humans, like buildings and factories [3]. This is a potential advantage over other robots that might struggle in such environments.
• Advanced Perception: Equipped with sensors and high-resolution cameras, Atlas 2 can perceive its surroundings and react accordingly [2]. This allows it to adapt to changing situations and navigate complex terrain.

Overall, Atlas 2 represents a significant step towards practical humanoid robots. Its electric design, enhanced mobility, dexterity, and human-centric design make it a versatile platform for various tasks, from industrial applications to disaster response scenarios.

For a visual demonstration of Atlas 2's capabilities, you can check out Boston Dynamics' video on YouTube

What are some of the most common robot parts?

1. My answer off the tip-top of my head — all articulated: Head, sensors, microphone, eyes, speech mechanism, arms, legs, torso, hands, fingers, feet, a computer ……….
2. WWW answers

A robot is a machine that can move on its own. They are powered by motors and electrical components like switches, relays, and sensors. Robots are not just for humans anymore as they have become more sophisticated with the advancement of technology.

Robots today can perform tasks that used to be done by humans such as moving boxes from one place to another or cleaning an office space. There are many different types of robots out there including industrial robots which make products in factories, mobile robots which use their small size to navigate around environments like warehouses and mobile service robots which help people with daily tasks like taking their groceries from the car park or helping them get in and out of the house.

I guess some of the answers for this question may be inaccurate and quite misleading due to the common misconception about the terminology of both fields.

Mechatronics engineering is a multidisciplinary field which combines electrical engineering, mechanical engineering, control engineering, and embedded systems. Mechatronics deals with the application of modern systems and control methods to practical situations. Cruise control, aircraft autopilot, and anti-lock braking systems (ABS) are typical mechatronic systems.

Robotics is a specific class of mechatronic systems. A robot is a (re-)programmable mechanical device which performs operations while interacting with its environment. Robotic engineering is the science behind the manufacturing and application of robots. Industrial robots (manipulators) and aerial robots such as quad-rotor helicopters are typical examples of robotic systems.

Note that a system being "autonomous" is a whole different categorization independent of the field. You may find non-autonomous robots, autonomous mechatronic systems, and vice versa.

Commonly you will find most bachelor/master programs, institutions, and conferences holding the name of "Robotics and Mechatronics" to eliminate the ambiguity.

However, if you're choosing between robotics and mechatronics engineering as two programs offered from different universities as undergraduate or even graduate studies, you should reconsider your question. Most probably both programs are offering almost the same content which may serve as a good base for your next step whether it's in the industry or in the academic field. Although you may find differences in the scope of the study (e.g., some concentrating on modelling and some on control), often it's independent of the "title" whether robotics or mechatronics engineering.

The simple answer is Hobby Electronics is for self satisfaction and Actual Robotics is for the Consumer application.Practically, there is no difference between Hobby and actual robotics.

Hobby Robotics vs Actual Robotics:

• The amount of investment is small, but not limited. Amount of investments are huge in case of Actual robotics
• You are the only one working on the project. You are the boss. And you can do anything you wish. In case of actual, you will be designing what is required for the end product based on the specifications given.
• You will probably use Arduino, PIC , ATMega or Raspberry Pi. You are not custom building the controllers or using sophisticated controllers.
• For hobby Robotics, you might build wheeled autonomous robot to perform certain tasks that you like or pick and place robots or autonomous boats. There are many hobby Robotic projects. With a decent investment, you can also design autonomous solar plane, a 4 feet butler humanoid robot using 1 Raspberry Pi (Or Laptop) and many controllers like arduino, ATMega etc. Depending on your interest, your skills and investment you can do anything.

• For Hobby Robotics, you wont be designing Autonomous robots for Surgery and other medical applications. You will not be designing a robot that does paint job for the cars and assemble automobiles etc.

So basically, I do not find any difference between Hobby Robotics and Actual Robotics. If you have good skills and decent investment, you can build anything.

What is a Machine?

A machine is a man-made device or system designed to perform tasks by applying mechanical, electrical, or computational processes. Machines can range from simple tools like pulleys and levers to complex systems like computers, robots, and AI-driven automation. They help humans perform tasks more efficiently by reducing physical effort, increasing precision, and automating repetitive actions.

How is a Machine Different from Humans?

While machines and humans share some abilities like problem-solving and task execution, they differ in several fundamental ways:

• Consciousness & Emotions – Humans have emotions, self-awareness, and consciousness, while machines operate based on programmed logic and algorithms.
• Creativity & Intuition – Humans can think creatively, innovate, and imagine new possibilities beyond existing patterns. Machines, on the other hand, follow predefined instructions and cannot truly "think outside the box."
• Adaptability – Humans learn and adapt dynamically in real-world situations, while machines require updates or retraining to change their behavior.
• Physical & Biological Differences – Machines are made of metal, circuits, and software, whereas humans have biological cells, a nervous system, and the ability to grow and heal.
• Decision-Making & Ethics – Humans make moral and ethical decisions based on emotions, values, and experience, while machines rely on programmed logic and lack a true understanding of ethics.
• Energy & Maintenance – Humans sustain themselves through food and rest, while machines need electricity, fuel, or external power sources and require maintenance or repairs when they break down.

Conclusion

Machines are designed to assist humans by improving efficiency and automating tasks, but they lack human traits like emotions, creativity, and ethical reasoning. While AI and robotics continue to evolve, they still remain tools rather than true replacements for human intelligence and decision-making.

The technical definition of the robot is a machine that does a task instead of a human, typically without human direction, and going by this definition robotics may well go into prehistory. The first robot, most likely, was an animal trap; neolithic hunters are known to have used traps to capture prey.

What would make this a robot rather than just a machine? It is its ability to act without human intervention. When the animal comes upon the device, activates the trigger and is captured by the mechanism, the machine has acted by itself, using a sensor to detect a change in its surroundings and act accordingly. Although simple when compared to the most advanced robotics of today, the principle is the same.

(Not to diminish the ingenuity of our forefathers, of course. The fact that very similar animal traps continue to be used to this day is a clear demonstration of their skill.)

There is a lot of hype of late in the manufacturing sector about robots and how they aid manufacturers to address some of the challenges they face in today’s market, such as augmented productivity and the scarcity of skilled workers.

Here’s an overview of four types of industrial robots that every manufacturer should know!

Yes, a robot can create another robot or something similar. This is known as automation or robotic automation, and it involves the use of robots to perform tasks that were previously performed by human workers. The process typically involves programming a robot to perform specific tasks, such as assembly, welding, or painting.

In some cases, robots can also be programmed to build other robots, a process known as self-replication. This can be achieved through a combination of software, hardware, and materials, and can involve the use of specialized robots known as "automated manufacturing systems." These systems can be programmed to assemble robots using pre-manufactured components, such as motors, actuators, and sensors.

The process of creating robots through automation is becoming increasingly common in manufacturing and production, as it allows for greater efficiency, precision, and speed in the production process. However, the development of these systems also raises important ethical and economic questions, such as the impact on human employment and the need for new skills and training in the workforce.

1. Design: Humanoid robots resemble human form with a head, torso, arms, and legs, unlike other robots which may have varied structures.

2. Functionality: Humanoids are designed to perform tasks in human environments, mimicking human movements and interactions.

3. Applications: Used in customer service, caregiving, and human interaction roles, while other robots may focus on manufacturing, exploration, or specialized tasks.

4. Complexity: Typically more complex in terms of mechanics, sensors, and AI to replicate human actions.

5. Interaction: Often equipped with advanced AI for better communication and social interaction with humans.

The planning of trajectory for the mobile robots, and consequently its better estimative of positioning, is the reason of intense scientific inquiry. A good path planning of trajectory is fundamental for optimization of the interrelation between the environment and the mobile robot. A great diversity of techniques based on different physical principles exists and different algorithms for the localization and the planning of the best possible trajectory.

The localization in structuralized environment is helped, in general, by external elements that are called of markers. It is possible to use natural markers that already existing in the environment for the localization. Another possibility is to add intensionally to the environment artificial markers to guide the localization of the robot.

This work uses an important mathematical and computational tool for the calculation of the data fusing collected by the sensors and the disturbances caused for the errors, with the purpose of estimate the mobile robot pose, that is the Extended Kalman filter (EKF). A mobile robot platform with differential traction and nonholonomics restrictions is used for experiments validation.

In the direct kinematics the system of mobile robot positioning is presented. A model for state space, plants and measurements are presented, that are needed for the development of the necessary attributes to the positioning estimates made with the Extended Kalman filter. Finally, we present the experimental and simulation results obtained from the models created.

2. Direct Kinematics for Differential Traction

This paper focuses on the study of the mobile robot platform, with differential driving wheels mounted on the same axis and a free castor front wheel, whose prototype used to validate the proposal system is depicted in Figures 11 and 1 which illustrate the elements of the platform.

Figure 1

Mobile robot platform and elements.

We assume that the robot is in one certain point (

) directed for a position throughout a line making an angle

with

axis, as illustrated in Figure 2.

Figure 2

Direct kinematics for differential traction in mobile robots.

Through the manipulation the control parameters

and

, the robot can be led at different positionings. The determination of the possible positionings to be reached, once given the control parameters, is known as direct kinematics problem for the robot. As illustrated in Figure 2, in which the robot is located in position (

), we have for the trigonometrical relations of the system

where ICC is the robot instantaneous curvature center.

As

and

are time functions and if the robot is in the pose (

) in the time

, and if the left and right wheel has ground contact speed

and

, respectively, then, in the time

the position of the robot is given by

Equation (2.2) describes the motion of a robot rotating a distance

about its ICC with an angular velocity given by

[1]. Different classes of robots will provide different expressions for

and

[2].

The forward kinematics problem is solved by integrating (2.2) from some initial condition (

), it is possible to compute where the robot will be at any time

based on the control parameters

and

. For the special case of a differential drive vehicle, it is given by

A question more interesting, and at same time more difficult to answer, is the following how can the control parameters could be selected in a way the robot obtain a specific global position or follow a specific trajectory? This is known as the task of determining the vehicle's inverse kinematics: inverting the kinematic relationship between control inputs and behavior. It is also related to the problem of trajectory planning.

2.1. Inverse Kinematics for Differential Drive Robots

Equation (2.3) describe a constraint on the robot velocity that cannot be integrated into a positional constraint. This is known as a nonholonomic constraint and it is in general very difficult to solve, although solutions are straightforward for limited classes of the control functions

and

[3]. For example, if it is assumed that

,

and

, then (2.3) yields

where

. Given a goal time

and goal position (

). Equation (2.4) solves for

and

but does not provide a solution for independent control of

. There are, in fact, infinity solutions for

and

from (2.4), but all correspond to the robot moving about the same circle that passes through (0,0) at

and

at

; however, the robot goes around the circle different numbers of times and in different directions.

3. Position Estimation with RF Signal ToF

The communication system between the mobile robot and the beacons is follow described. The mobile robot, and each one of the beacons, have a module of control and reception of the address codes and a module of transmission. The communication protocol between the embedded control system, located in the mobile robot, and the beacons, that are located in strategical points in the environment, are composed of a frame formed for five quaternary codes.

3.1. Communication Protocol

The timing diagram shown in Figure 3 illustrates as each one of the codes in function of clock signal is formed.

Figure 3

Communication protocol codes.

Each half clock period correspond to a time about 896 μs. Each code has a time period composed of 8 clocks cycles, that is 14,336 ms. Table 1 depicts in a logic way the formation of the codes.

Table 1

Logic formation of each code.

Each code is configured by a logic signals sequence, each one with a determined period. Table 2 shows how each logic signal of each code is composed.

Table 2

The timing of the logic codes.

The idea is to mount a quaternary codifier using binary logic levels, associates in such way that the logic levels alternate and the total period of each code is the same. The codification implemented was conceived here aiming the minimizing of the errors, such as the transmission of the one exactly signal level is transmitted without transitions of level for long time periods. In this case, the receiver tends to put out of the way itself and to perform the reading out the correct point, originating errors. In this way, RF transmission of the codes is sufficiently robust and trustworthy, practically extinguishing errors of signal decoding signal inside the area of system range.

3.2. The Communication Frame

The communication frames used between the mobile robot and the beacons are composed for five quaternary codes. The Figure 4 illustrates an example of a communication protocol frame. As each code has a period about 14,336 ms, the all frame has transmission time about 71,68 ms.

Figure 4

Example of a communication protocol frame.

The maximum number of possible combination is given by

Each beacon has it own address, composed by five codes. In this way, the system is able to deal with up to 1024 beacons, with their own individual address.

3.3. The RF Link

The coded signal is transmitted in RF modulated by BASK-OOK technic. The carrier signal frequency is about 433,92 MHz (UHF band).

The RF link uses a half-duplex channel between mobile robot and beacons. The mobile robot control system is previously programmed with quantity and address of each beacon. Figure 5 depicts an example of environment configuration of the communication between the robot and beacons.

Figure 5

An example of environment beacons arrangement and the communication system.

3.4. Beacon Transceiver System

The beacon embedded system is composed basically by two modules. One is responsible for RF signal receive and make all the concerned computation. This module has a 16F630 PIC microcontroller, operating at 4 MHz clock frequency. The other one is the RF signal transmitter. This module is equipped with 12F635 microcontroller and also operates at 4 MHz. The system is able to operate in autonomous way, been programmed with specific address. In other hand, the mobile robot must be programmed with the amount and the address of all operative beacon inside the navigating environment. Figure 6 depicts the block diagram of the RF transceiver at mobile robot and at beacons.

Figure 6

Block diagram of the RF transceiver at mobile robot and at beacons.

Figure 7 shows the beacons RF transceiver modules. The transmission module (Figure 7(b)) is able to function in asynchronous independent way, emitting a address code frame in a certain period of predetermined time, or synchronous way commanded by the reception and control module (Figure 7(a)). In the first case, a battery 12 V A23 model is used which allows autonomy of more than 3 months of continuous use, due to ultra low power energy consumption given by the embedded microcontroller with nanowatt technology. In second case the power supply and transmission command are made by the reception control module, illustrated in Figure 7(a). This second one is the mode utilized by this work.

(a) Implemented reception module

(b) Implemented transmission module

(a) Implemented reception module

(b) Implemented transmission module

Figure 7

Beacons RF transceiver modules.

The mobile robot, as each one of the beacons, have a transceiver control system composed by reception module and transmission module. As the objective of our system is to provide a triangulation between the mobile robot position and the beacons, the transmission modules work in synchronous way. It is assumed that the module of control of reception-transmission of the mobile robot has been previously loaded with the amount of existing beacons in the environment and with its respective addresses codes. The functioning of the system goes to the following procedure.(1)The mobile robot emits a address code-frame for first beacon. In this instant it sends an interrupt control signal to the central processing unit for triggering and starts a timer counter. The robot then, waits the return of the signal. This return must occur in up to 100 ms.(2)If the signal returns, means that the beacon recognized the code and sent back the same code. In this instant is sent a signal to the robot embedded central processing unit for stops the timer and calculation of the signal return delay time, that could be about ns.(3)If the signal was not returned, means that the beacon is out of area reach or occurred some error in signal transmission-reception.(4)Increment the number of beacon and go to the loop first item.

The distance between the robot and a certain

beacon is computed with base of the delay time in the reception of the same transmitted code. The total elapsed time between the code final transmission, sent by the robot, and the reception of the same code, sent back to the beacon, can be calculated by

where

is the travel signal time between leaves robot transmitter and reach beacon reception,

is the processing signal time by the beacon,

is the signal return elapse time,

is the frame code period and

is the processing time of the sent back signal received by robot.

It is well known that RF signal cover one meter in about 3,3 ns because its velocity is about 0,3 m/ns in air. We can considering that the linear speed of the robot is so small that the displacement of the robot could be considering as being zero during the time

. In this way, the distance in meters between the mobile robot and the beacon

can be given by

where

and

are given in ns.

The elapsed time

is computed with a 64 bits timer of the Texas Instrument TMS320C6474-1200 dualcore robot embedded processor. The instruction cycle time of it is about 0,83 ns (1,2 GHz clock Device), allowing timer calculations in order of ns, essential for our case of study. The times

and

are determined empirically and

ms. In this way, the covered distance between the robot and beacon

should be done by

Algorithm 1 depicts the computation method for distance

calculation using RF ToF.

input: The mobile robot is initialized with total number of active beacons

in the environment (

) and theirs respectives address.

output: The distance between the robot and the beacon

based at delay RF signal time.

System setup;

while The system is active  do

for

until

do

To transmit a frame-coded for beacon

;

To initiate timer for period

calculation;

if  The same frame-coded signal returns.AND.

ms  then

Stops the timer and calculate the time

;

Calculate the distance

;

else

Some tramission/reception RF signal error occurred;

Try next active beacon;

end

end

end

Algorithm 1

Computational method for distance

calculation using RF ToF.

4. Triangulation

Triangulation refers to the solution of constraint equations relating the pose of an observer to the positions of a set of landmarks. Pose estimation using triangulation methods from known landmarks has been practiced since ancient times and was exploited by the ancient Romans in mapping and road construction during the Roman Empire.

The simplest and most familiar case that gives the technique its name is that of using bearings or distance measurements to two (or more) landmarks to solve a planar positioning task, thus solving for the parameters of a triangle given a combination of sides and angles. This type of position estimation method has its roots in antiquity in the context of architecture and cartography and is important today in several domains such as survey science. Although a triangular geometry is not the only possible configuration for using landmarks or beacons, it is the most natural [1].

Although landmarks, beacons and robots exist in a three-dimensional world, the limited accuracy associated with height information often results in a two-dimensional problem in practice; elevation information is sometimes used to validate the results. Thus, although the triangulation problem for a point robot should be considered as a problem with six unknown parameters (three position variables and three orientation variables), more commonly the task is posed as a two-dimensional (or three-dimensional) problem with two-dimensional (or three-dimensional) landmarks [4].

Depending on the combinations of sides (S) and angles (A) given, the triangulation problem is described as “side-angle-side” (SAS), and so forth. All cases permit a solution except for the AAA case in which the scale of the triangle is not constrained by the parameters. In practice, a given sensing technology often returns either an angular measurement or a distance measurement, and the landmark positions are typically known. Thus, the SAA and SSS cases are the most commonly encountered. More generally, the problem can involve some combination of algebraic constraints that relate the measurements to the pose parameters. These are typically nonlinear, and hence a solution may be dependent on an initial position estimate or constraint [5]. This can be formulated as

where the vector

expresses the pose variables to be estimated (normally, for 2D cases

), and

is the vector of measurements to be used. In the specific case of estimating the position of an oriented robot in the plane, this becomes

If only the distance to a landmark is available, a single measurement constrains the robot's position to the arc of a circle. Figure 8 illustrates perhaps the simples triangulation case. A robot at an unknown location

senses two beacons

and

by measuring the distances

and

to them. This corresponds to our case of study in which active beacons at known locations emit a signal and the robot obtains distances based on the time delay to arrive at the robot. The robot must lie at the intersection of the circle of radius

with center at

, and with the circle or radius

with center at

. Without loss of generality we can assume that

is at the origin and that

is at

. Then we have

A small amount of algebra results in

resulting in two solutions

and

.

Figure 8

Simple triangulation example. A robot at an unknown location

.

In a typical application, beacons are located on walls, and thus the spurious (in our example, the

) solution can be identified because it corresponds to the robot's being located on the wrong side of (inside) the wall.

Although distances to beacons provide a simple example of triangulation, most sensors and landmarks result in more complex situations [6]. The situation for two beacons is illustrated in Figure 9(a). The robot senses two known beacons and measures the bearing to each beacon relative to its own straightahead direction. This obtains the difference ins gearing between the directions to the two beacons and constrains the true position of the robot to lie on that portion of the circle shown in Figure 9(a). We can note that the mathematics admits two circular arcs, but one can be excluded based on the left-right ordering of the beacon directions. The loci of points that satisfy the bearing difference is given by

where

and

are the distances from the robot's current position

to beacons

and

, respectively. The visibility of a third beacon, as can be seen at Figure 9(b), gives rise to three nonlinear constraints on

,

and

:

which can be solved using standard techniques to obtain

,

, and

. Knowledge of

,

and

leads to the robot's position [7].

(a) Triangulation with two beacons

(b) Triangulation with three beacons

(a) Triangulation with two beacons

(b) Triangulation with three beacons

Figure 9

Location estimative of the mobile robot based on beacons triangulation.

The geometric arrangement of beacons with respect to the robot observer is critical to the accuracy of the solution. A particular arrangement of beacons may provide high accuracy when observed from some locations and low accuracy when observed from others. For example, in two dimensions a set of three collinear beacons observed with a bearing measuring device can provide good positional accuracy for triangulation when viewed from a point away from the line joining the beacons (e.g., a point that forms an equilateral triangle with respect to the external beacons). On the other hand, if the robot is located on the line joining the beacons, the position can only be constrained to lie somewhere on this (infinite) line.

4.1. Triangulation with RF Beacons

In our case of study the beacons's position at 2D environment are known and thus, the distances between the beacons. If the

beacons are positioned at points

and the robot's position is given by

, then, (4.6), that express the robot triangulation with three beacons, yield

where the robot's position

can be inferred by numerical methods.

5. Data Fusion

The question of how to combine data of different sources generates a great quantity of research in the academics ambients and at the research laboratories. In the context of the mobile robotic systems, the data fusing must be effected in at least three distinct fields: arranging measurements of different sensors, different positions, and different times.

5.1. State Space Models

A system for which the state vector can be fully determined from enough number of measurements is described as being perceivable. As used by Bar-Shalom et al. [8], to describe the estimate of state to be computed, is used

to denote the estimate of the vector

in the time step

using collected data in a period of time where

is including the time step

. Using remarks until step

, however abstaining

, to form a prediction, this is in general expressed as

which denotes prediction of state vector

based in the information availability strict before the time

. Being based on the availability of information until, and including themselves, the time

, it forms an updated date state estimated

, which denotes the estimative of the state vector

in the time

.

5.2. Plant Model

A plant model that describes how a state of the system

, which in ours particular case represents the position of the mobile robot, changes in function of the time, control input

and the noise

can be expressed by

where

is the state transition function and

is the noise function. One of most common and conventional way to represent the noise model is using the Gaussian noise model with zero average with covariance

(Gelb also utilizes the notation

to represent the Gaussian noise model with zero average with covariance

) [9].

A model of linear plant, from (5.1), can be written as

where the matrix

tells how the system evolves from a state to another one in the absence of entrances (that is frequently given as a identity matrix) and the matrix

tells how the entrances of the control modifies the state of the system.

Considering a linear omnidirectional mobile robot with displacements restricted to the plan, a simple model of plant can be considered, as follow presented.

The robot state is given by

where

describes the robot pose at global coordinates system. As suppose that the robot is equipped with some omnidirectional locomotion system, then the control input

can be described as an independent change in the robot

and

localization. If the error in the movement of the robot is independent in the directions

and

, and if this error can be modeled by some noise function

and

, then the robot plant model is given by (5.1), and at ours particular case becomes

which the robot moves to where it will be commanded with each movement being corrupted by the noise process. Equation (5.3) describes a model of linear plant.

5.3. Measurement Model

The measurement model describes how the sensors data change in function of the system state. If the sensor model is inverting (hypothetical case) it will allows that the sensor data cam be used for state calculations. As explained for Leonard and Durrant-Whyte [10], which developed works with mobile robots equipped with ultrasonic sensors, the measurement model tells the sensor observation for robot position and the target geometry (bulkhead) that produces the observation, and it has the form of

where

is the noise function for

so that represents the Gaussian noise model with zero average with covariance matrix

, and

is the target state vector and change accordingly the aim shape, that can be basically corners, edges, cylindrical surfaces or plain surfaces. The measurement function

express a observation

about the sensor

for the target

with a vehicle localization function

and the target geometry.

As well as in the plant model, a linear measurement model is particularly interesting. From (5.4), this takes the form of

where

is the matrix which express how the measurements are derived with the state linear transformation. This simple case illustrate how a stare estimative cam be recover for measurement:

if it is assumed the

matrix is reversibly.

The majority of the mobile robots traction by wheels (MRTW) cannot be modeled in a linear way, and it is necessary to consider plant, model and not linear estimative processes, as ours case. Considering a nonlinear system, where the robot can be modeled as a punctual robot with independent control of the orientation and the speed, as the case of the synchronous transmission mobile robots. Then, for this type of robot, the control input

that is, in the period

until

the robot moves from a distance

forward, to direction that it is pointed, and then rotate itself

. The system state is given by

and the nonlinear plant model is given by

Each movement of the robot has a noise process parcel

, which has a covariance matrix known or estimable

. It is assumed for this process of noise that the same satisfies the necessary conditions that assures the use of the Kalman filter(presented in Section 5.4). If, in the practical way, the robot moves in distinct steps composites of pure rotations or pure translations (i.e., only one between

and

is different of zero), then only two versions of

are necessary.

Assuming now that the robot is equipped with a sensor that can determine the distance from robot to one determined target marker in the environment. For example, the target can emit an only sound with a known frequency, and the robot is equipped with a receiver that captures the sound. If the robot and the sound emitted at the target have synchronized clocks, the distance between the target and the robot can be estimated. If the target is located at (

), measurement model for this robot comes from (5.4) and is given by

This model of measurement has the parcel of degradation given by the noise process

with covariance matrix

.

5.4. Kalman Filter

To control a mobile robot, frequently it is necessary to combine information of multiple sources. The information that comes from trustworthy sources must have grater importance about those one collected by less trustworthy sensors.

A general way to compute the sources that are more or less trustworthy and which weights must be given to the data of each source, making a weighed pounder addition of the measurements, are known with Kalman filter [11]. It is one of the methods more widely used for sensorial fusing in mobile robotics applications [12]. This filter is frequently used to combine data gotten from different sensors in a statistical optimal estimate. If a system can be described with a linear model and the uncertainties of the sensors and the system can be modeled as white Gaussian noises, then the Kalman filter gives a optimal estimate statistical for the casting data. This means that, under certain conditions, the Kalman filter is able to find the best estimative based on correction of each individual measure [13]. Figure 10 depicts the particular schematic for Kalman filter localization [14].

Figure 10

Schematic for Kalman filter mobile robot localization.

Figure 11

Experimental mobile robot prototype.

The Kalman filter consists of the stages follow presented in each time step, except for the initial step. It is assumed, for model simplification, that the state transition matrix

and the observation function

remain constant in function of the time. Using the plant model of (5.2) and computing a system state estimate in the time (

) based on robot position knowledge in the instant of time

, we have how the system evolves in the time with the input control

:

In some practical equations the input

is not used. It can also, to actualize the state certainty as expressed for the state covariance matrix

through the displacement in the time, as:

Equation (5.10) express the way which the system state knowledge gradually decays with passing of the time, in the absence of external corrections. The Kalman gain can be expressed as

but, how it did not compute

, this can be computed by

Using this matrix, a estimate of revised state can be calculated that includes the additional information gotten by the measurement. This involves the comparison of the current sensors data

with the data of the foreseen sensors using it state estimate. The difference between the two terms

or, at the linear case

is related as the innovation. If the state estimate is perfect, the innovation must be not zero only which the sensor noise. Then, the state estimate actualized is given by

and, the up-to-date state covariance matrix is given by

where

is the identity matrix.

5.5. Extended Kalman Filter

In many robotic applications with sensor data fusing, the system to be modeled fails for having a nonlinear Gaussian noise distribution. While the errors are approximately Gaussian, the Kalman filter can be used, even so, probably will not be optimal. For nonlinear systems, is used the Extended Kalman filter (EKF). This involves the linearization of the plant, (5.1) and, if necessary, the linearization of the measurement (5.4) cancelling high order terms of the Taylor expansion [15].

The model plant linearization involves the jacobian calculation of the plant model

and to use it as a linear estimate of

in the Kalman filter. The model measurement linearization involves the jacobian calculation of the measurement model

and to use it as linear estimative

.

For ours case of EKF use, takes the example of the plant model and nonlinear measurement model presented in Section 5.3. To simplify the exposition, it is assumed that

and

. For each robot movement, the next stages are follow.(1)For represent the robot displacement, it's used (5.1) and (5.7). The known control parameters are utilized for the robot pose estimation at the time , as:(2)A linearized plant model version is generated in the current estimative of the robot pose as:(3)The state uncertainty is generated by the state covariance matrix actualization, using measurements obtained until the time , including itself, through:which is the result of the linearization of (5.10).(4)The sensor model is linearized around current estimative of robot pose , as:If the sensor have a reference dot like target in , then(5)Using the value , the Kalman gain, that comes for (5.12), is computed like(6)The innovation, like presented in (5.13), in this way is compute as(7)Now, is possible to calculate the robot pose estimative, like shown at (5.15), like been(8)Finally, the actualized covariance matrix, like illustrated by (5.16), is now calculate as

After certain time interval, that is shortest possible, the derivatives used in the linearization model must be recalculated through the estimated current state. This backwards a deficiency in the EKF: if the estimated state is very far from the current state, the linear approach of the system behavior will not be enough precise.

6. Mobile Robot Rapid Prototyping

Figure 11 illustrates the mobile robot prototype developed by a research team led by authors and used for experiments validation and as base for the models and simulations.

The use of the rapid prototyping technique in mobile robotic systems differs from the traditional target used in mechanics engineering and enters in new field of research and development for projects of mobile robots mechatronics systems. In this way, the rapid prototyping of these systems is associated not only with the project of the physical system, but mainly with the experimental implementations in the fields of hardware and software of the robotic system. It is fundamental that the architecture of hardware of the considered system be opened and flexible in the way of effecting the necessary modifications for system optimization. A proposal of open architecture system was presented in [16].

The software of the embedded control system of the mobile robot, in the context of the rapid prototyping, can be elaborated in simulators and tested all the parameters for adjustments that makes necessary in accordance with the physical system to be implemented, the hardware architecture, the actuators and the sensors. In this way, in the context of this work, the rapid prototyping is then the methodology that allows the creation of a virtual environment of simulation for the project of a controller for mobile robots. After being tested and validated in the simulator, the control system is programmed in the control board memory of the mobile robot. In this way, a economy of time and material are obtained, sooner validating all the model virtually and later operating the physical implementation of the system.

6.1. HIL (Hardware-in-the-Loop)

The HIL technique is used in development and tests for real-time embedded systems. HIL provide a platform accomplish of development for adding the complexity of the plant under control to the tests platform. The control system is enclosed in the tests and developments through its mathematical models representations and all the respective dynamic model [17].

The Figure 12 illustrates the use of the HIL simulation technique for real-time simulation and experimental validation of the considered mobile robotic system. With the utilization of HIL it's possible to implement de Kalman filter methodology with others embedded control techniques for improve the mobile robot localization.

Figure 12

HIL technique for mobile robot system.

7. Experimental Results

Figures 13 and 14 depicts the result of EFK pose estimative applied in the trajectory of the mobile robot prototype (showed in Figure 11) accomplishing different routes.

Figure 13

Almost linear trajectory.

Figure 14

Irregular curvilinear trajectory.

As illustrated in Figure 13 it can seen that the mobile robot starts at point (10,10) moving itself with constant linear speed. The ellipses delimit the area of uncertainty in the estimates. It can be observed that these ellipses are bigger in the trajectory extremities, because in these points lass measurements date are computed. The average quadratic error varies depending on the chosen trajectory. It can be noticed that the pose estimative improves for more linear trajectories and with high frequency of on-board sensors measurements.

Figure 14 depicts another mobile robot trajectory example. In this case, the route is more irregular and curvilinear. The uncertainty of pose estimative are great than the first example (Figure 13). Thats because in this trajectory the on-board sensors measurements becomes more unprecise and with low-frequency samples.

Usually a robot is making some decisions (traditionally related to motion of something) in an autonomous manner, and these are informed by sensed information. Most of hobby robotics (like battle bots) are remote controlled and therefore most or all decisions are made by the controller or user. A complication is presented by most commercial quadrotors that are remote operated, since they have some autonomous stabilization that satisfies the criteria mentioned earlier. So you could say there's a gradient involved in how autonomous or RC a machine really is. An actual robot operates autonomously for the most part.

The degree of autonomy is important since an actual robot's purpose is to do a task that a human would otherwise have to do. A hobby robot is usually not replacing any human labor, and is associated with leisure/sport.