Robotics and Servo Mechanism

This definition implies that a tool can only be called a “robot” if it contains a movable mechanism, influenced by sensing, planning, And actuation And control parts. It does not insinuate that a maximum number of these parts must be implemented in program, or be changeable by the “consumer” who makes use of the device; for example, the motion behavior can have been hard-wired in to the tool by the manufacturer.

So, the introduced definition, as well as the remainder of the material in this part of the Book, covers not “pure” robotics or only “intelligent” robots, but the broader domain of robotics And automation. This includes “dumb” robots such as: metal And woodworking machines, “intelligent” washing machines, dish washers And pool cleaning robots, etc. These examples all have sensing, planning And control, but often not in individually separated parts. For example, the sensing And planning behavior of the pool cleaning robot have been integrated in to the mechanical design of the tool, by the intelligence of the human developer.

Robotics is, to a large extent, all about process integration, achieving a task by an actuated mechanical tool, by an “intelligent” integration of parts, plenty of of which it shares with other domains, such as systems And control, computer science, character animation, machine design, computer vision, artificial intelligence, cognitive science, biomechanics, etc. In addition, the boundaries of robotics cannot be clearly defined, since also its “core” ideas, ideas And algorithms are being applied in an ever increasing number of “external” applications, And, vice versa, core expertise from other domains (vision, biology, cognitive science or biomechanics, for example) are becoming crucial parts in increasingly modern robotic systems.

This part of the WEBook makes an work to define what exactly is that abovementioned core material of the robotics domain, And to report it in a consistent And motivated structure. Nevertheless, this chosen structure is one of the plenty of feasible “views” that one can need to have on the robotics domain.

In the same vein, the abovementioned “definition” of robotics is not meant to be definitive or final, And it is only used as a rough framework to structure the various chapters

Parts of robotic systems

This figure depicts the parts that are part of all robotic systems. The purpose of this Section is to report the semantics of the terminology used to classify the chapters in the WEBook: “sensing”, “planning”, “modeling”, “control”, etc.

The actual robot is some mechanical tool (“mechanism”) that moves around in the environment, And, in doing so, physically interacts with this surroundings. This interaction involves the exchange of physical energy, in some form or another. Both the robot mechanism And the environment can be the “cause” of the physical interaction through “Actuation”, or experience the “effect” of the interaction, which can be measured through “Sensing”.

Robotics as an integrated process of control interacting with the physical world.

Sensing And actuation are the physical ports through which the “Controller” of the robot determines the interaction of its mechanical body with the physical world. As mentioned already before, the controller can, in one extreme, consist of program only, but in the other extreme everything may even be implemented in hardware.

Within the Controller part, several sub-activities are often identified:

Modelling. The input-output relationships of all control parts can (but need not) be derived from information that is stored in a model. This model can have plenty of forms: analytical formulas, empirical look-up tables, fuzzy rules, neural networks, etc.

The name “model” often gives rise to heated discussions among different research “schools”, And the WEBook is not interested in taking a stance in this debate: within the WEBook, “model” is to be understood with its minimal semantics: “any information that is used to choose or influence the input-output relationships of parts in the Controller.”

The other parts discussed below can all have models inside. A “System model” can be used to tie multiple parts together, but it is clear that not all robots use a Process model. The “Sensing model” And “Actuation model” contain the information with which to transform raw physical information in to task-dependent information for the controller, And vice versa.

Planning. This is the activity that predicts the result of potential actions, And selects the “best” one. by definition, planning can only be completed on the basis of some kind of model.

Regulation. This part processes the outputs of the sensing And planning parts, to generate an actuation setpoint. Again, this regulation activity could or could not depend on some kind of (process) model.

The term “control” is often used in lieu of “regulation”, but it is impossible to clearly identify the domains that use one term or the other. The meaning used in the WEBook will be clear from the context.

Scales in robotic systems

The abovementioned “components” description of a robotic process is to be complemented by a “scale” description, i.e., the following process scales have a large influence on the specific content of the planning, sensing, modelling And control parts at one particular scale, And hence also on the corresponding sections of the WEBook.

Mechanical scale. The physical volume of the robot determines to a large extent the limites of what can be completed with it. Roughly speaking, a large-scale robot (such as an autonomous container crane or a space shuttle) has different capabilities And control issues than a macro robot (such as an industrial robot arm), a desktop robot (such as those “sumo” robots popular with hobbyists), or milli micro or nano robots.
Spatial scale. There’s large differences between robots that act in 1D, 2D, 3D, or 6D (seven positions And seven orientations).

Time scale. There’s large differences between robots that must react within hours, seconds, milliseconds, or microseconds.

Power density scale. A robot must be actuated in order to move, but actuators need space as well as energy, so the ratio between both determines some capabilities of the robot.

Process complexity scale. The complexity of a robot process increases with the number of interactions between independent sub-systems, And the control parts must modify to this complexity.

Computational complexity scale. Robot controllers are inevitably jogging on real-world computing hardware, so they are constrained by the obtainable number of computations, the obtainable communication bandwidth, And the obtainable memory storage.

Obviously, these scale parameters never apply independently to the same process. For example, a process that must react at microseconds time scale can not be of macro mechanical scale or involve a high number of communication interactions with subsystems.

Background sensitivity

Finally, no description of even scientific material is ever fully objective or context-free, in the sense that it is impossible for contributors to the WEBook to “forget” their background when writing their contribution. In this respect, robotics has, roughly speaking, seven faces: (i) the mathematical And engineering face, which is “standardized” in the sense that a large consensus exists about the tools And theories to make use of (“systems theory”), And (ii) the AI face, which is poorly standardized, not because of a lack of interest or research efforts, but because of the inherent complexity of “intelligent behaviour.” The terminology And systems-thinking of both backgrounds are significantly different, hence the WEBook will accomodate sections on the same material but written from various perspectives. This is not a “bug”, but a “feature”: having the different views in the context of the same WEBook can only lead to a better mutual understanding And respect.

Research in engineering robotics follows the bottom-up approach: existing And working systems are extended And made more versatile. Research in artificial intelligence robotics is top-down: assuming that a set of low-level primitives is obtainable, how could one apply them in order to increase the “intelligence” of a process. The border between both approaches shifts continuously, as increasingly “intelligence” is cast in to algorithmic, system-theoretic form. For example, the response of a robot to sensor input was thought about “intelligent behaviour” in the late seventies And even early eighties. Hence, it belonged to A.I. Later it was shown that plenty of sensor-based tasks such as surface following or visual tracking could be formulated as control issues with algorithmic solutions. From then on, they did not belong to A.I. any more.

Robotics Expertise

Most industrial robots have at least the following four parts:

Sensors, Effectors, Actuators, Controllers, And common effectors known as Arms.

Plenty of other robots also have Artificial Intelligence And effectors that help it accomplish Mobility.

This section discusses the basic technologies of a robot. Click one of the links above or use the navigation bar menu on the far right.

Robotics Expertise – Sensors

Most robots of today are deaf And blind. Sensors can provide some limited feedback to the robot so it can do its job. Compared to the senses And abilities of even the simplest living things, robots have a long way to go.

The sensor sends information, in the kind of electronic signals back to the cfontroller. Sensors also give the robot controller information about its surroundings And lets it know the exact position of the arm, or the state of the world around it.
Sight, sound, touch, taste, And smell are the kinds of information they get from our world. Robots can be designed And programmed to get specific information that is beyond what our 5 senses can tell us. For example, a robot sensor might “see” in the dark, detect small amounts of invisible radiation or measure movement that is small or quick for the human eye to see.

Here are some things sensors are used for:

Physical Property

Expertise

Contact Bump, Switch
Distance Ultrasound, Radar, Infra Red
Light Level Picture Cells, Cameras
Sound Level microphones
Strain Strain Gauges
Rotation Encoders
Magnetism Compasses
Smell Chemical
Temperature Thermal, Infra Red
Inclination Inclinometers, Gyroscope
Pressure Pressure Gauges
Altitude Altimeters

Sensors can be made simple And sophisticated, depending on how much information needs to be stored. A switch is a simple on/off sensor used for turning the robot on And off. A human retina is a complex sensor that makes use of over a hundred million photosensitive elements (rods And cones). Sensors provide information to the robots brain, which can be treated in various ways. For example, they can basically react to the sensor output: if the switch is open, if the switch is closed, go.

Levels of Processing

To figure out if the switch is open or closed, you will need to measure the voltage going through the circuit, that is electronics. Now lets say that you have a microphone And you need to recognize a voice And separate it from noise; that is signal processing. Now you have a camera, And you need to take the pre-processed picture And now you need to figure out what those objects are, perhaps by comparing them to a large library of drawings; that is computation. Sensory information processing is a complex thing to try And do but the robot needs this in order to have a “brain”. The brain has to have analog or digital processing capabilities, wires to connect everything, support electronics to go with the computer, And batteries to provide power for the whole thing, in order to process the sensory information. Perception requires the robot to have sensors (power And electronics), computation (more power And electronics, And connectors (to connect it all).

Switch Sensors

Switches are the simplest sensors of all. They work without processing, at the electronics (circuit) level. Their general underlying principle is that of an open vs. closed circuit. If a switch is open, no current can flow; if it is closed, current can flow And be detected. This easy principle can (And is) used in a wide range of ways.

Switch sensors can be used in a variety of ways:

contact sensors: detect when the sensor has contacted another object (e.g., triggers when a robot hits a wall or grabs an object; these may even be whiskers)

limit sensors: detect when a mechanism has moved to the finish of its range

shaft encoder sensors: detects how plenty of times a shaft turns by having a switch click (open/close) every time the shaft turns (e.g., triggers for each turn, allowing for counting rotations)

There’s plenty of common switches: button switches, mouse switches, key board keys, phone keys, And others. Depending on how a switch is wired, it can be normally open or normally closed. This would of work depend on your robot’s electronics, mechanics, And its task. The simplest yet useful sensor for a robot is a “bump switch” that tells it when it is ran in to something, so it can back up And turn away. Even for such a simple idea, there’s plenty of different ways of implementation.

Light Sensors

Switches measure physical contact And light sensors measure the amount of light impacting a photocell, which is fundamentally a resistive sensor. The resistance of a photocell is low when it is brightly illuminated, i.e., when it is light; it is high when it is dark. In that sense, a light sensor is a “dark” sensor. In setting up a photocell sensor, you will finish up using the equations they learned above, because you will need to deal with the relationship of the photocell resistance picture, And the resistance And voltage in your electronics sensor circuit. Of work since you will be building the electronics And writing the program to measure And use the output of the light sensor, you can always manipulate it to make it simpler And more intuitive. What surrounds a light sensor affects its properties. The sensor can be shielded And positioned in various ways. Multiple sensors can be arranged in useful configurations And isolate them from each other with shields.

like switches, light sensors can be used in plenty of different ways:

Light sensors can measure:

light intensity (how light/dark it is)

differential intensity (difference between photocells)

break-beam (change/drop in intensity)

Light sensors can be shielded And focused in different ways

Their position And directionality on a robot can make a great deal of difference And impact

Polarized light

“Normal” light emanating from a source is non-polarized, which means it travels at all orientations with respect to th