Robotics and AI

The concept of creating machines that can operate autonomously dates back to classical times, but research into the functionality and potential uses of robots did not grow substantially until the 20th century. Throughout history, robotics has been often seen to mimic human behavior, and often manage tasks in a similar fashion. Today, robotics is a rapidly growing field, as technological advances continue, research, design, and building new robots serve various practical purposes, whether domestically, commercially, or militarily. Many robots do jobs that are hazardous to people such as defusing bombs, mines and exploring shipwrecks.

The word robotics was derived from the word robot, which was introduced to the public by Czech writer Karel ?apek in his play R.U.R. (Rossums Universal Robots), which was published in 1920. The word robot comes from the Slavic word robota, which means labour. The play begins in a factory that makes artificial people called robots, creatures who can be mistaken for humans – similar to the modern ideas of androids. Karel ?apek himself did not coin the word. He wrote a short letter in reference to an etymology in the Oxford English Dictionary in which he named his brother Josef ?apek as its actual originator.

According to the Oxford English Dictionary, the word robotics was first used in print by Isaac Asimov, in his science fiction short story "Liar!", published in May 1941 in Astounding Science Fiction. Asimov was unaware that he was coining the term; since the science and technology of electrical devices is electronics, he assumed robotics already referred to the science and technology of robots. In some of Asimovs other works, he states that the first use of the word robotics was in his short story Runaround (Astounding Science.

Automation & Robotic Technologies provides industrial automation solutions customized to fulfill your business requirements. When your project demands professionals that have the capability to design, build and integrate automation equipment, robotics and machine vision applications, A&RT delivers the outcome with the confidence and assurance needed.

Today the choices for automation equipment require the consumer to be completely up to date to take full advantage of all new technologies arriving in the marketplace. The integrator of this technology needs to flexible, adaptable and experienced enough to implement it properly. A&RT designs equipment with ‘quality’ product components to ensure our engineered solution provides the competitive edge it needs now, and in the future.

Robot Classification Objectives

1. Be aware of robot classification.
2. Be acquainted with manipulator arm geometry.
3. Understand the degrees of  freedom of a robotic system.
4. Recognize the type of power sources used in current robots.
5. Be familiar with type of motion.
6. Know a robot’s path control.
7. Understand the intelligence level of robots.

Classification: classified into six categories
1. Arm geometry: rectangular;cylindirical;spherical; jointed-arm(vertical);joined-arm(horizontal).
2. Degrees of freedom: robot arm; robot wrist.
3. Power sources: electrical;pneumatic;hydraulic;any combination.
4. Type of motion: slew motion; joint-interpolation; straight-line interpolation; circular interpolation.
5. Path control: limited sequence; point-to-point; continous path; controlled path.
6. Intellligence level: low-technology(nonservo); high-techonology(servo).

Robot control and Intelligence.

Robots are composed of movable physical structures, a power system (hydraulic, electrical or pneumatic), sensor system, a motor and a “brain” that tells its parts what to do.

They may have an “arm,” called an end effector that can use tools or be grippers. They will usually have a sensor which enables them to receive information about what is happening around them. A sensor may be used to sense speed and acceleration, tactile and distance or force and torque. The sensor allows it to adjust its own position in relation to a change it senses in the environment.

Most importantly, they have a reprogrammable “brain” that can guided from point to point through the various steps of a preplanned operation that is stored in the robotic control system. We produce robotic control systems which are easily reprogrammable and we provide step-by-step directions to help you do it.

We also give you an understanding of the scientific concepts that are behind the development of robots and robotic control systems.

Our system solutions enable rapid development of intelligent distributed control systems for applications including:
1. Digital Signal Processing Modules
2. Unmanned ground vehicles
3. Student Science Projects
4. Automotive
5.Education and research in the exciting field of robotics
6. Including swarm intelligence, emergent computation and control, neural networks,
7. genetic programming, other fascinating biologically inspired technologies

Direct Kinematics

The primary objective of the robotic manipulator is to control both the position and orientation of the tool in three dimensional space. . This requires the formulation of a relationship between the joint variables and the position and orientation of the tool which is termed as the direct kinematics problem.

It is formally stated as follows:
Once the vector of joint variables of the manipulator is known, we have to determine the position and orientation of the tool with respect to a coordinate frame attached to the robot base.

The Arm Equation

The solution to the direct kinematics problem requires the representation of position and orientation of the mobile tool w.r.t. a coordinate frame attach to the fixed base, this involves a sequence of coordinate transformations, involving both rotations and translations, from tool to wrist, wrist to elbow and so on; with each coordinate transformations represented by a matrix.  This way we arrive at a coordinate transformation matrix which is called the arm matrix. In its equation form, it is called the arm equation. It is this equation that maps mobile tool coordinates into fixed base coordinates.

Now in order to specify the position and orientation of the mobile tool in terms of the fixed base coordinate frame coordinate transformations involving both rotations and translations are required.

Four axis SCARA Robot

The SCARA acronym stands for Selective Compliance Assembly Robot Arm or Selective Compliance Articulated Robot Arm.In 1981, Sankyo Seiki, Pentel and NEC presented a completely new concept for assembly robots. The robot was developed under the guidance of Hiroshi Makino, a professor at the University of Yamanashi. The robot was called Selective Compliance Assembly Robot Arm, SCARA. Its arm was rigid in the Z-axis and pliable in the XY-axes, which allowed it to adapt to holes in the XY-axes.

By virtue of the SCARAs parallel-axis joint layout, the arm is slightly compliant in the X-Y direction but rigid in the ‘Z’ direction, hence the term: Selective Compliant. This is advantageous for many types of assembly operations, i.e., inserting a round pin in a round hole without binding.

The second attribute of the SCARA is the jointed two-link arm layout similar to our human arms, hence the often-used term, Articulated. This feature allows the arm to extend into confined areas and then retract or “fold up” out of the way. This is advantageous for transferring parts from one cell to another or for loading/ unloading process stations that are enclosed.

SCARAs are generally faster and cleaner than comparable Cartesian robot systems. Their single pedestal mount requires a small footprint and provides an easy, unhindered form of mounting. On the other hand, SCARAs can be more expensive than comparable Cartesian systems and the controlling software requires inverse kinematics for linear interpolated moves. This software typically comes with the SCARA though and is usually transparent to the end-user.

Inverse Kinematics

Inverse kinematics refers to the use of the kinematics equations of a robot to determine the joint parameters that provide a desired position of the end-effector. Specification of the movement of a robot so that its end-effector achieves a desired task is known as motion planning. Inverse kinematics transforms the motion plan into joint actuator trajectories for the robot.

The movement of a kinematic chain whether it is a robot or an animated character is modeled by the kinematics equations of the chain. These equations define the configuration of the chain in terms of its joint parameters. Forward kinematics uses the joint parameters to compute the configuration of the chain, and inverse kinematics reverses this calculation to determine the joint parameters that achieves a desired configuration.
For example, inverse kinematics formulas allow calculation of the joint parameters that position a robot arm to pick up a part. Similar formulas determine the positions of the skeleton of an animated character that is to move in a particular way.

Kinematic analysis

Kinematic analysis is one of the first steps in the design of most industrial robots. Kinematic analysis allows the designer to obtain information on the position of each component within the mechanical system. This information is necessary for subsequent dynamic analysis along with control paths.

Inverse kinematics is an example of the kinematic analysis of a constrained system of rigid bodies, or kinematic chain. The kinematic equations of a robot can be used to define the loop equations of a complex articulated system. These loop equations are non-linear constraints on the configuration parameters of the system. The independent parameters in these equations are known as the degrees of freedom of the system.

While analytical solutions to the inverse kinematics problem exist for a wide range of kinematic chains, computer modeling and animation tools often use Newtons method to solve the non-linear kinematics equations.
Other applications of inverse kinematic algorithms include interactive manipulation, animation control and collision avoidance.

Approximating solutions to IK systems

There are many methods of modelling and solving inverse kinematics problems. The most flexible of these methods typically rely on iterative optimization to seek out an approximate solution, due to the difficulty of inverting the forward kinematics equation and the possibility of an empty solution space. The core idea behind several of these methods is to model the forward kinematics equation using a Taylor series expansion, which can be simpler to invert and solve than the original system.

Five axis Articulated robot

Industrial robots are essential components of today’s factory and even more of the factory of the future . Most of the industrial robots are precise automation devices that can repeat a desired task any number of times without loss of precision. The robots must be flexible enough to reach the required workspace but at the same time should be easy to control. As we increase the reach and flexibility of a robotic arm the complexity of control increases. When a requirement can be achieved using a  particular number of links, increasing the link number will lead to redundancy and also make planning and control difficult. Most of the industrial robots have 4/5 degrees of freedom (4/5 axis). The first 3 axes help the robot to reach a particular point in 3D space and the rest of the axes help in the orientation of the end effector. The axes used for orientation are concurrent so that there is only rotational transformation using these axes.
Many industrial robots designed for jobs like wire EDM, polishing, grinding, arc welding, etc, have only four axis or five axis. This is due to the tools or objects for such jobs are symmetrical. The tool can be treated as line segment as the rotation of the tool about its own axis does not change the attitude of the tool. A four axis robot can guide a line in 3-dimension; A robot with five-axis can guide a line segment for a specified pose. This paper will be discussing about a 5 axis manipulator following a continuous path motion. CAD modeling the proposed 5-axis articulated robot configuration is developed and Denavit–Hartenberg (DH) parameters are estimated. Forward and inverse kinematics problems of this manipulator are formulated. In this work, a trajectory  planning approach is devised to generate the trajectory of the end effector maintaining the constant velocity of the welding torch to maintain the weld quality. Finally, the trajectory planning is simulated through Simulink, MatLab.

Workspace Analysis and Trajectory Planning

Work envelope of 4-axis SCARA Robot

The term SCARA is an acronym that stands for Selective Compliant Assembly Robot Arm. The SCARA robot is based on a 4-axis design. It is ideal for high-speed assembly, kitting, packaging, and other material-handling applications.
Adepts high speed SCARA robot (4-axis robot) innovations have lead the industry for over twenty-five years. In 1984, Adept introduced the AdeptOne SCARA robot, the worlds first direct-drive robot. The AdeptOne SCARA robot changed the face of robotics with its innovative, cutting-edge design.

Adepts high speed SCARA robot innovations continue today with the i-series and s-series Cobra SCARA robots. The Cobra i600 SCARA robot and the Cobra i800 SCARA robot (4-axis robots) have all electronics and motion controls embedded in the robot arm. The Cobra s600 SCARA robot and Cobra s800 SCARA robot take advantage of the extended application capabilities offered by the external SmartController CX for sophisticated motion control and vision guidance. And, the Cobra s800 inverted SCARA robot provides the same high speed SCARA robot work envelope and payload in an inverted (ceiling mount) configuration.

Adepts smallest SCARA robot offering, the Cobra s350 SCARA robot, provides Cobra SCARA robot (4-axis robot) high speed and performance in a smaller motion envelope.

Work envelope of 5-axis articulated Robot

The need for automation and robotics grows stronger every day as labor costs rise and competition from low-wage overseas locations intensifies. At the same time, todays product lifecycles are becoming shorter and the demand for customization and subsequent part complexity grows greater. Many products (not to mention their components) are becoming smaller and tolerances tighter. Flexible, controlled automation often is the only way to guarantee production efficiency and high quality.

As a result of the advancements of automation in general and robotics in particular, the assembly process is faster, more efficient, and more precise than ever before. Fortunately, as each new generation of robot technology is introduced, speed and performance improve and costs decrease. Automation and robotics, therefore, become more affordable and valuable to the manufacturer.

Conventional wisdom of the past steered some assemblers to the Cartesian coordinate robot, which consists of an orthogonal-axis structure.

"Two- and three-axis non-servo Cartesian devices have a lot of components like cylinders, solenoids, tubing, and switches," explains James C. Cooper, distribution network account manager for FANUC Robotics America Inc. "This complexity leads to several points of potential failure, but that can all be replaced by one of our robots, at a significantly higher mean time between failure, which is now 78,000 hours for our robots."

The current most popular robotics solution for assembly is the four-axis SCARA robot arm, which can move to any X-Y-Z coordinate within its work envelope. There is a fourth axis of motion, which is the wrist rotation. The X, Y, and roll movements are obtained with three parallel-axis rotary joints. The vertical Z motion is usually an independent linear axis at the wrist or in the base. SCARA robots are typically used in 2-D assembly operations where the final move to insert the part is a single vertical motion. Component insertion into printed circuit boards is an example. SCARAs are very common in pick-and-place, assembly, and packaging applications.

"You could do a specific pick and place with a pneumatic mechanism, but if you get into an application where you needed to possibly shift the part or rotate the part, then youre adding two or more mechanisms together," says Mike English, president of the Warwick, RI-based integrator Interplex Automation. "With how robot prices have come down so much over the years, I could design and build a pick-and-place unit and by the time I put all the slides together and make or buy adaptor plates, put sensors on the mechanism, assemble it, wire it, plumb it, and add up my raw material cost and my labor cost, its about the same as what a robots going to cost. With a robot, you just bolt it to a table, program it, and its ready to go."

The advantages of a vertically articulated robot compared to a SCARA are its flexibility and dexterity.

"Sometimes workpieces come into the robot cell at an angle and with SCARAs, something has to be done to make the part flat. Thats additional cost and additional hardware," Cooper explains. "With the dexterity of the vertically articulated robot, you can use the robot to pick up and re-orient the part."
FANUC six-axis robots allow for parts to be manipulated gently and accurately.

Five- and six-axis articulating robots also are more adaptable to variations or changes during a project, and offer more flexibility during and after a program.
"The robot is programmable where the pick-and-place unit isnt," English says. "There are always changes in design and process."

English describes a robot he first installed for assembly, after which it was asked to help perform an in-process inspection. "The robot allows me to stop along the way, vision-inspect the part, and place it either in the reject bin or where it needs to go next in the process," he says. "Thats an easy change. I dont need to build a whole lot of other stuff. The robot is the material management unit."

Trajectory Planning

Mark Senti, vice president of Advanced Magnet Lab Inc., an integrator based in Melbourne, FL, cites an example where an odd-shaped circuit board is inserted into a tight-fitting key fob. Both components were on the far edge of the specification and to insert the circuit board, a six-axis FANUC robot was programmed to first tilt and then roll it into place.

"A four-axis solution would make it almost impossible to insert that vertically," Senti says. "A six-axis robot allows you to manipulate the part very gently and very accurately. With hard automation, youre stuck if a change is made after installation. Robotics allows you to easily and quickly adapt to these changes."

Despite the advancements in robotics, integrators still specify Cartesian and SCARA systems because of their history of and reputation for high-speed, high-precision, standard control platforms, and – until recently – lower cost compared to five- or six-axis articulating robots.

The pick-and-place operation

SMT (surface mount technology) component placement systems, commonly called pick-and-place machines or P&Ps, are robotic machines which are used to place surface-mount devices (SMDs) onto a printed circuit board (PCB). They are used for high speed, high precision placing of broad range of electronic components, like capacitors, resistors, integrated circuits onto the PCBs which are in turn used in computers, consumer electronics as well as industrial, medical, automotive, military and telecommunications equipment.

The placement equipment is part of a larger overall machine that carries out specific programmed steps to create a PCB Assembly. Several sub-systems work together to pick up and correctly place the components onto the PCB. These systems normally use pneumatic suction cups, attached to a plotter-like device to allow the cup to be accurately manipulated in three dimensions. Additionally, each nozzle can be rotated independently.

Basic Concepts of Artificial Intelligence

Artificial intelligence (AI) is the intelligence exhibited by machines or software. It is also an academic field of study. Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines".

AI research is highly technical and specialised, and is deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. Some subfields focus on the solution of specific problems. Others focus on one of several possible approaches or on the use of a particular tool or towards the accomplishment of particular applications.

The central problems (or goals) of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception and the ability to move and manipulate objects. General intelligence (or "strong AI") is still among the fields long term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are a large number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others. The AI field is interdisciplinary, in which a number of sciences and professions converge, including computer science, psychology, linguistics, philosophy and neuroscience, as well as other specialized field such as artificial psychology.

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical deductions. By the late 1980s and 1990s, AI research had also developed highly successful methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.

For difficult problems, most of these algorithms can require enormous computational resources – most experience a "combinatorial explosion": the amount of memory or computer time required becomes astronomical when the problem goes beyond a certain size. The search for more efficient problem-solving algorithms is a high priority for AI research.

Human beings solve most of their problems using fast, intuitive judgements rather than the conscious, step-by-step deduction that early AI research was able to model. AI has made some progress at imitating this kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the structures inside the brain that give rise to this skill; statistical approaches to AI mimic the probabilistic nature of the human ability to guess.

Application

Artificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is described as the AI effect. An area that artificial intelligence has contributed greatly to is Intrusion detection.

There are a number of competitions and prizes to promote research in artificial intelligence. The main areas promoted are: general machine intelligence, conversational behavior, data-mining, robotic cars, robot soccer and games.

Platforms

A platform (or "computing platform") is defined as "some sort of hardware architecture or software framework (including application frameworks), that allows software to run." As Rodney Brooks pointed out many years ago, it is not just the artificial intelligence software that defines the AI features of the platform, but rather the actual platform itself that affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms rather than in isolation.

A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert systems, albeit PC-based but still an entire real-world system, to various robot platforms such as the widely available Roomba with open interface.
The field was founded on the claim that a central property of humans, intelligence—the sapience of Homo sapiens—"can be so precisely described that a machine can be made to simulate it." This raises philosophical issues about the nature of the mind and the ethics of creating artificial beings endowed with human-like intelligence, issues which have been addressed by myth, fiction and philosophy since antiquity. Artificial intelligence has been the subject of tremendous optimism but has also suffered stunning setbacks. Today it has become an essential part of the technology industry, providing the heavy lifting for many of the most challenging problems in computer science.

Elements of Knowledge Representation

Knowledge representation and reasoning (KR) is the field of artificial intelligence (AI) devoted to representing information about the world in a form that a computer system can utilize to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.

Examples of knowledge representation formalisms include semantic nets, Frames, Rules, and ontologies. Examples of automated reasoning engines include inference engines, theorem provers, and classifiers.
A classic example of how setting an appropriate formalism leads to new solutions is the early example of the adoption of Arabic over Roman numerals. Arabic numerals facilitate larger and more complex algebraic representations, thus influencing future knowledge representation.

Knowledge representation incorporates theories from psychology which look to understand how humans solve problems and represent knowledge. Early psychology researchers did not believe in a semantic basis for truth. For example, the psychological school of radical behaviorism which dominated US universities from the 1950s to the 1980s explicitly ruled out internal states as legitimate areas for scientific study or as legitimate causal contributors to human behavior. Later theories on semantics support a language-based construction of meaning.

Knowledge-representation is the field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems. The justification for knowledge representation is that conventional procedural code is not the best formalism to use to solve complex problems. Knowledge representation makes complex software easier to define and maintain than procedural code and can be used in expert systems.
For example, talking to experts in terms of business rules rather than code lessens the semantic gap between users and developers and makes development of complex systems more practical.

Knowledge representation goes hand in hand with automated reasoning because one of the main purposes of explicitly representing knowledge is to be able to reason about that knowledge, to make inferences, assert new knowledge, etc. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system.

A key trade-off in the design of a knowledge representation formalism is that between expressivity and practicality. The ultimate knowledge representation formalism in terms of expressive power and compactness is First Order Logic (FOL). There is no more powerful formalism than that used by mathematicians to define general propositions about the world. However, FOL has two drawbacks as a knowledge representation formalism: ease of use and practicality of implementation. First order logic can be intimidating even for many software developers. Languages which do not have the complete formal power of FOL can still provide close to the same expressive power with a user interface that is more practical for the average developer to understand. The issue of practicality of implementation is that FOL in some ways is too expressive. With FOL it is possible to create statements (e.g. quantification over infinite sets) that would cause a system to never terminate if it attempted to verify them. Thus, a subset of FOL can be both easier to use and more practical to implement.

Task Planning

As machines become smarter, the programming becomes more sophisticated.No machine has yet been built that has intelligence anywhere near that of a human being. Some researchers think that true artificial intelligence (AI), at a level near that of the human brain, will never be achieved.

The programming of robots can be divided into levels, starting with the least sophisticated and progressing to the theoretical level of true AI. The drawing shows a four-level scheme. Level 3, just below AI, is called tasklevel programming. As the name implies, programs at this level encompass whole tasks, such as cooking meals, mowing a lawn, or cleaning a house.

Task-level programming lies just above the hierarchy from complexmotion planning, but below the level of sophistication generally considered to be AI.

Fine motion planning

Motion planning (also known as the "navigation problem" or the "piano movers problem") is a term used in robotics for the process of breaking down a desired movement task into discrete motions that satisfy movement constraints and possibly optimize some aspect of the movement.

For example, consider navigating a mobile robot inside a building to a distant waypoint. It should execute this task while avoiding walls and not falling down stairs. A motion planning algorithm would take a description of these tasks as input, and produce the speed and turning commands sent to the robots wheels. Motion planning algorithms might address robots with a larger number of joints (e.g., industrial manipulators), more complex tasks (e.g. manipulation of objects), different constraints (e.g., a car that can only drive forward), and uncertainty (e.g. imperfect models of the environment or robot).

Motion planning has several robotics applications, such as autonomy, automation, and robot design in CAD software, as well as applications in other fields, such as animating digital characters, video game artificial intelligence, architectural design, robotic surgery, and the study of biological molecules.