The robot’s dynamic model is required in the implementation of most advanced model-based control schemes. The dynamic model is crucial because it can be used to linierize the non-linier system in both joint space and task space. Since the robot’s dynamic parameters are normally not available for industrial manipulators, proper procedures should be carried out to identify these parameters.
One way to identify the dynamic parameter is to dismantle the robot and measure link by link. However, it is obvious that his approach is not always feasible in practice. Another problem, with dismantling approach is that it does not account for the effects of joint friction.
In order to account for joint friction, several methods were proposed. These methods can be roughly divided into two groups: to identify joint friction and rigid body dynamic separately or to identify joint friction and rigid body dynamics simultaneously. The former first identify the rigid body dynamic parameters using the identified friction parameters. Since friction parameters are identified joint by joint, non-linier dynamic friction models such as Stribeck and/or hysteresis effects can be considered.
The main drawback of this method comes from the fact that friction can be much time-varying. Moreover, friction forces/torques are always coupled to the inertial forces/torques, thus, one can not be precisely identified without the other.
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3D Vision-Based Control on an Industrial Robot
Industrial robots are designed for tasks such as pick and place, welding, and painting. The environment and the working conditions for those tasks are well set. If the working conditions changed, those robots may not be able to work properly. Therefore, external sensors are necessary to enhance the robot’s capability to work in a dynamic environment. A vision sensor is an important sensor that can be used to extend the robot’s capabilities. The image of objects of interest can be extracted from their environment, and then information from these images can be computed to control the robot. The control that uses the images as feed back signals is known as vision based control. Recently, vision-based control has become a major research field in robotics.
Vision-based control can be classified into two main categories. The first approach, feature based visual control, uses image features of a target object from image (sensor) space to compute error signals directly. The error signals are then used to compute the required actuation signals for the robot. The control law is also expressed in the image space. Many researchers in this approach use a mapping function (Jacobian) from the image space to the Cartesian space.
The image Jacobian, generally, is a function of the focal length of the lens of the camera, depth, and the image features.
Vision-based control can be classified into two main categories. The first approach, feature based visual control, uses image features of a target object from image (sensor) space to compute error signals directly. The error signals are then used to compute the required actuation signals for the robot. The control law is also expressed in the image space. Many researchers in this approach use a mapping function (Jacobian) from the image space to the Cartesian space.
The image Jacobian, generally, is a function of the focal length of the lens of the camera, depth, and the image features.
Overview the Robotic Future
• The industry faces many of the same challenges that the personal computer business faced 30 years ago. Because of a lack of common standards and platforms, designers usually have to start from scratch when building their machines.
• Another challenge is enabling robots to quickly sense and react to their environments. Recent decreases in the cost of processing power and sensors are following researchers to tackle these problems.
• Robot builders can also take advantage of new software tools that make it easier to write programs that work with different kinds of hardware. Networks of wireless robots can tap into the power of desktop PCs to handle tasks such as visual recognition and navigation.
The word ROBOT was popularized in 1921 by Czech playwright Karel Capek, but people have envisioned creating robot like devices for thousands of years. In Greek and Roman mythology, the gods of metalwork built mechanical servants made from gold. In the first century A.D, Heron of Alexandria, the great engineer credited with inventing the first steam engine.
Over the past century, anthropomorphic machines have become familiar figures in popular culture through books such as Isaac Asimov I, Robot, movies such as Star Wars and television shows such as Star Trek. The popularity the robots in fiction indicates that people are receptive to the idea that these machines will one day walk among us as helpers and even as companions.
• Another challenge is enabling robots to quickly sense and react to their environments. Recent decreases in the cost of processing power and sensors are following researchers to tackle these problems.
• Robot builders can also take advantage of new software tools that make it easier to write programs that work with different kinds of hardware. Networks of wireless robots can tap into the power of desktop PCs to handle tasks such as visual recognition and navigation.
The word ROBOT was popularized in 1921 by Czech playwright Karel Capek, but people have envisioned creating robot like devices for thousands of years. In Greek and Roman mythology, the gods of metalwork built mechanical servants made from gold. In the first century A.D, Heron of Alexandria, the great engineer credited with inventing the first steam engine.
Over the past century, anthropomorphic machines have become familiar figures in popular culture through books such as Isaac Asimov I, Robot, movies such as Star Wars and television shows such as Star Trek. The popularity the robots in fiction indicates that people are receptive to the idea that these machines will one day walk among us as helpers and even as companions.
Trends in Intelligent Robotic Industry Development
According to the definition of the International Federation of Robotics (IFR), robotics can be classified into two categories: industrial robotics and service robotics. Survey reports from Japan Robot Association (JARA) reveal that as industrial robotics are only used in precision manufacturing and the industrial robotics market is becoming gradually saturated in the mid+ and long term, there is limited room for future growth.
In contrast, there is great potential for family/personal service robotics. As the populations of most developed countries are aging and birth rates are low, there is an increasing demand for home care for the elderly, child education and entertainment.
In the beginning, at a time when there was almost no demand for applications of any kind, prototype intelligent service robots were mostly research models developed by academic research institutions. In recent years, Japanese auto makers, especially Honda and Toyota, have expanded the scale of service robotics R&D and endeavored to achieve market objectives for product commercialization; they have also developed and been utilizing their respective ASIMO and tour guide robots.
The United States puts more emphasis on AI and control technology R&D. US manufacturers and academic institutions have all been endeavoring to develop AI robots, launching products such as Davensi robotics system for surgical operations, tour guide robots, and the Roomba. The industry trend shows that the US is manufacturing robotic components for diversified uses, and is involves in the establishment of the robotics value chain.
In contrast, there is great potential for family/personal service robotics. As the populations of most developed countries are aging and birth rates are low, there is an increasing demand for home care for the elderly, child education and entertainment.
In the beginning, at a time when there was almost no demand for applications of any kind, prototype intelligent service robots were mostly research models developed by academic research institutions. In recent years, Japanese auto makers, especially Honda and Toyota, have expanded the scale of service robotics R&D and endeavored to achieve market objectives for product commercialization; they have also developed and been utilizing their respective ASIMO and tour guide robots.
The United States puts more emphasis on AI and control technology R&D. US manufacturers and academic institutions have all been endeavoring to develop AI robots, launching products such as Davensi robotics system for surgical operations, tour guide robots, and the Roomba. The industry trend shows that the US is manufacturing robotic components for diversified uses, and is involves in the establishment of the robotics value chain.
Industrial Robots to Replace Human Workers
According to the Robotic Industries Association, an industrial robot is an automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes which may be either fixed in place or mobile for use in industrial automation applications. The first industrial robot, manufactured by Unimate, was installed by General Motors in 1961. Thus industrial robots have been around for over four decades.
According to the International Federation of Robotics, another professional organization, a service robot is a robot which operates semi or fully autonomously to perform services useful to the well being of humans and equipment, excluding manufacturing operations.
Many industrial automation tasks like assembly tasks are repetitive and tasks like painting are dirty. Robots can sometimes easily perform these tasks. Human workers often don’t require intelligence or exercise any decision making skills. Many of these dumb tasks like vacuum cleaning or loading packages onto pallets can be executed perfectly by robots with a precision and reliability that humans may lack.
As our population ages and the number of wage earners becomes a smaller fraction of our population, it is clear that robots have to fill the void in society. Industrial, and to a greater extent, service robots have the potential to fill this void in the coming years.
A second reason for the deployment of industrial robots is the trend toward small product volumes and an increase in product variety. As the volume of products being produced decreases, hard automation becomes a more expensive proposition, and robotics is the only alternative to manual production.
According to the International Federation of Robotics, another professional organization, a service robot is a robot which operates semi or fully autonomously to perform services useful to the well being of humans and equipment, excluding manufacturing operations.
Many industrial automation tasks like assembly tasks are repetitive and tasks like painting are dirty. Robots can sometimes easily perform these tasks. Human workers often don’t require intelligence or exercise any decision making skills. Many of these dumb tasks like vacuum cleaning or loading packages onto pallets can be executed perfectly by robots with a precision and reliability that humans may lack.
As our population ages and the number of wage earners becomes a smaller fraction of our population, it is clear that robots have to fill the void in society. Industrial, and to a greater extent, service robots have the potential to fill this void in the coming years.
A second reason for the deployment of industrial robots is the trend toward small product volumes and an increase in product variety. As the volume of products being produced decreases, hard automation becomes a more expensive proposition, and robotics is the only alternative to manual production.
State of the Art in Theory and Practice of Industrial Robots
Today industrial robots present a mature technology. They are capable of lifting hundreds of pounds of payload and positioning the weight with accuracy to a fraction of a millimeter. Sophisticated control algorithms are used to perform positioning tasks exceptionally well in structure environments.
FANUC, the leading manufacturer of industrial robots, has an impressive array of industrial robot products ranging from computerized numerical control (CNC) machines with 1 nm Cartesian resolution and 10-5 degrees angular resolution to robots with 450 kg payloads and 0.5 mm repeatability. Some of their robots include such features as collision detection, compliance control, and payload inertia/weight identification. The control software supports networking and continuous coordinated control of two arms. Force feedback is sometimes used for assembly tasks.
The nature robotic workcell has changed since early days of robotics. Instead of having a single robot synchronized with material handling equipment like conveyors, robots now work together in a cooperative fashion eliminating mechanized transfer devices. Human workers can be seen in closer proximity to robots and human-robots cooperation is closer to becoming a reality.
However, industrial robots still do not have the sensing, control and decision making capability that is required to operate in unstructured, 3D environments. Cost effective, reliable force sensing for assembly still remains a challenge. Finally we still lack the fundamental theory and algorithms for manipulation in unstructured environments, and industrial robots currently lack dexterity in their end-effectors and hands.
FANUC, the leading manufacturer of industrial robots, has an impressive array of industrial robot products ranging from computerized numerical control (CNC) machines with 1 nm Cartesian resolution and 10-5 degrees angular resolution to robots with 450 kg payloads and 0.5 mm repeatability. Some of their robots include such features as collision detection, compliance control, and payload inertia/weight identification. The control software supports networking and continuous coordinated control of two arms. Force feedback is sometimes used for assembly tasks.
The nature robotic workcell has changed since early days of robotics. Instead of having a single robot synchronized with material handling equipment like conveyors, robots now work together in a cooperative fashion eliminating mechanized transfer devices. Human workers can be seen in closer proximity to robots and human-robots cooperation is closer to becoming a reality.
However, industrial robots still do not have the sensing, control and decision making capability that is required to operate in unstructured, 3D environments. Cost effective, reliable force sensing for assembly still remains a challenge. Finally we still lack the fundamental theory and algorithms for manipulation in unstructured environments, and industrial robots currently lack dexterity in their end-effectors and hands.
Robots in Lean Manufacturing
To understand the impact of robots on lean manufacturing, we need to gain a good understanding of the term – Lean Manufacturing. Lean manufacturing is a management philosophy focusing on reduction of the seven manufacturing related wastes as defined originally by Toyota. The wastes are:
• Overproduction (production ahead of demand).
• Transportation (moving product that is not actually required to perform the processing).
• Waiting (waiting for the next production step).
• Inventory (all components, work in progress and finished product not being processed).
• Motion (people or equipment moving or walking more than is required to perform the processing).
• Over processing (due to poor tool or product design creating activity).
• Defects (the efforts involved in inspecting for and fixing defects).
There has been a steady increase in the role of industrial robots in manufacturing. With over 15,000 industrial robots sold every year, robots have become a mainstay in the manufacturing industry. Traditionally, robots have not always been viewed to have a role in the implementation of lean strategies. However, due to their flexibility, reliability and repeatability, to name a few advantages, the role of robots in constantly increasing.
Robots have been an off the shelf purchase item for the last two decades. The cost of common robot models from major manufacturers has plummeted due to large volume sales to automotive OEM’s and to the financially negative impact of competition.
• Overproduction (production ahead of demand).
• Transportation (moving product that is not actually required to perform the processing).
• Waiting (waiting for the next production step).
• Inventory (all components, work in progress and finished product not being processed).
• Motion (people or equipment moving or walking more than is required to perform the processing).
• Over processing (due to poor tool or product design creating activity).
• Defects (the efforts involved in inspecting for and fixing defects).
There has been a steady increase in the role of industrial robots in manufacturing. With over 15,000 industrial robots sold every year, robots have become a mainstay in the manufacturing industry. Traditionally, robots have not always been viewed to have a role in the implementation of lean strategies. However, due to their flexibility, reliability and repeatability, to name a few advantages, the role of robots in constantly increasing.
Robots have been an off the shelf purchase item for the last two decades. The cost of common robot models from major manufacturers has plummeted due to large volume sales to automotive OEM’s and to the financially negative impact of competition.
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