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A few contributions to robotics

2.1. Shearing Sheep

Accidentally, I found robotics.

In 1976, I was stunned to see how much Australian wool producers were willing to spend on equipment to regularly remove sheep. A nearly 100% increase in the price of shearing within 18 months almost drove the USindustry to despair. Two separate research teams were formed to develop rival prototypes.

David Henshaw was a scientist who worked for a government textile research laboratory in 1974. He discovered that the location and conductivity of the electric current between the steel shearing handler and the sheep’s mouth could be controlled. Using a primitive device (Fig. 1) He was able to make a single “blow” across the sheep’s backbones while he was attached to a cart with wheels. Normal Lewis, an engineer-turned-woolgrower, had devised a me-chanical solution: two thin sensing wheels could measure the profile of the sheep ahead of a cutter which followed the path they traced out. A private company was contracted by the British government to create a set of prototypes. buy cso database.

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When I was able to see each one, January 27, 1977, it struck me that none of the groups had ever considered computer control. Both used hardwired electronic controllers. Both operated with mechanical sensors. However, the electronic sense of Henshaw outpowered the mechanical wheels. Computer control was more flexible and would allow for the adaptive control required to operate at high speeds.

It was amazing to see how much of my knowledge in mechanical engineering, machine designing, geometry, computing, and nav-igation could be incorporated into one project. My coworker and me created an arm that hydraulically articulates on seven axes. It has a three-axis virtual mechanism that centers the wrist (Fig. 2.) 2.)

We began shearing wool from sheep in July 1979, 20 months after we started the ma-chine. We quickly outperformed the previous machines. Software sheep was an important aspect of the control system. It was a mathematical representation that showed the expected form of the sheep’s body without wool.

After we began to get attention from media outlets, some people suggested that we had built the “robot shearer”. However, I hadn’t realized that we had actually constructed a robot. Although we had initially expressed interest in the Unimate, the control and mechanical systems were not designed for shearing. We created a ma-chine that can shear sheep. However, it was not intended to be a general-purpose manipulator. We didn’t make this distinction clear when we presented our first film at the international robotics convention (Trevelyan Key, Owens 1982). When they saw an animal being lifted onto a cradle and then secured, the crowd laughed and chuckled. Our large and somewhat awkward handler quickly and carefully shredded the wool in a perfectly straight line, the sheep was stunned. We started to work on a robot that could shear sheep.

The robot’s shearing abilities were demonstrated and the wool industry was keen to see the sheep’s handling be automated. Our first robot was intended to perform shearing tasks. Its usable space was much smaller than its size suggests. It was decided that a new robot design was needed first. We didn’t follow the advice and were left with the task of designing a complex machine that could move the sheep au-tomatically through approximately 15 different positions. These positions stretch the necks and legs as well as hold the sheep for several hours. Our team grew from 5 to 18 people.

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An engineering team designed the first sheep-manipulator model called ARAMP in 1983. It had 43 movements that could all be actuated, and most of them could be controlled with a single button.

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could collide with one another (see possible collisions (see. 3). David Elford was the head of the mechanical design team. He had designed a large part of the top-speed machinery used for peeling, coring and processing fruits worldwide in the 1960s and 70s.

After a string of impressive demonstrations, a new shearing device was developed with huge space. We then had to deal with singularity issues, which ORACLE, our first robot, cleverly avoided. Analytical techniques were developed to assist robots in avoiding singularities. These techniques were not able to adjust the sheep-shearing trajectory on the fly unless there were vast zones around singularities that could be avoided. We were able to create a wrist-operated mech-anism that was robust and without singularities (Trevelyan, others). 1986), which removed many of the otherwise-challenging design constraints on the new Shear Magic (SM) robot and its control software.

The SM robot impressed woolgrowers by its similar appearance (see Figure). 4). This was no mistake. We concealed the complexity of the exteriors that ORACLE/ARAMP could not play with by integrating all hydraulic piping and electronic parts within the structure. It could have been fatal in the real world. However, it was crucial during the initial stages of development.

The fourth and final phase of development was designed to simplify the handling of sheep. However, it was not acceptable to hide the complexity. It was important to consider loading sheep that was not considered when developing the ARAMP. To reduce shear, the head had to be controlled precisely.

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Around the ears and eyes. Most robotics research stops involve three or more simultaneous arm manipulations. A sheep is able to walk on four legs, and its head has flexibility in its neck and body. The five “extremities” had to be kept under tight mechanical control, while simultaneously rotating the sheep. It was not an easy task.

We worked on several solutions that we knew were impossible to implement over the course of 18 month. Fortunately, we discovered another Simplicity in this abyss of faith and christened the invention Simplified Loading and Manipulation Platform. Also known as SLAMP (Trevelyan and Elford 1988). We demonstrated automated loading and shearing of whole sheep on February 29, 1989, more 10 years after the original robot shearing demonstration. The entire process took about 25 minutes. While our research was focused on reliability and time, only a small percentage of the results were implemented into fully functional demonstrations.

One of the most significant modifications was to remove the dependence on measuring conductivity of the skin and combs. This is basically equivalent to the control of force issue that robotics scientists are familiar with. The force applied to the skin directly affected conductivity, but results were not always consistent and many other factors had to be taken into account. CSO Email Leads

Although we came up with a completely different method, there was no need to make any major mechanical adjustments. The hydraulic pressurized system was used to apply a force to the shearing-comb, subject to friction uncertainty. This then allowed us to determine the displacement of our re-sultingcomb. These results were shocking. The previous maximum speed for shearing was approximately 10 cm/sec. This was lower for many sheep areas. Our new method allowed us to use the comb to smoothen the skin. We were able to do this at 80 cm per second.

The cutter did not have enough power to shred the wool at this speed and the hydraulic supply of the machine was not sufficient to keep up with it. We were able to lower the frequency of the robotic arm’s operation from 25 Hz for SM to 5 Hz. This resulted in significant savings for future shearing robots.

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Machine-vision was another important field. Computer vision was not able to resolve the problems of accuracy in sheep im-ages measurement. This was crucial in the prediction of a precise “software sheep”, for each animal. Our own “snakes”, which are adaptive contour models (Kass Witkin and Terzopoulos 1988), were created. They were so precise that it was impossible for us to predict the frequency of failure (see figure). 5).

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Many people thought it was intelligent. Our research was presented at the 1988 IJCAI Sydney exhibition. Trevelyan 1989. We showed how to create smooth, fluid movements in human shearers by using an elegant method to handle interruptions that require wool to be moved away or to avoid skin cuts. Participants were confused to learn that we did not use a specialist system nor artificial neural networks. We wrote all of the software in Fortran 77.

In 1990, the major wool-buying countries pulled out of the market and caused an economic crisis. Russia and Eastern Europe both ran out of credit, while China was denied credit after the events in Beijing in July 1989. Both demand and prices plummeted. Other long-term research programs were also severely curtailed. After a large research study on the financial viability of shearing machines, our research was completed in 1993. Due to the current state of wool production, attempts to form a joint venture commercially with merchant banks and wool sector failed. The investment needed, approximately US$35million, was small for such a large industry. However, woolgrowers were reluctant or unable invest in anything but their immediate survival.

As an experiment, shearing sheep robotically has been a great success. It produced two important results. First, we created a method to sharpen shearing combs as well as shearing cutters. This is now taught to shearers since 1990. It was considered a “black practice” and only a few shearers were able to master it. The SLAMP technique has been greatly simplified and refined (see the figure). 6.) It is currently being commercialized to eliminate all the heavy lifting involved in manually shearing (Trevelyan, 1996a). Shearing is not reserved for a select group of physically fit and flexible men who have worked as shearers for at least four to five years. Both men and women can improve their skills over their lifetimes, increasing efficiency in all areas of industry, not just shearing. Shearing robots are considered the best long-term solution for the 21st Century (Elford 1999 personal communication).

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2.2. Robotics is simple

1992 was a pivotal year for me. I decided to focus my efforts on robotics. This is simple, long-term research that uses low-budget technology.

Blocks, without modifications to the internals. Working with robots and computers will allow students to easily share their knowledge with industrial partners. For reliable robots to be built, they need reliable and low-cost building blocks. It was interesting to work in a completely different direction from the rest of the discipline.

2.3. Accuracy and calibration

Giovanni Legnani’s ideas (Legnani Mina and Trevelyan 1996) inspired me to tackle the problem of absolute positioning accuracy. I had noticed that there wasn’t an easy, inexpensive solution to the issue industrial robots have when calibrating. The methods described in the literature were based on expensive sensors, and they are not applicable in industrial cells where fixtures and part mistakes are as important as those of robots. Students helped us design a laser/mirror/lens system with high precision measurements and a recursive filter to estimate the kinematic parameters. 7) (Cleary 1997). This project is far from over. Although the technique of calibration worked well in our laboratory, it is not easy for other labs to use. Students studying research often ask for details about our calibration procedures.

2.4. Vision

An award for research allowed me to continue my research on snakes and computer vision. This was in an effort to improve upon the success of the previous research.

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Methods for sheep-shearing using images of plants (Trevelyan & Murphy 1996). This was because the program relied heavily upon graphical programming executed using a basic GUI toolkit for X-Windows and an Unix workstation. It was difficult to transfer it to a DOS-compatible PC. To run DOS, we created a low-level Windows system (LittleX), which has been a tremendous help to many since it was posted on our website. After identifying many holes in Microsoft documentation, we concluded that Visual Basic is a useful environment to run Windows operating systems. It is now possible to say that Microsoft’s Microsoft operating system implementations are a waste of time and effort. Without this, we would not be able move on to Web telerobotics.

2.5. Web Telerobotics

Ken Taylor, a bright and newly graduated PhD student, challenged me to address a new problem: “Where are all of the robots?” What was the reason that the optimistic predictions made in the 1970s and early 1980s were completely false? There was much speculation at the time that robots and automation would eliminate manual labor from manufacturing by the mid-90s. However, it became clear that this was completely false in 1994.

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Are robots on the horizon? Is there any technological factor that has impeded this progress? Ken realized this was a difficult question to answer so he decided to look at the Internet, which had changed dramatically with the introduction of the World Wide Web. Ken was inspired by a Web-page image of a Cambridge University cafeteria, which was updated each time a user requested it. This Web-page showed that any Web user could control the robot if they could find images similar to it. He mobilized my research team and other students as soon as possible. Our ASEA robot was launched in September. This happened just two weeks after Ken Goldberg’s Telerobot team in the southern region of California launched their two-axis Raiders Telerobot. You could use the robot to move blocks for kids, and there are some incredible designs (see Figure). 8).

Ken Taylor was also inspired to segregate action and thinking by geographical separation. Engineers often find that the number of states an automated system can enter is greater than the ability to program the appropriate responses. This is common in traditional robotics. The ability to access human minds in large numbers is much greater than the need for manual labor. Telerobotics can make use of this centralizing potential with cost-effective Internet technology, but it requires the assistance of a distributed team. CSO Mailing leads

Although Ken and his team had difficulties with unstable operating systems and Web servers and Web browsers, they were able at the end 1995 to collect data on the actions of the Telerobot Web site visitors, which numbered in the thousands, each month. The telerobot initiative produced one of the most surprising outcomes.

The website has a high user base each year. It has been criticised for its “why” feature. We are currently working to address the issues that have resulted.

We believe they have great entertainment potential. Many people comment on their experiences trolling machines in the other part of the globe from their posts. It is possible that telerobotic access could soon be granted to wildlife reserves where human intervention would result in destruction.

In 1996, Barney Dalton joined the team. He has transformed the software into a system that is extremely reliable. Visitors who are short-term contributors have also contributed, such as an interface that is augmented, which gives users a more intuitive visual interface, created by Harald Friz (Dalton, Friz, and Taylor, 1998).

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2.6. Demining

In 1994-1995, many people suggested that I create a robot to remove anti-personnel mining, which was being recognized as a major source of destruction around the world. There were many strategies I considered, including the cable-based one.

The suspended robot (Trevelyan 1996b, 1997) but the team quickly realized that this device would be impossible to use due to the absence of reliable sensors.

Mining clearance, also called humanitarian demining, is a tedious and time-consuming process. Every metal fragment detected by metal detectors must be carefully examined as a potential mine. For every mine that is removed, there may be thousands of metal fragments. If the number of metal fragments found per square meter is greater than 20, the entire surface of the ground must be examined on a scale of centimeters to centimeters. Although dogs can be helpful in some situations, they need to adapt to the local climate. Deminers must also be careful around dense vegetation and strong odors that result from recent human presence or other pollutants.

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Although several hundred million dollars have been spent by military programs and government research to find better sensors, the ultimate goal remains elusive. A “no-mine detector device is required to inform someone who is likely to commit a crime that the ground they plan to walk on is safe. Metal detectors are unable to distinguish between a mine or a metal fragment. Therefore, it is necessary to use other sensors. A variety of sensors have been tested, including ground-penetrating radar microwaves and infrared as well as acoustics.

even water jets. These signals are heavily dependent on metal detector signals, so sensor fusion has not yet produced satisfactory results. The probability of detection in real-world situations was about 90% for the second half 1998. This is 2 to 3 orders of magnitude lower than the 99.6 per cent required for security and certainty (Trevelyan 1998).

However, mechanical clearance has not been as effective. Some machines can achieve 90 percent clearance in tri-als, but it’s difficult to confirm after equipment has scattered metallic pieces to 40 cm below the surface.

Six months of careful study led me to the conclusion that robotics technology was unlikely to produce real-world results in the next ten years. This is a conclusion that has not changed in the three years since. However, the lack of research on all aspects demining was not done.

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There were many opportunities to make practical discoveries in a very short time. The basic equipment used by miners was not protected as Western weapons are heavy and uncomfortable. It was also expensive. The situation was not ideal for simple methods. However, it presented many challenges and obstacles.

Mining operations are often carried out in the “fourth-world”, where conditions are typically more difficult than in the third world. The condition of the country’s political, commercial and social institutions is often in decline. This could be due to civil wars. Landmines are a low-cost tool that can harass and terrorize people. This is why it is a feat in itself to make an organization work efficiently. Because local people don’t have the same educational opportunities as those in most third world countries, adapting to new technologies is a difficult task.

UNOCHA 1998 identifies Afghanistan’s demining program as one of the best in the world. With the help of family connections, we created a Pakistani re-search team. It was staffed with local engineers and technical staff that work closely with Afghan deminers. We can access their ideas and suggestions without cultural barriers, such as “we must please the Westerner who’s the expert.” We created concepts to protect ourselves using protective clothing, improved digging tools, and blast shields. These were also modified and refined by deminers working in local conditions (see Figure). 9) (Trevelyan 1999). Deminers are using some of them in the field.

3.2. Intelligence

Although most of the behavior we consider intelligent and conscientious can be automated in the beginning, we are unable to automate the actions we perform without thinking about them. The things we do that we don’t think about and assume we know how to duplicate them, we cannot comprehend the consequences.

3.3. Simplicity

In engineering, simplicity is key to success. Robots must not only look easy but also have an inherent simplicity to make them useful.
Force Control

It is much easier and more efficient for effective control of force to be achieved through regulation of the generator, rather than regulating the positions and relying upon erratic and sometimes unclear surface interactions to generate desired force of contact.

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3.1. Technology’s rate of change

It is false that technology is evolving at an “ever-increasing rate of technological development”. However, technology is not evolving at the same pace as 100 years ago when electricity was first introduced to the West. This happened in just three or four years. My eldest son com-

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3.5. The most recent developments in the field

Robotics’ most innovative innovations come from applications that require a precise level of performance to be successful. This is a convincing argument. This venture is not something we should have considered.

If we had been working in robotics research before we started, the research might have been more successful. Otherwise, we could dismiss the project as not up to the standard of the technology. While we were doing our first shearing test, I noticed that roboticists were using computers many times larger than ours in order to control the manipulator’s arms. This task required skin sensing, compliant force control surface modeling, adaption, and on-line trajectory modification monitoring faults as well safety interlocks. Our computer was able to handle it with just 1%. We needed to develop new methods for controlling manipulators that were not widely accepted in robotics. These methods were born out of a need for performance. We created the control system and robot system to fulfill these requirements, rather than altering existing research in robotics. CSO Email Database Lists

3.6. Technology Transfer

Robotics technology must be communicated by people. Communicating in writing, using software or hardware that works is not an easy task. The entire work drawings, computer programs, as well as the robot used in the sheep-shearing process were saved by wool industries after the program was completed. This was to ensure that the industry’s “intellectual properties” were protected. We had this information, but the possibilities are still to be realized.

4. Evidence from the Field

Robotics was a new field of research discovered in the early 1980s, just 10 years after the first international conference on robots (such as the International Symposia on Industrial Robots). Many of the most respected journals were founded within a few months. It’s interesting to see the contents of these journals today as well as those that started in the same time period.

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These are the topics of the random early issues of International Journal of Robotics Research (3;3), International Journal of Robotic Systems (2), and Robotica (1). Items marked with an asterisk belong to both lists.

* Control and dynamics of robots equipped with flexible arms.

* Dynamic and rigid control of manipulators

* manipulator kinematics, efficient calculation techniques,*

Motion planning and off-line programming*

* Object recognition based on visual, range data, and tactile sensing

*Studies on actuators, transducers, and transmissions

* Accuracy Improvement for rigid manipulators

* Image processing for visualisation

* Robotics, the next phase in research and

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These are the most recent issues of International Journal of Robotics Research (17:12), International Journal of Robotic Systems (16;1) and Robotica (16:6).

* Control and dynamics of flexible arm robots

* Dynamic and rigid control of manipulators

* manipulator kinematics, efficient calculation techniques,*

Motion planning and off-line programming

* Object recognition with images, data on range, and tactile sensing

*Studies on actuators, transducers, and transmissions

Automated fixture design based on kinematics

* Mobile manipulator, platform control
Although they may not be the most popular topics in current debates, they are still interesting. The first mobile robots appeared in the 1950s and early 1960s. Since the 1970s, assembly tasks have been studied. Walking robots were first developed in the 1980s.

This analysis could show what many researchers have said: robotics research has stagnated to a certain degree. This perspective suggests that robotics progress comes more from technological advancements that enable it, such as computing, rather than technological advances that are inherent in robotics.

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Another possibility is that robotics was an established field of research before the creation of these journals. This could mean that the steady advancement in robotics has been more significant than what the journals would indicate. The field has made some tangible progress: both tools and applications are available to make robots. Robotics research is often applied in industry without the need to refer to researchers or receive publicity. I have seen examples of entirely indigenous robotics technology in Western Australian companies2:

* Elegantly simple ROVs designed for use underwater

Telerobots for large-scale descaling are available to process-plant tanks with store-age.

2. Western Australia is home to 1.7 million people. The state’s industrial base consists of mining and agriculture, as well as offshore oil-and-gas industry.

* Equipment for laying pipelines of deep-sea oil or gas.

* sterilizable abatoir robots,

*Automatic abatoir systems and automated meat inspection and grading technologies

* Robots targeted for counter-terrorism practice ranges

* Robotic measurement of ore properties online

Researchers have a tendency not to include certain types of robots in our definition of “robot”, but this is limiting our research.

4.1. Comments from Researchers CSO Email Marketing Database

As I was writing this paper I sought out comments and suggestions from people of different backgrounds about the future direction of robotics research in 2000. These comments helped me to formulate the argument in this paper. I have copied a selection of their comments.

Robotics researchers received the most responses

Marcello Ang personal communications 1999 . . . To use robots or create new applications, you don’t need to be a scientist or engineer.

Vijay Kumar, personal communication 1999: “I have a futuristic concept for human-wearable robotics that act as extensions to the human body to assist users in accomplishing tasks they wouldn’t be able to do otherwise.” Our current focus is on children with disabilities.


Ron Daniel, personal communications Ron Daniel, personal communications, 1999 “Robotic implants that are usable for human use.” We already have intelligent prosthetic hands. There are many more areas where we can harness the intelligence of our replacement modules. …. I consider
More research is required on actuators. Most of the effort is focused on intelligence A robot factory

The moon will provide a place for humans to live. ….”

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Andrew Goldenberg, personal communication 1999: “Robots should not be modular and made from basic, fully-integrated components ….Too many papers to listinthe past.

This is detrimental to the field’s advancement. …. There is not enough research on the most recent methods of actuation.

Jack Phillips: “What is a robotic? ?…there are some people who fit lasers and computers on the road grader. However, they should redesign it instead. …. When is it a robot, you ask?

“A robot?”

Gintaras Radzivanas: “I doubt that robotics will be used often in environments in which the environment changes constantly.”
Tools such as Matlab or LabView, which are not dependent on the operating system or hardware, allow me to create the software that I need for the arms that I develop.”

We get to know the industry.

Bruce Varley the treatment of humans in process automation systems can be very poor and sometimes extremely bad. It is hard “…. to integrate humans and machines. We need to find a solution.

There are many human-factors experts. Engineers, sociologists, philosophers, and even shrinks who manage rodents in mazes.

The comments of researchers can confirm that robotics research has been pushed beyond the current boundaries by them (and other researchers). A few casual remarks can also bring back anger, which is due to the inconsistencies in current definitions. However, there are clearly defined long-term goals which can ensure a long and prosperous future in robotics research.

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5. Conclusion: Definitions and Applications

It is a paradox that much of our research is done in machines we don’t consider robots. It is actually our definition that limits the number of robots in use. Because technology that augments or replaces human labor is what we use to create robots in our research labs, this is because we only allow the technology we use to make them. These two examples are enough to convince most robotics researchers not to classify them as robots.

* The Kreepy Krawly pool cleaning machine is capable of performing a similar job to what many “intelligent” floor cleaning robots are designed to do. It meets the Mac-quarie definition of a “robot.” It can redirect its body away to avoid obstacles. It uses a simple mechanical guide to guide its way down. Once it has reached the surface of a pool, it then runs to scrub the entire pool. It isn’t programmed and doesn’t even have computers, so it wouldn’t be called a robot.

* Cruise missiles include many of the navigational and control methods that have been studied in mobile robotics research. However, it is possible to be uncomfortable accepting potentially dangerous weapons as robots.

These unneeded problems are caused by incorrect definitions. We use the wrong definitions to build robots.

They are used in almost every industrial process. Our research improves these technologies by creating new requirements, pushing the programming limits, and pushing the performance limits that have been established. Robotics students are taught about live-streamed software communication and actuators. These devices can be used to make ma-chines, processing facilities, and mine transportation. Researchers sometimes recognize these machines as robots or “robotic” and use selective robotic technologies. They then realize that the idea of total and complete automation, which drives most of their research, is not practical or suitable. It typically costs more than simpler solutions to the main problem, which is making the best use of available resources.

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Technology will continue to evolve regardless of the definition of robotics. However, it is urgent that we clarify our goals as robotics researchers to remove the contradictions in current robotics definitions. This can help our students to develop a broad and advanced view of the field.

Robotics research has relied on a range of more fundamental and specific fields for its success. This includes:

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* Mechanism Theory

* mechanics, dynamics,

* mechanical design,

* materials,

* hydraulics,

* mechatronics,

* Computer engineering and science

* computer vision,

* Electronics and electrical engineering

* communications,

* sensors, transducers,

* optics, acoustics, radar,

*automatic control, cybernetics,

* Biology, Zoology and

* Human psychological and physiological.

Robotics is a fascinating and challenging field because of the diversity of its disciplines. Although the current definitions focus on the idea of automatons, they are more grounded in science fiction literature than the real world. This is contrary to the lessons learned. Total automation is often impossible or unneeded.

5.1. Robotics: The science and art of extending human Motor abilities with machines

This definition covers all of our research efforts. It doesn’t cause conflicts in the application. Robotics research is driven primarily by human potential. This can be achieved by replacing an automated system with a human, or by incrementally improving existing human capabilities.

The definition could be argued to be too broad. All machines enhance our abilities in some way. This is true regardless of whether they are able to write more clearly (computer printers), or transport us (cars cars, automobiles, and bicycles). Robotics has created a unified structure that allows for the development of efficient machines. Robotics technology has been a huge success. It is important to remember that robotics-related technology is not able to perform any activity that is performed by humans. Some people would prefer to use the term “intelligent” to describe “machines.” It would be wrong to exclude “dumb”, “simple”, or “intelligent”, as these are often more efficient than the “intelligent”, which has been evaluated.

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Young researchers face many challenges due to the sheer volume of literature. Every year, the robotics field produces thousands of papers at conferences and journals. It is home to tens of thousands papers from the many disciplines that are involved. It’s possible to find reputable research papers in robotics that aren’t nearly identical to those published by other research groups working on the same problems. In order to make it easier for students to understand supporting documents, the academic strategy is to limit the scope of student research. For example, one could recommend focusing on manipulator arm with three prismatic and five rotating joints.

With that in mind, I think the proposed definition for robotics can be used to inform research on human ability or ability to perform a particular job. This was the basis for two major research projects, which included sheep shearing and mine clearing. It was crucial to study the human activities throughout the project. We tried to automatize the shearing process to improve our understanding. When the robot didn’t do what it was supposed, it was often because we did not have a good understanding of human capabilities. The question “Where are all these robots?” was the inspiration for tele-robotics research. This led to the creation of this article. It addresses the question by changing what robotics means.

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Without looking at the implications of the definition of robot, I won’t be able finish my essay. My opinion is that the relationship between my understanding of “robotics”, and “robot,” is not different from the relationship between musicology3

Music and other media. Any interpretation of “robot” is unaffected by the concept of “robotics.”

What impact will changing the definition of “robotics”, on how we research, have?

First, we need to address a major omission from our approach. We should look closely at how humans communicate with computers. We must also examine the established fields of human factors, ergonomics and robotics research. To raise awareness, we must invite experts from these fields to events.

The second option is to examine examples of successful technologies we don’t yet know about, which can be referred to as robotics. It is important that you understand the various approaches to these technologies as well as how they were created.

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We must also address the problem of students not having easy access to growing research literature. This problem isn’t unique to robotics. The number of related disciplines is vast, making it difficult to find relevant literature. While the Internet is a great tool, it won’t stop teams from working together on the same issue.

Fourth, biologists, human physiologicallogists, zoologists, and psychologists must work together to reproduce human characteristics and animals to better understand human limitations and capabilities.

Machine Learning and Artificial Intelligence allow for the production and management of large amounts of scientific data.

The Department of Energy’s scientific user facilities provide access to the most advanced instruments for research and ever-increasing amounts of data. The DOE’s Basic Energy Sciences (BES), scientific instrumentation for user facilities at x-ray neutron and nanoscale research, is among the most effective. It has over 16,000 users annually and produces huge amounts of data. This is a huge number, but it requires new technologies to address a multitude of technical issues in data acquisition, control, modeling, analysis, and reporting. Machine learning and artificial Intelligence (AI/ML), have opened up new possibilities for optimization, substitute models, advanced data analysis, and inverse problems. These amazing abilities indicate that AI/ML could greatly accelerate the search for fundamental phenomena across a broad spectrum of time, energy, and length, leading to breakthroughs in science across all disciplines. Buy Chief Sales Officer Email Database Leads Lists.

Both the scientific community and industry already use AI/ML techniques for data analysis. During an experiment, users facilities should use AI/ML technologies. This includes data analysis, data creation, data acquisition and data storage. AI/ML will be used to assist researchers using exascale computing. These advances will open up new research opportunities in energy sciences and other areas. AI/ML will help scientists move from the relatively simple measurement of properties and performance of molecules and substances to more complex interconnected functionalities of battery and information technology. This includes quantum-based sensors and devices that can be used in areas where conventional sequential optimization models and serendipitous material discoveries are not possible. AI/ML-enabled facilities for science will enable us to maximize DOE’s research impact.

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BES convened an expert roundtable with experts from each facility to determine the Priority and identify Priority Research Opportunities (PROs). This was to discuss the areas of physics and chemical synthesis. It also covered the areas of detector technology, modeling, simulation and atomic-scale characterisation techniques. On October 22-23, 2019, the roundtable met to discuss long-term, coordinated AI/ML research projects. This will allow for major breakthroughs in the areas of photon, neutron, and nanoscale science.

This report highlights the four PROs identified in the Pro 1 roundtable discussion. How AI/ML can draw highly-value information out of vast datasets. PRO 2 describes how AI/ML could make use of this information in real time to improve facilities’ research output. PRO3 is about AI/ML-based virtual labs (i.e. computing models of facilities for experimental purposes) to aid users and facilities in creating and controlling machine parameters, as well designing and executing experiments. The report’s final section provides an overview of maths, computer science, and describes areas where AI/ML capabilities can be useful to users of BES facilities.

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PRO 1: Effectively extract strategic and vital information from large, complex data sets

How can we extract reliable and useful information from the complex data that is being generated at BES’s science facilities?

New developments in BES’s xray, neutron, and nanoscale scientific facilities allow for the capture of larger data sets that are often taken in multiple modes. The sheer volume of data can make it hard to draw scientific conclusions due to the amount of work required to process and analyze the data. AI/ML methods can dramatically reduce the amount of effort needed while still allowing for immediate, real-time extraction properties of noisy or insufficient measurements. AI/ML is able to help uncover the complexity of high-dimensional issues (e.g. Multimodal measurements, various experiments, and so on. By identifying connections that are difficult to perceive.

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PRO 2: The autonomous control of scientific systems

How can we address the problems that result from complex, large-scale operations of scientific facilities for users in real time?

To realize the full potential of current and future-generation measurements, it will be necessary to use the most advanced methods to maintain and create the highest performance and to automate scientific discovery. AI/ML-based technologies are needed to efficiently search large complex parameter areas in real time and determine the health or failure of high-power equipment as well as the results of experiments conducted on these instruments. These capabilities are able to reduce the time it takes to tune a facility, decrease its downtime and increase performance.

PRO 3: Offline design and optimization of facilities, as well as research

Key question: How do we enable virtual laboratories–offline design and optimization of facility operation–to achieve new scientific goals?

Virtual laboratory environments that are physically accurate for the experimental facility (i.e. An experiment in the cloud will help guide in-silico research from conception to synthesis and measurement. Digital Twins are able to accurately replicate facilities. They can also provide constant updates from real-world experiences and workflows that allow for new capabilities and innovative strategies for improving the knowledge acquisition process. Digital twins can be used to help develop AI/ML strategies for addressing the different Priority Research Opportunities.

PRO 4: Use data from shared research for machine learning-driven discoveries

How can science be used to accelerate discovery through the use diverse data collected by the BES facilities available for scientific users?

To accelerate research across institutions, it is necessary to improve data sharing as well as curation. New AI/ML technology can combine diverse scientific data sources to create vast new datasets. This opens up new possibilities for science discovery. The development of shared workflows that use a shared repository can help to advance standards in data formats, formats, priorities, formats, and other formats. These data sets could be used to train new AI/ML techniques.

Results of research. Published under Creative Commons
International License for Attribution 4.0
Figure 1. Figure 1. Automating the entire experimental workflow–instrument setup and tuning, sample selection and synthesis, measurement, data analysis and model-driven data interpretation, and follow-up experimental decision-making–will bring about revolutionary efficiencies and

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The US Department of Energy (DOE), offers access to some of the most advanced instruments for research. One of the most important Basic Energy Sciences (BES), Nanoscale, X-ray, and Neutron SUFs in the world employs more than 16,000 people annually and generates petabytes, which is equivalent to one million gigabytes worth of data that provides high-impact science. Modern facilities have the ability to handle a variety of technical problems, including data acquisition and control, modeling and analysis. Instrument improvements will enable more advanced research through the provision of a higher quantity and a better quality probe particle (i.e. New methods are required to obtain the research results. Nanoscale Science Research Centers, along with Synchrotron Light Sources Neutron Sources (NSRCs), offer exciting new experiments that combine multiple data sets. Active control is required for NSRCs to synthesize new material. The ability to collect and use large amounts of data to guide research and experiments will open up new research avenues in engineering, biological and physical science.

As new sources provide greater coherence, coherent imaging with x-rays (or “lensless”) is becoming a more pressing challenge. It can be used at storage ring-based synchrotrons and free electron lasers (XFELs). Advanced forecasting and feedback are vital to research quality. They are also essential for high-resolution simulations. Accelerators require optimization of high-dimensional areas, as well as anomaly/breakout detection in order to maximize performance. This is to protect the high-power machine that has high repetition rates. However, lensesless imaging can be extremely computational and data-intensive. Sophisticated compression/rejection data pipeline tools operating at the “edge” (i.e., next to the detector or experiment) are needed to extract and save information “on the fly.” To automatically steer experiments or synthesis through a high-dimensional parameter area, active control is required. Figure 1.

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This illustration shows an autonomous control system that is used in experimental systems.

Data collection is not complete without the need for new tools to analyze and share multimodal data sets that include simulations and data mergings. Large-scale computations require both automation and innovative data science methods. These applications include molecular dynamics simulations that compare to neutron scattering results and the density function theory (DFT), to compare with neutron data Monte Carlo Ray Tracing to model instrumentation and complex sampling effects and diffuse scattering modeling for investigating imperfections in solids and large-scale reconstructions from tomographic images. The NSRCs are a place where you can discover new ideas.

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Intuition and design principles models are used to drive chemical compounds and materials that have desirable properties for social purposes. These models are slower than the process of simulation and experimentation. There are many chemical compounds and materials out there. Random experiments are similar to finding the right properties using a needle in a haystack.

Data science and computational problems are present throughout the facility’s entire life-cycle. However, it is expected that both machine-learning (ML), as well as artificial intelligence (AI), will have an impact that is transformative for SUF science. AI/ML strategies to analyze, control, and model will greatly speed up research and discovery with computational methods. Artificial intelligence refers to machines that can perform tasks similar to human intelligence. These include making plans, understanding language and recognising sounds, objects, learning, problem solving, and learning. One way to achieve AI is through the ML method. Machines that learn from data without being explicitly programmed are a part of the ML method. AI/ML will be an integral part DOE’s design and development arsenal in the next ten years, just as experimental computational, theoretical and other tools are. SUF researchers will work with AI/ML specialists from DOE to operate facilities and generate research data. This will allow scientists to create new models of physical properties as well as theoretical discoveries, which will fuel scientific research and enable new ways of designing materials and chemicals.

Although AI/ML is widely recognized as a collection data analysis tools, the scope of the SUFs’ activities extends beyond facilities operations. They cover everything from the creation and analysis of new research to the analysis of existing machines. AI/ML can integrate simulations, physics, and data to optimize accelerators. This allows users to create complex configurations that offer new capabilities. The automation of control over experimental systems can transform the way researchers work. This allows them to explore difficult problems in high dimensions that were previously impossible. These advancements could allow for the exploration of targeted chemicals and substances 1000 times faster than current methods. They also may help to understand the conformational patterns of proteins and reveal intricate hierarchical relations that span from molecular interactions transport phenomena to understanding the energy landscapes involved in chemical and material transformations.

BES hosted a roundtable of experts to determine Priority Research Opportunities (PROs).
October 22-23, 2015 from user and SUF communities. They also included scientists from a range of disciplines and cross-cutting sciences like computational science detector technology and accelerator technology, theory and simulation, modeling, simulation, and atomic-scale characterisation methods. Roundtable participants identified potential research areas for the future that could form the basis of a long-term, planned research initiative that could result in significant advances in neutron and photon science. See Appendix A to see the participants and their affiliations. Also, appendix B contains the agenda.

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Roundtable participants were asked to share their views on how big data and AI/ML can be used to maximize the effectiveness and impact of SUFs. The simulation process will face technical challenges, along with control data acquisition and deeper data analyses. Participants considered new technologies for speeding up high-fidelity simulations for online models, fast-tuning in high-dimensional spaces, anomaly/breakout detection, “virtual diagnostics” that can operate at high-repetition rates, and sophisticated compression/rejection data reduction workflows operating at the edge to capture high-value data and steer experiments in real time.
1. How can AI/ML improve your lab’s efficiency? your lab?

2. What are the strengths and weaknesses of detectors? How can AI/ML assist?

3. AI/ML can enhance the user experience at DOE facilities when they acquire data using novel methods of experimentation, such as adaptive control, data analysis and data analysis. ?

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4. Are there any limitations in AI/ML development for data production and analysis at your facility? Please describe.

5. Is there a way to integrate Advanced Scientific Computing Research (ASCR), data analytics, HPC (HPC), and high-speed networking capabilities for research-related or theoretical issues that require a lot data?

6. Which aspects of AI/ML interest you most? This can be used to create user-friendly services. CSO Email Profile

Participants engaged in a roundtable discussion to identify possible themes. The most prominent themes were identified in four breakout sessions. They concern online control, data acquisition, multimodal analysis and simulations/models. The writing team identified four PROs from the discussions of the first morning. They also provided examples of “killer applications” to show the potential benefits of each PRO.

This report highlights the key issues and research directions that have been identified by the AI/ML roundtable. These issues are described in detail by the four PROs. Each one is described in detail in each section of the report.

PRO 1: Collect strategic and critical information efficiently from large, complex data sets

PRO 2: Meet the challenges of autonomous control in scientific systems

PRO 3: Offline design and optimization of facilities and research

PRO 4: Use the shared data of scientists for machine learning-driven discoveries

The report ends with a section that highlights opportunities to collaborate ASCR to facilitate the creation of enhanced AI/ML capabilities relevant to each of the four Pros.

A coordinated effort will be made to achieve major breakthroughs in photon, neutron and nanoscale science. This will allow for these facilities to become the next generation of capabilities.

PRO 1. PRO 1. Effectively extract strategic information from large databases

How can we get reliable and useful information out of the complex and ever-growing information generated by BES’s research facilities and users?


It is clear that BES user facilities, which include a variety x-ray electron and neutron, optical probes as well as atoms, are creating ever-larger, more complex data streams at speeds greater than traditional analysis techniques [1-101-10]. These streams are essential for science because they provide chemical and physical information as well as mesoscale, nanoscale, and anatomical structures and dynamics. AI/ML methods must be restricted and influenced by physical theories to facilitate and accelerate sampling of dynamic and structure spaces and effective forward modeling and pattern matching.

Tools and methods such as x-ray optical, neutron, electron probes with other microscopy techniques) are becoming more common. The tools and methods (e.g. x-ray optical and neutron probes with other microscopy technologies) for studying phenomena at the nanoscale dramatically increase. However, we are now faced with the challenge of harnessing the data and connecting the different parts of scientific data that has been generated. These issues can be solved by three technological breakthroughs: (1) faster understanding and characterisation of samples; (2) real-time control and online experiments that are automated and (3) the ability to handle greater complexity by uncovering connections in large-scale environments. If scientists are unable to meet these challenges, then the SUFs’ research output will not be able match the facility’s capabilities.

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Directions for Research

The PRO is composed of three themes. AI/ML technologies can be used to increase the efficiency of research in BES SUs by:

1. Data transformation from raw data into scientific data (i.e. capturing noisy, imperfect, and rough images of observations, as well as taking physical quantities or valuable information.

2. This allows for quick extraction of information to allow for instant feedback to participants. It also allows for modification of the process while it is still in progress and, more generally, to provide a basis for autonomous controlling an experiment. (PRO 2. ).