We can not write an introduction to robotics without talking the fundamental laws of robots. The popular Russian writer of science fiction Isaac Asimov formulated the three fundamental laws for robots. The perspective in contrast to the robot described by Capek, a benevolent, good robot that acts as the human being. Asimov visualized the robot as an automated mechanical creature of human appearance having no feelings. The behavior and acts were dictated by a “brain” programmed by human beings, in such a way that certain ethical rules were satisfied.
The robotics term was thereafter introduced by Asimov, as the science devoted to the study of robots which was based on three fundamental laws. They were complemented by Asimov’s law in 1985. Since the robot laws establishment, the word robot has attained the alternate meaning as an industrial product designed by engineers or specialized technicians.
1. A robot may not allow humanity to come to harm, injure humanity, or, through inaction.
2. A robot may not allow a human being to injure, or, through inaction.
3. A robot has to obey the orders given it by human beings except where such orders would conflict with the First Law.
4. A robot has to protect its own existence as long as such protection does not conflict with the First or Second Laws. The first laws were complemented by two more laws goaled for industrial robots by Stig Moberg from ABB Robotics. In the additional laws, also the robot motion is considered.
5. A robot has to follow the trajectory specified by its master, as long as it does not conflict with the first three laws.
6. A robot has to follow the velocity and acceleration specified by its master, as long as nothing stands in its way and it does not conflict with the other laws.
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The Terms Robotics and Industrial Robots
The robots distinction lie somewhere in the sophistication of the programmability of the device – a (NC) milling machine is not an industrial robot. If a device of mechanical can be programmed to perform a wide variety of applications, it is probably an industrial robot. The essential difference between an NC machine and an industrial robot is the versatility of the robot, that it is provided with tools of different types and has a large workspace compared to the volume of the robot itself. The numerically controlled machine is dedicated to a special task, although in a fairly flexible way, which gives a system built after fixed and limited.
The learning and control of industrial robots is not a new science, rather a mixture of “classical fields”. From mechanical engineering, the machine is studied in dynamic and static situations. It means of the spatial mathematics motions can be described. The designing and evaluating Tools algorithms to achieve the desired motion are provided by control theory. Electrical engineering is helpful when designing interfaces and sensors for industrial robots. Last but not least, computer science gives for programming the device to perform a desired task.
The term robotics has presently been defined as the science studying “the intelligent connection of perception to action”. Industrial robotics is a subject concerning robot design, control and applications in industry and the products are now reaching the level of a mature technology. The robotics technology status can be reflected by the definition of a robot originating from the Robot Institute of America.
Most of the organizations recently agree less or more to the definition of industrial robots, formulated by the International Organization for Standardization, ISO.
• Manipulating industrial robot is a controlled automatically, multi-purpose, reprogrammable, manipulative machine with several degrees of freedom, which may be either fixed in place or mobile for use in industrial automation applications.
• Manipulator is a machine, the mechanism of which usually contains of a series of segments jointed or sliding relative to one another, for the purpose of grasping and/or moving objects (pieces or tools) usually in several degrees of freedom.
The learning and control of industrial robots is not a new science, rather a mixture of “classical fields”. From mechanical engineering, the machine is studied in dynamic and static situations. It means of the spatial mathematics motions can be described. The designing and evaluating Tools algorithms to achieve the desired motion are provided by control theory. Electrical engineering is helpful when designing interfaces and sensors for industrial robots. Last but not least, computer science gives for programming the device to perform a desired task.
The term robotics has presently been defined as the science studying “the intelligent connection of perception to action”. Industrial robotics is a subject concerning robot design, control and applications in industry and the products are now reaching the level of a mature technology. The robotics technology status can be reflected by the definition of a robot originating from the Robot Institute of America.
Most of the organizations recently agree less or more to the definition of industrial robots, formulated by the International Organization for Standardization, ISO.
• Manipulating industrial robot is a controlled automatically, multi-purpose, reprogrammable, manipulative machine with several degrees of freedom, which may be either fixed in place or mobile for use in industrial automation applications.
• Manipulator is a machine, the mechanism of which usually contains of a series of segments jointed or sliding relative to one another, for the purpose of grasping and/or moving objects (pieces or tools) usually in several degrees of freedom.
Scenarios of Future Industrial Robots
Long-term visions industrial robots of the future have been depicted in five scenarios which are given in the following as examples:
1. Robot assistants as a versatile tool at the workplace
Scenario: A robot assistant is used as a versatile tool by the worker at a manual workplace. The applications could be manifold: arc welding, machining, woodworking, aircraft assembly etc.
Operation: The arm of compact robot is towed manually to the workplace. On a wireless portable interface the worker selects a process (e.g. “welding”). The worker indicates the process by guiding the robot along contours or over surfaces while giving additional instructions by voice. Process parameters are set and the sensor supported motion results in the machined/welded contours. The worker may override the robot motion as required. Successive tasks can be performed automatically without supervision by the worker.
2. Robot assistants in crafts
Scenario Robot as a versatile assistant for crafts
Operation The robot is mobile and is equipped with two arms and is instructed by gesture, voice and graphics. A craftsman (e.g. locksmith) has to weld a steel structure (stairway). The robot fettles the seams automatically with a brush.
3. Robots for empowering humans
Scenario The robot for human augmentation (force or precision augmentation) in assembly
Operation In a bus gear box assembly the heavy central shaft is grasped by the robot which balances it softly so the worker can insert it precisely in the housing. The robot learns and optimizes the constrained motion in successive steps “on the job”.
4. Multi-robot cooperation
Scenario: Many robots cooperate to execute a manufacturing task within a minimal workcell.
Operation: Robot 1 fetches a panel that has to be mounted simultaneously with the cover and robot 2 tells robot 1 where to put the panel. Finally robot 3 fetches an automatic screw driver to mount the cover and the panel together on the washing machine framework. If this is not OK, robot 3 needs the help from a worker who will change for example the orientation of the screw driver by direct interaction with the tool and the assembly can proceed.
1. Robot assistants as a versatile tool at the workplace
Scenario: A robot assistant is used as a versatile tool by the worker at a manual workplace. The applications could be manifold: arc welding, machining, woodworking, aircraft assembly etc.
Operation: The arm of compact robot is towed manually to the workplace. On a wireless portable interface the worker selects a process (e.g. “welding”). The worker indicates the process by guiding the robot along contours or over surfaces while giving additional instructions by voice. Process parameters are set and the sensor supported motion results in the machined/welded contours. The worker may override the robot motion as required. Successive tasks can be performed automatically without supervision by the worker.
2. Robot assistants in crafts
Scenario Robot as a versatile assistant for crafts
Operation The robot is mobile and is equipped with two arms and is instructed by gesture, voice and graphics. A craftsman (e.g. locksmith) has to weld a steel structure (stairway). The robot fettles the seams automatically with a brush.
3. Robots for empowering humans
Scenario The robot for human augmentation (force or precision augmentation) in assembly
Operation In a bus gear box assembly the heavy central shaft is grasped by the robot which balances it softly so the worker can insert it precisely in the housing. The robot learns and optimizes the constrained motion in successive steps “on the job”.
4. Multi-robot cooperation
Scenario: Many robots cooperate to execute a manufacturing task within a minimal workcell.
Operation: Robot 1 fetches a panel that has to be mounted simultaneously with the cover and robot 2 tells robot 1 where to put the panel. Finally robot 3 fetches an automatic screw driver to mount the cover and the panel together on the washing machine framework. If this is not OK, robot 3 needs the help from a worker who will change for example the orientation of the screw driver by direct interaction with the tool and the assembly can proceed.
Main Obstacles to Progress Long term Vision of Future Robot
The described long-term vision realization is subject to overcoming the following barriers:
• Man-machine-interaction: Today, manufacturing tasks cannot be expressed in intuitive enduser terms as would be typically required for instructions by voice. Multimodal dialogues based on voice, graphics, and texts should be initiated to quickly resolve insufficient or ambiguous information.
• Mechanical limitations: Robot mechanics account for some 80% of the system price. For some parts, particularly gears, there exists a painful dependency on Japanese suppliers. New drive lines should be developed where high density motors and compliant compact gears (e.g. on the basis of mechanical wave generators) with integrated torque and position sensors are used in order to decrease this dependency. Advanced control of sensor based drive systems will make it possible to decrease cost and weight without reducing the robot performance. Furthermore a cooperative space-sharing robot needs harmless motions. This can be achieved by intrinsically safe designs or suitable sensor equipment.
• Sensors: Full 3D recognition is required for work piece and worker localization in less structured environments. Inexpensive sensors do not exist yet but high volume supervision and entertainment applications will make this technology affordable.
• Robot automation life-cycle costs. The gains of robots productivity are probably less pronounced than quality gains, especially for investments into cooperating which in some cases will result in severe cost limits of such systems to achieve cost-effectiveness.
• Socio-economic factors. The systems of advanced mechatronic may slow down investments in novel robot systems especially in areas with little or no automation a strong conservative attitude in industry towards. The introduction of robotics into industries characterized by low status and bad working conditions can contribute to changing their attractiveness to employ young people.
• Standards. First standards towards cooperative robots and intelligent assist devices (e.g. “smart balancers”) are about to emerge. New standards for robot assistants allowing physical interaction at normal working speeds will be required. Setting new standards needs committed industries to support the high cost and time involved.
• Man-machine-interaction: Today, manufacturing tasks cannot be expressed in intuitive enduser terms as would be typically required for instructions by voice. Multimodal dialogues based on voice, graphics, and texts should be initiated to quickly resolve insufficient or ambiguous information.
• Mechanical limitations: Robot mechanics account for some 80% of the system price. For some parts, particularly gears, there exists a painful dependency on Japanese suppliers. New drive lines should be developed where high density motors and compliant compact gears (e.g. on the basis of mechanical wave generators) with integrated torque and position sensors are used in order to decrease this dependency. Advanced control of sensor based drive systems will make it possible to decrease cost and weight without reducing the robot performance. Furthermore a cooperative space-sharing robot needs harmless motions. This can be achieved by intrinsically safe designs or suitable sensor equipment.
• Sensors: Full 3D recognition is required for work piece and worker localization in less structured environments. Inexpensive sensors do not exist yet but high volume supervision and entertainment applications will make this technology affordable.
• Robot automation life-cycle costs. The gains of robots productivity are probably less pronounced than quality gains, especially for investments into cooperating which in some cases will result in severe cost limits of such systems to achieve cost-effectiveness.
• Socio-economic factors. The systems of advanced mechatronic may slow down investments in novel robot systems especially in areas with little or no automation a strong conservative attitude in industry towards. The introduction of robotics into industries characterized by low status and bad working conditions can contribute to changing their attractiveness to employ young people.
• Standards. First standards towards cooperative robots and intelligent assist devices (e.g. “smart balancers”) are about to emerge. New standards for robot assistants allowing physical interaction at normal working speeds will be required. Setting new standards needs committed industries to support the high cost and time involved.
Robotic in Automotive Industries
Currently, robots have been used mainly in the automotive industries, including their supply chains, accounting for more than 60% of total robot sales. Typically prime targets for robot automation in car manufacturing are welding, assembly of body, motor and gear-box, and painting and coating. Automotive industries are the driver key of application in terms of cost, technology and services robotics industry are subject to fierce global competition. Robot systems increasingly to be the central portion of investments in automotive manufacturing which may reach 60 % of the total manufacturing equipment investment in the year 2010 (for car and 1st tier suppliers). In general it is estimated that the a robot automation investment cost in these industries accounts to 4 times the unit prize of a robot.
The automation degree in the automotive industries is expected to increase in the future as robots will push the limits towards flexibility regarding faster change-over-times of different product types (through rapid programming generation schemes), capabilities to deal with tolerances (through an extensive use of sensors) and costs (by reducing customized work-cell installations and reuse of manufacturing equipment). These challenges lead to the following present RTD trends in robotics:
• Expensive fixing equipment and single-purpose transport is replaced by standard robots thus offering continuous production flows. Remaining fixtures may be adjusted by the robot itself.
• Cooperative robots in a work-cell coordinate fixing, handling and process tasks so that robots may be adjusted easily to varying work piece geometries, process parameters and task sequences. Short change-over times are achieved by automated program generation which takes into account necessary synchronization, collision avoidance and robot-to-robot calibration.
• Increased use of sensor systems and measuring devices mounted on robots and RFID-tagged parts carrying individual information contributes to better dealing with tolerances in automated processes.
• The gap between fully manual and fully automated task execution of human-robot-cooperation bridges. Robots and people will share cognitive, sensing, and physical capabilities.
The automation degree in the automotive industries is expected to increase in the future as robots will push the limits towards flexibility regarding faster change-over-times of different product types (through rapid programming generation schemes), capabilities to deal with tolerances (through an extensive use of sensors) and costs (by reducing customized work-cell installations and reuse of manufacturing equipment). These challenges lead to the following present RTD trends in robotics:
• Expensive fixing equipment and single-purpose transport is replaced by standard robots thus offering continuous production flows. Remaining fixtures may be adjusted by the robot itself.
• Cooperative robots in a work-cell coordinate fixing, handling and process tasks so that robots may be adjusted easily to varying work piece geometries, process parameters and task sequences. Short change-over times are achieved by automated program generation which takes into account necessary synchronization, collision avoidance and robot-to-robot calibration.
• Increased use of sensor systems and measuring devices mounted on robots and RFID-tagged parts carrying individual information contributes to better dealing with tolerances in automated processes.
• The gap between fully manual and fully automated task execution of human-robot-cooperation bridges. Robots and people will share cognitive, sensing, and physical capabilities.
Robotic Application in Industries
There are various new fields of applications in which robot technology is not widespread today due to its lack of flexibility and high costs involved when dealing with varying lot sizes and variable product geometries. New robotic applications will soon emerge from new industries and from SMEs, which cannot use today’s inflexible robot technology or which still require a lot of manual operations under strenuous, unhealthy and hazardous conditions. Relieving people from bad working conditions (e.g., operation of hazardous machines, handling poisonous or heavy material, working in dangerous or unpleasant environments) leads to many new opportunities for applying robotics technology. Bad working conditions examples can be found in foundries or the metal working industry.
Besides the need of handling objects at very high temperatures, work under unhealthy conditions takes place in manual fettling operations, which contribute to about 40% of the total production cost in a foundry. Manual fettling that’s mean strong vibrations, heavy lifts, metal dust and high noise levels, resulting in annual hospitalization costs of more than €150m in Europe. Bad working conditions can also be found in slaughterhouses, fisheries and cold stores where beside low temperatures also the handling of sharp tools makes the work unhealthy and hazardous
• Assembly and disassembly (vehicles, airplanes, refrigerators, washing machines, consumer goods). In some cases fully automatic task operation by robots is impossible. Cooperative robots should support the worker in terms of force parallelization, augmentation, or sharing of tasks.
• Aerospace industry presently uses customized NC machines for drilling, machining, assembly, quality testing operations on structural parts. In assembly and quality testing, the automation level is still low due to the variability of configurations and insufficient precision of available robots. Identified requirements for future robots call for higher accuracy, adaptivity towards workpiece tolerances, flexibility to cover different product ranges, and safe cooperation with operators.
• SME manufacturing: Fettling, cutting, deflashing, deburring, drilling, milling, grinding and polishing of products made of metal, glass, ceramics, plastics, rubber and wood.
• Food and consumer good industries: Processing, filling, assembly, handling and packaging of food and consumer goods
• Construction: Drilling, Cutting, grinding and welding of large beams and other construction elements for buildings, bridges, ships, trains, power stations, wind mills etc.
Besides the need of handling objects at very high temperatures, work under unhealthy conditions takes place in manual fettling operations, which contribute to about 40% of the total production cost in a foundry. Manual fettling that’s mean strong vibrations, heavy lifts, metal dust and high noise levels, resulting in annual hospitalization costs of more than €150m in Europe. Bad working conditions can also be found in slaughterhouses, fisheries and cold stores where beside low temperatures also the handling of sharp tools makes the work unhealthy and hazardous
• Assembly and disassembly (vehicles, airplanes, refrigerators, washing machines, consumer goods). In some cases fully automatic task operation by robots is impossible. Cooperative robots should support the worker in terms of force parallelization, augmentation, or sharing of tasks.
• Aerospace industry presently uses customized NC machines for drilling, machining, assembly, quality testing operations on structural parts. In assembly and quality testing, the automation level is still low due to the variability of configurations and insufficient precision of available robots. Identified requirements for future robots call for higher accuracy, adaptivity towards workpiece tolerances, flexibility to cover different product ranges, and safe cooperation with operators.
• SME manufacturing: Fettling, cutting, deflashing, deburring, drilling, milling, grinding and polishing of products made of metal, glass, ceramics, plastics, rubber and wood.
• Food and consumer good industries: Processing, filling, assembly, handling and packaging of food and consumer goods
• Construction: Drilling, Cutting, grinding and welding of large beams and other construction elements for buildings, bridges, ships, trains, power stations, wind mills etc.
Grasper Control Language of BarretHand Robotic
The BarrettHand consists its central supervisory microprocessor that coordinates four dedicated motion-control microprocessors and controls I/O via the RS232 line inside its compact palm. The control electronics are created on a parallel 70-pin backplane bus. Associated with each motion-control microprocessor are the related motor commutation electronics, sensor electronics, and motor-power current-amplifier electronics for that finger or spread action. The microprocessor of supervisory directs I/O communication via a high-speed, industry-standard RS232 serial communications link to the work cell PC or controller. RS232 offers compatibility with any robot controller while limiting umbilical cable diameter for all communications and power to only 8mm. The published grasper communications language (GSL) optimizes communications speed, exploiting the difference between bandwidth and time-of-flight latency for the special case of graspers. It is important to recognize that graspers usually remain inactive during most of the work cell cycle, while the arm is performing its gross motions, and are only active for short bursts at the ends of an arm’s trajectories.
While the robotic arm knees high control bandwidth during the entire cycle, the grasper has plenty of time to receive a large amount of setup information as it approaches its target. Then, the work cell controller releases a “trigger” command with precision timing, such as the ASCII character “C” for close, which begins grasp execution within a couple milliseconds.
The grasper can accept commands and communicate from any robot-workcell controller, PC, UNIX box, Mac, or even a Palmpilot via standard ASCII RS232-C serial communication — the common denominator of communications protocols. Though robust, RS232 has a slow bandwidth compared to FireWire standards or USB, but its simplicity leads to small latencies for short bursts of data. It has achieved time of flight to acknowledge and execute a command (from the work cell controller to the grasper and then back again to the work cell controller) of the order of milliseconds by streamlining the GCL.
While the robotic arm knees high control bandwidth during the entire cycle, the grasper has plenty of time to receive a large amount of setup information as it approaches its target. Then, the work cell controller releases a “trigger” command with precision timing, such as the ASCII character “C” for close, which begins grasp execution within a couple milliseconds.
The grasper can accept commands and communicate from any robot-workcell controller, PC, UNIX box, Mac, or even a Palmpilot via standard ASCII RS232-C serial communication — the common denominator of communications protocols. Though robust, RS232 has a slow bandwidth compared to FireWire standards or USB, but its simplicity leads to small latencies for short bursts of data. It has achieved time of flight to acknowledge and execute a command (from the work cell controller to the grasper and then back again to the work cell controller) of the order of milliseconds by streamlining the GCL.
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