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Industrial robot
From Wikipedia, the free encyclopedia.
Industrial robotics refers to the study, design and use of robots for manufacturing. Applications for industrial robots include welding, painting, assembly, product inspection and testing.
There are a small number of commonly used configurations for industrial automation, including articulated robots (the most common), SCARA robots and gantry robots (a.k.a. Cartesian robots or x-y-z robots).
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Robot actions are determined by programmed routines that specify the direction, speed, and distance of a series of coordinated motions. For more precise guidance, robots are often assisted by machine vision systems acting as the eyes.
History of Industrial Robotics
The first company to produce an industrial robot was Unimation.
In most cases, a machine vision system will use a combination of these processing techniques to perform a complete inspection. A system that reads a barcode may also check a surface for scratches and measure the length and width of a machined component.
Applications of Machine Vision
Machine vision systems are widely used in semiconductor device fabrication; indeed, without machine vision, yields for computer chips would be significantly reduced. Machine vision systems inspect silicon wafers, processor chips, and subcomponents such as resistors and capacitors.
In the automotive industry, machine vision systems are used to guide industrial robots, gauge the fit of stamped metal components, and inspect the surface of the painted vehicle for defects.
Though machine vision techniques were developed for the visible spectrum, the same processing techniques may be applied to images captured using imagers sensitive to other forms of light such as infrared.
Related fields
Machine vision is distinct from computer vision, an academic field of research often classified as a subfield of artificial intelligence. Computer vision extends to topics related to autonomous robotics and machine representation of human vision. Machine vision refers to automated imaging systems used in factories, assembly plants, and other industrial environments.
Industrial Robots.
To cope with shrinking product life cycles, computational methods are needed to reduce the time required to configure assembly lines. One idea is to formally model a small ``vocabulary of parameterized modular components (e.g., robot arms, conveyor belts, flexible part feeders, modular fixtures, light beams, D vision systems) and develop CG-based algorithms to efficiently compile them into an assembly system for a given CAD product model [7]. Here are a few relevant problem areas
1.CONVEX DECOMPOSITION For motion planning, collision checking is currently most efficient for convex polyhedra, so we need better methods for convex decomposition of polyhedra. Algorithms should produce a near-optimal number of convex components. As for other research areas, fast, robust code is needed. Convex decomposition has many applications and is currently a bottleneck. See [] for a survey of current methods and [] for a practical approach to the problem.
.GRASPING AND FIXTURING The goal here is to place contacts - fingers or fixture elements - so as to constrain object motion. This is one of the central problems in robotics. A number of basic questions have been answered, such as the minimum number of contacts sufficientto hold any nonrotational part in form closure (4 in D, 7 for polyhedra) [84][6] but efficient algorithms are still needed. In modular fixturing, where fixture elements are constrained to a regular lattice, recent results suggest a number of open questions about the existence of solutions for classes of fixtures and parts [115][14][154].
When there is uncertainty in part pose or applied forces, minimizing the number of grasp points can be posed as a convex set covering problem. Recently, CG researchers have described efficient and probably practical algorithms for near-optimal grasps. This goes beyond the previous works which either do not consider optimality, or do not allow search over a large space of possible finger placements. If the CG methods pan out in practice, this would be a strong vindication of CG in robotics.
.APPROXIMATION ALGORITHMS FOR GRASP CIRCUIT PLANNING Consider picking up parts as they arrive on a conveyor belt the gripper may have multiple suction cups that must be ``loaded during each pass Given a robot gripper withsuction cups and a collection ofparts, rapidly approximate an optimal path for loading the suction cups and depositing the parts in a pallet. Although this problem can be reduced to TSP, recent approximation results were successfully adapted to this and variations where the points to be visited are moving with known velocity []. Approximation algorithms have rarely been applied to problems in industrial robotics but hold great promise.
4.GEOMETRIC PROBING CAD models are assumed by many algorithms but are often unavailable. Geometric probing can be used to generate such models. Probing hardware includes touch probes, light beams, scanline and raster cameras. Depending on the application and sensor, the probing strategy may compute convex hull, line hull or ray hull. See [14] for a review. More work is needed on online probing strategies that include models of probe and control uncertainty. The problem of probing to minimize error turns out to be dual to grasping, so the algorithms mentioned there are directly relevant.
5.PART ORIENTING Algorithmic approaches to orienting (``feeding) parts for industrial assembly are needed. To be practical and cost effective, feeders must be able to rapidly and repeatedly orient parts at subsecond rates. Current feeders are designed by human trial and error. Complete algorithms are needed to take part geometry as input and generate feeding strategies or feeder hardware specifications as output. A complete algorithm for feeding D parts with algebraic boundaries is described in [14]. More work is needed to design feeders for d parts and to provide accurate mechanical simulation of collisions and statistical behavior of parts arriving in bulk.
6.ASSEMBLY SEQUENCE PLANNING The problem of disassembling a collection of parts has a strong geometric component. For local motions, a disassembly sequence can be found in polynomial time. The worst case running time is, but it is fast in practice [144]. More recently, good worst-case bounds have been given for an important special case where the set of possible disassembly directions is fixed a priori (true for many robotic workcells). The latter methods use lower envelopes and Davenport-Schinzel sequences to efficient find collisions along the disassembly direction. The main limitation so far is the inability to deal with complex motion during disassembly. e.g. a translation and twist motion would not be possible. Extensions of this type, plus the addition of other constraints on the sequence, will make this a rich area for future research.
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