Industrial manipulators
Successes with at least partially automating the nuclear industry also meantthe technology was available for other applications, especially general manufacturing.
Robot arms began being introduced to industries in 1956 by
Unimation (although it wouldn’t be until 1972 before the company made a
profit).37 The two most common types of robot technology that have evolved
for industrial use are robot arms, called industrial manipulators, and mobile
carts, called automated guided vehicles (AGVs).
An industrial manipulator, to paraphrase INDUSTRIAL the Robot Institute of America’s
MANIPULATOR definition, is a reprogrammable and multi-functional mechanism that is designed
to move materials, parts, tools, or specialized devices. The emphasis
in industrial manipulator design is being able to program them to be able
to perform a task repeatedly with a high degree of accuracy and speed. In
order to be multi-functional, many manipulators have multiple degrees of
freedom, as shown in Fig. 1.4. The MOVEMASTER arm has five degrees
of freedom, because it has five joints, each of which is capable of a single
rotational degree of freedom. A human arm has three joints (shoulder, el
bow, and wrist), two of which are complex (shoulder and wrist), yielding six
degrees of freedom.
Control theory is extremely important in industrial manipulators. Rapidly
moving around a large tool like a welding gun introduces interesting problems,
like when to start decelerating so the gun will stop in the correct location
without overshooting and colliding with the part to be welded. Also,
oscillatory motion, in general, is undesirable. Another interesting problem is
the joint configuration. If a robot arm has a wrist, elbow and shoulder joints
like a human, there are redundant degrees of freedom. Redundant degrees
of freedom means there are multiple ways of moving the joints that will accomplish
the same motion. Which one is better, more efficient, less stressful
on the mechanisms?
It is interesting to note that most manipulator control was assumed to be
ballistic control, or open BALLISTIC CONTROL loop control. In ballistic control, the position trajectory
OPEN LOOP CONTROL and velocity profile is computed once, then the arm carries it out. There
are no “in-flight” corrections, just like a ballistic missile doesn’t make any
course corrections. In order to accomplish a precise taskwith ballistic control,
everything about the device and how itworks has to bemodeled and figured
CLOSED-LOOP into the computation. The opposite of ballistic control is closed-loop control,
CONTROL where the error between the goal and current position is noted by a sensor(s),
and a new trajectory and profile is computed and executed, thenmodified on
the next update, and so on. Closed-loop control requires external sensors to
FEEDBACK provide the error signal, or feedback.
In general, if the structural properties of the robot and its cargo are known,
these questions can be answered and a program can be developed. In practice,
the control theory is complex. The dynamics (how themechanism moves
and deforms) and kinematics (how the components of the mechanism are
connected) of the systemhave to be computed for each joint of the robot, then
those motions can be propagated to the next joint iteratively. This requires a
computationally consuming change of coordinate systems from one joint to
the next. To move the gripper in Fig 1.4 requires four changes of coordinates
to go from the base of the armto the gripper. The coordinate transformations
often have singularities, causing the equations to perform divide by zeros. It
can take a programmer weeks to reprogram a manipulator.
One simplifying solution is tomake the robot rigid at the desired velocities,
reducing the dynamics. This eliminates having to compute the terms for
overshooting and oscillating. However, a robot is made rigid by making it
heavier. The end result is that it is not uncommon for a 2 ton robot to be
able to handle only a 200 pound payload. Another simplifying solution is to
avoid the computations in the dynamics and kinematics and instead have the
programmer use a teach pendant. TEACH PENDANT Using a teach pendant (which often looks
like a joystick or computer game console), the programmer guides the robot
through the desired set of motions. The robot remembers these motions and
creates a program from them. Teach pendants do not mitigate the danger
of working around a 2 ton piece of equipment. Many programmers have to
direct the robot to perform delicate tasks, and have to get physically close
to the robot in order to see what the robot should do next. This puts the
programmer at risk of being hit by the robot should it hit a singularity point
in its joint configuration or if the programmer makes a mistake in directing
a motion. You don’t want to have your head next to a 2 ton robot arm if it
suddenly spins around!
AUTOMATIC GUIDED Automatic guided vehicles, or AGVs, are intended to be themost flexible con-
VEHICLES veyor system possible: a conveyor which doesn’t need a continuous belt or
roller table. Ideally an AGV would be able to pick up a bin of parts or manufactured
items and deliver them as needed. For example, an AGV might
receive a bin containing an assembled engine. It could then deliver it automatically
across the shop floor to the car assembly area which needed an
engine. As it returned, it might be diverted by the central computer and instructed
to pick up a defective part and take it to another area of the shop for
reworking.
However, navigation (as will be seen in Part II) is complex. The AGV has
to know where it is, plan a path from its current location to its goal destination,
and to avoid colliding with people, other AGVs, and maintenance
workers and tools cluttering the factory floor. This proved too difficult to do,
especially for factories with uneven lighting (which interferes with vision)
and lots of metal (which interferes with radio controllers and on-board radar
and sonar). Various solutions converged on creating a trail for the AGV to
follow. One method is to bury a magnetic wire in the floor for the AGV to
sense. Unfortunately, changing the path of an AGV required ripping up the
concrete floor. This didn’t help with the flexibility needs of modern manufacturing.
Another method is to put down a strip of photochemical tape for
the vehicle to follow. The strip is unfortunately vulnerable, both to wear and
to vandalism by unhappy workers. Regardless of the guidance method, in
the end the simplest way to thwart an AGV was to something on its path.
If the AGV did not have range sensors, then it would be unable to detect
an expensive piece of equipment or a person put deliberately in its path. A
few costly collisions would usually led to the AGV’s removal. If the AGV
did have range sensors, it would stop for anything. A well placed lunch box
could hold the AGV for hours until a manager happened to notice what was
going on. Even better from a disgruntled worker’s perspective, many AGVs
would make a loud noise to indicate the path was blocked. Imagine having
to constantly remove lunch boxes from the path of a dumb machine making
unpleasant siren noises.
From the first, robots in the workplace triggered a backlash. Many of the
human workers felt threatened by a potential loss of jobs, even though the
jobs being mechanized were often menial or dangerous. This was particularly
true of manufacturing facilities which were unionized. One engineer
reported that on the first day it was used in a hospital, a HelpMate Robotics
cart was discovered pushed down the stairs. Future models were modified
to have some mechanisms to prevent malicious acts.
Despite the emerging Luddite effect, industrial engineers in each of the
economic powers began working for BLACK FACTORY a black factory in the 1980’s. A black factory
is a factory that has no lights turned on because there are no workers.
Computers and robots were expected to allow complete automation of manufacturing
processes, and courses in “Computer-Integrated Manufacturing
Systems” became popular in engineering schools.
But two unanticipated trends undermined industrial robots in a way that
the Luddite movement could not. First, industrial engineers did not have
experience designing manufacturing plants with robots. Often industrial
manipulators were applied to the wrong application. One of the most embarrassing
examples was the IBM Lexington printer plant. The plant was
built with a high degree of automation, and the designers wrote numerous
articles on the exotic robot technology they had cleverly designed. Unfortunately,
IBM had grossly over-estimated the market for printers and the plant
sat mostly idle at a loss. While the plant’s failure wasn’t the fault of robotics,
per se, it did cause many manufacturers to have a negative view of automation
in general. The second trend was the changing world economy. Customers
were demanding “mass customization.” Manufacturers who could
make short runs of a product tailored to each customer on a large scale were
the ones making the money. (Mass customization is also referred to as “agile
manufacturing.”) However, the lack of adaptability and difficulties in programming
industrial robot arms and changing the paths of AGVs interfered
with rapid retooling. The lack of adaptability, combined with concerns over
worker safety and the Luddite effect, served to discourage companies from
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