End Effector Design Issues

Good end effector design is in many ways the same as good design of any mechanical device. Foremost, it requires:

• A formal understanding of the functional specifications and relevant constraints. In the authors, experience, most design “failures” occurred not through faulty engineering, but through incompletely articulated requirements and constraints. In other words, the end effector solved the wrong problem.

• A “concurrent engineering” approach in which such issues as ease of maintenance, as well as related problems in fixturing, robot programming, etc., are addressed in parallel with end effector design.

• An attention to details in which issues such as power requirements, impact resistance, and sensor signal routing are not left as an afterthought. Some of the main considerations are briefly discussed below.

Sensing

Sensors are vital for some manufacturing applications and useful in many others for detecting error
conditions. Virtually every end effector design can benefit from the addition of limit switches, proximity sensors, and force overload switches for detecting improperly grasped parts, dropped parts, excessive assembly forces, etc. robot controller . The most complex class of sensors includes cameras and tactile arrays. A number of commercial solutions for visual and tactile imaging are available, and may include dedicated microprocessors and software.

These binary sensors are inexpensive and easy to connect to most industrial controllers. The next level of sophistication includes analog sensors such as strain gages and thermocouples. For these sensors, a dedicated microprocessor as well as analog instrumentation is typically required to interpret the signals and communicate with the

Although vision systems are usually thought of as separate from end effector design, it is sometimes desirable to build a camera into the end effector; this approach can reduce cycle times because the robot does not have to deposit parts under a separate station for inspecting them.
 
 Actuation

The actuation of industrial end effectors is most commonly pneumatic, due to the  availability of
compressed air in most applications and the high power-to-weight ratio that can be obtained. The grasp force is controlled by regulating air pressure.  The chief drawbacks of pneumatic actuation are the difficulties in achieving precise position control for active hands (due primarily to the compressibility of air) and the need to run air lines down what is otherwise an all-electric robot arm. Electric motors are also common. In these, the grasp force is regulated via the motor current. A  variety of drive mechanisms can be employed between the motor or cylinder and the gripper jaws, including worm gears, rack and pinion, toggle linkages, and cams to achieve either uniform grasping forces or a self-locking effect. For a comparison of different actuation technologies, with emphasis on servo-controlled appli- cations, see Hollerbach et al. (1992).

Fundamentals and Design Issues

A robot manipulator is fundamentally a collection of links connected to each other by joints, typically with an end effector (designed to contact the environment in some useful fashion) connected to the mechanism. A typical arrangement is to have the links connected serially by the joints in an open-chain fashion. Each joint provides one or more degree of freedom to the mechanism.

 

Manipulator designs are typically characterized by the number of independent degrees of freedom in the mechanism, the types of joints providing the degrees of freedom, and the geometry of the links connecting the joints. The degrees of freedom can be revolute (relative rotational motion θ between joints) or prismatic (relative linear motion d between joints). A joint may have more than one degree of freedom. Most industrial robots have a total of six independent degrees of freedom. In addition, most current robots have essentially rigid links (we will focus on rigid-link robots throughout this section).

Robots are also characterized by the type of actuators employed. Typically manipulators have hydraulic or electric actuation. In some cases where high precision is not important, pneumatic actuators are used.

 A number of successful manipulator designs have emerged, each with a different arrangement of joints and links. Some “elbow” designs, such as the PUMA robots and the SPAR Remote Manipulator System, have a fairly anthropomorphic structure, with revolute joints arranged into “shoulder,” “elbow,” and “wrist” sections. A mix of revolute and prismatic joints has been adopted in the Stanford Manipulator and the SCARA types of arms. Other arms, such as those produced by IBM, feature prismatic joints for the “shoulder,” with a spherical wrist attached. In this case, the prismatic joints are essentially used as positioning devices, with the wrist used for fine motions.

The above designs have six or fewer degrees of freedom. More recent manipulators, such as those of the Robotics Research Corporation series of arms, feature seven or more degrees of freedom. These arms are termed kinematically redundant, which is a useful feature as we will see later .

Key factors that influence the design of a manipulator are the tractability of its geometric (kinematic) analysis and the size and location of its workspace. The workspace of a manipulator can be defined as the set of points that are reachable by the manipulator (with fixed base). Both shape and total volume are important. Manipulator designs such as the SCARA are useful for manufacturing since they have a simple semicylindrical connected volume for their workspace (Spong and Vidyasagar, 1989), which facilitates workcell design. Elbow manipulators tend to have a wider volume of workspace, however the workspace is often more difficult to characterize. The kinematic design of a manipulator can tailor the workspace to some extent to the operational requirements of the robot.

In addition, if a manipulator can be designed so that it has a simplified kinematic analysis, many planning and control functions will in turn be greatly simplified. For example, robots with spherical wrists tend to have much simpler inverse kinematics than those without this feature. Simplification of the kinematic analysis required for a robot can significantly enhance the real-time motion planning and control performance of the robot system. For the rest of this section, we will concentrate on the kinematics of manipulators.

 For the purposes of analysis, a set of joint variables (which may contain both revolute and prismatic variables), are augmented into a vector q, which uniquely defines the geometric state, or configuration of the robot. However, task description for manipulators is most naturally expressed in terms of a different set of task coordinates. These can be the position and orientation of the robot end effector, or of a special task frame, and are denoted here by Y. Thus Y most naturally represents the performance of a task, and q most naturally represents the mechanism used to perform the task. Each of the coordinate systems q and Y contains information critical to the understanding of the overall status of the manipulator. Much of the kinematic analysis of robots therefore centers on transformations between the various sets of coordinates of interest.

Manipulator Kinematics

The study of manipulator kinematics at the position (geometric) level separates naturally into two subproblems: (1) finding the position/orientation of the end effector, or task, frame, given the angles and/or displacements of the joints (Forward Kinematics); and (2) finding possible angles/displacements of the joints given the position/orientation of the end effector, or task, frame  (Inverse Kinematics). At the  velocity level, the Manipulator Jacobian  relates joint  velocities to end effector  velocities and is important in motion planning and for identifying Singularities. In the case of Redundant Manipulators, the Jacobian is particularly crucial in planning and controlling robot motions. We will explore each of these issues in turn in the following subsections.

Operating System for Embedded systems

Embedded systems can have anything between a complex real-time operating system, such as Linux, or just the application program with no operating system, whatsoever. It all depends on the intended application area. For the Eye- Con controller, we developed our own operating system RoBIOS (Robot Basic Input Output System), which is a very lean real-time operating system that provides a monitor program as user interface, system functions (including multithreading, semaphores, timers), plus a comprehensive device driver library for all kinds of robotics and embedded systems applications. This includes serial/parallel communication, DC motors, servos, various sensors, graphics/text output, and input buttons.

 The RoBIOS monitor program starts at power-up and provides a comprehensive control interface to download and run programs, load and store programs in flash-ROM, test system components, and to set a number of system parameters. An additional system component, independent of RoBIOS, is the Hardware Description Table (HDT, see Appendix C), which serves as a userconfigurable hardware abstraction layer [Kasper et al. 2000], [Bräunl 2001]. RoBIOS is a software package that resides in the flash-ROM of the controller and acts on the one hand as a basic multithreaded operating system and on the other hand as a large library of user functions and drivers to interface all on-board and off-board devices available for the EyeCon controller. RoBIOS offers a comprehensive user interface which will be displayed on the integrated LCD after start-up. Here the user can download, store, and execute programs, change system settings, and test any connected hardware that has been registered in the HDT

Pprogram are shown in photo. Hardware access from both the monitor program and the user program is through RoBIOS library functions. Also, the monitor program deals with downloading of application program files, storing/ retrieving programs to/from ROM, etc.

The RoBIOS operating system and the associated HDT both reside in the controller’s flash-ROM, but they come from separate binary files and can be downloaded independently. This allows updating of the RoBIOS operating system without having to reconfigure the HDT and vice versa. Together the two binaries occupy the first 128KB of the flash-ROM; the remaining 384KB are used to store up to three user programs with a maximum size of 128KB each

Since RoBIOS is continuously being enhanced and new features and drivers are being added, the growing RoBIOS image is stored in compressed form in ROM. User programs may also be compressed with utility srec2bin before downloading. At start-up, a bootstrap loader transfers the compressed RoBIOS
from ROM to an uncompressed version in RAM. In a similar way, RoBIOS unpacks each user program when copying from ROM to RAM before execution.

User programs and the operating system itself can run faster in RAM than in ROM, because of faster memory access times. Each operating system comprises machine-independent parts (for example
higher-level functions) and machine-dependent parts (for example device drivers for particular hardware components). Care has been taken to keep the machine-dependent part as small as possible, to be able to perform porting to a different hardware in the future at minimal cost.

Applications to Robot Control

Genetic algorithms to robot control are briefly discussed in the following sections. These topics are dealt with in more depth in the following chapters on behavior-based systems and gait evolution.

 Genetic algorithms have been applied to the evolution of neural controllers for robot locomotion by numerous researchers. This approach uses the genetic algorithm to evolve the weight- ings between interconnected neurons to construct a controller that achieves the desired gait. Neuron inputs are taken from various sensors on the robot, and the outputs of certain neurons are directly connected to the robot’s actuators. successfully generated gaits for a hexapod robot using a simple traditional genetic algorithm with one-point crossover and mutate. A simple neural network controller was used to control the robot, and the fitness of the individuals generated was evaluated by human designers. evolved a controller for a simulated salamander using an enhanced genetic algorithm. The neural model employed was biologically based and very complex. However, the system developed was capable of operating without human fitness evaluators.

Genetic algorithms have been used in a variety of different ways to newl produce or optimize existing behavioral controllers. used a genetic algorithm to control the weightings and internal parameters of a simple reactive schema controller. In schema-based control, primitive motor and perceptual schemas do simple distributed processing of inputs (taken from sensors or other schemas) to produce outputs. Motor schemas asynchronously receive input from perceptual schemas to produce response outputs intended to drive an actuator. A schema arbitration controller produces output by summing contributions from independent schema units, each contributing to the final output signal sent to the actuators according to a weighting. These weightings are usually manually tuned to produce desired system behavior from the robot.

The approach taken by Ram et al. was to use a genetic algorithm to determine an optimal set of schema weightings for a given fitness function. By tuning the parameters of the fitness function, robots optimized for the qualities of safety, speed, and path efficiency were produced. The behavior of each of these robots was different from any of the others. This graphically demonstrates how behavioral outcomes may be easily altered by simple changes in a fitness function.

Example Evolution

 

Harvey used a genetic algorithm to evolve a robot neural net controller to perform the tasks of wandering and maximizing the enclosed polygonal area of a path within a closed space. The controller used sensors as its inputs and was directly coupled to the driving mechanism of the robot. A similar approach was taken in Venkitachalam 2002 but the outputs of the neural network were used to control schema weightings. The neural network produces dynamic schema weightings in response to input from percep- tual schemas.

Analog versus Digital Sensors

A number of sensors produce analog output signals rather than digital signals. This means an A/D converter  is required to connect such a sensor to a microcontroller. Typical examples of such sensors are:
• Microphone
• Analog infrared distance sensor
• Analog compass
• Barometer sensor
Digital sensors on the other hand are usually more complex than analog sensors and often also more accurate. In some cases the same sensor is available in either analog or digital form, where the latter one is the identical analog sensor packaged with an A/D converter.

The output signal of digital sensors can have different forms. It can be a parallel interface (for example 8 or 16 digital output lines), a serial interface (for example following the RS232 standard) or a “synchronous serial” interface.

The expression “synchronous serial” means that the converted data value is read bit by bit from the sensor. After setting the chip-enable line for the sensor,the CPU sends pulses via the serial clock line and at the same time reads 1 bit of information from the sensor’s single bit output line for every pulse (for example on each rising edge). See photo below for an example of a sensor with a 6bit wide output word. 

 

What is Mechatronics System

Mechatronics is a methodology used for the optimal design of electromechanical products.

A methodology is a collection of practices, procedures, and rules used by those who work in a particular branch of knowledge or discipline. Familiar technological disciplines include thermodynamics, electrical engineering, computer science, and mechanical engineering, to name several. Instead of one, the mechatronic system is multidisciplinary, embodying four fundamental disciplines: electrical, mechanical, computer science, and information technology.

The F-35, a U.S. Department of defense joint strike fighter plane developed by Lockheed Martin Corporation, is an example of mechatronic technology in action. The design metric emphasizes reliability, maintainability, performance, and cost. Multidisciplinary functions, including the on-board prognostics for zero downtime and cockpit technology, are being designed into the aircraft starting at the preliminary design stage.

Multidisciplinary systems are not new. They have been successfully designed and used for many years. One of the most common is the electromechanical system, which often uses a computer algorithm to modify the behavior of a mechanical system. Electronics are used to transduce information between the computer science and mechanical disciplines.

The difference between a mechatronic system and a multidisciplinary system is not the constituents, but rather the order in which they are designed. Historically, multidisciplinary system design employed a sequential design-by-discipline approach. For example, the design of an electromechanical system is often accomplished in three steps, beginning with the mechanical design. When the mechanical design is complete, the power and microelectronics are designed, followed by the control algorithm design and implementation. The major drawback of the design-by-discipline approach is that, by fixing the design at various points in the sequence, new constraints are created and passed on to the next discipline. Many control system engineers
are familiar with the quip:

Design and build the mechanical system, then bring in the painters to paint it and the control system engineers to install the controls.

Control designs often are not efficient because of these additional constraints. For example, cost reduction is a major factor in most systems. Trade offs made during the mechanical and electrical design stages often involve sensors and actuators. Lowering the sensor–actuator count, using less accurate sensors, or using less powerful actuators, are some of the standard methods for achieving cost savings.

The mechatronic design methodology is based on a concurrent (instead of sequential) approach to discipline design, resulting in products with more synergy.

The branch of engineering called systems engineering uses a concurrent approach for preliminary design. In a way, mechatronics is an extension of the system engineering approach, but it is supplemented with information systems to guide the design and is applied at all stages of design—not just the preliminary design step—making it more comprehensive. There is a synergy in the integration of mechanical, electrical, and computer systems with information systems for the design and manufacture of products and processes. The synergy is generated by the right combination of parameters; the final product can be better than just the sum of its parts. Mechatronic products exhibit performance characteristics that were previously difficult to achieve without the synergistic combination. The key elements of the mechatronics approach are presented in Figure 1-1.

Even though the literature often adopts this concise representation, a clearer but more complex representation is shown in Figure 1-2. Mechatronics is the result of applying information systems to physical systems. The physical system (the rightmost dotted block of Figure 1-2) consists of mechanical, electrical, and computer systems as well as actuators, sensors, and real-time interfacing. In some of the literature, this block is called an electromechanical system.

FIGURE 1-1 MECHATRONICS CONSTITUENTS

FIGURE 1-2 MECHATRONICS KEY ELEMENTS

A mechatronic system is not an electromechanical system but is more than a control system.

Mechatronics is really nothing but good design practice. The basic idea is to apply new controls to extract new levels of performance from a mechanical device. Sensors and actuators are used to transduce energy from high power (usually the mechanical side) to low power (the electrical and computer side). The block labeled “Mechanical systems” frequently consists of more than just mechanical components and may include fluid, pneumatic, thermal, acoustic, chemical, and other disciplines as well. New developments in sensing technologies have emerged in response to the ever-increasing demand for solutions of specific monitoring applications. Microsensors are developed to sense the presence of physical, chemical, or biological quantities (such as temperature, pressure, sound, nuclear radiations, and chemical compositions). They are implemented in solid-state form so that several sensors can be integrated and their functions combined.

Control is a general term and can occur in living beings as well as machines. The term “Automatic control” describes the situation in which a machine is controlled by another machine. Irrespective of the application (such as industrial control, manufacturing, testing, or military), new developments in sensing technology are constantly emerging.

Mechatronics Systems