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What is Robotics

Robotics Technology

Sensors

Effectors

Actuators

Controllers

Arms

Artificial Intelligence

Mobility

Type of Robots

Robotics History

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Robotics Technology - Effectors

An effector is any device that affects the environment. Robots control their effectors, which are also known as end effectors. Effectors include legs, wheels, arms, fingers, wings and fins. Controllers cause the effectors to produce desired effects on the environment.  An actuator is the actual mechanism that enables the effector to execute an action. Actuators typically include electric motors, hydraulic or pneumatic cylinders, etc.  The terms effector and actuator are often used interchangeably to mean "whatever makes the robot take an action." This is not really proper use. Actuators and effectos are not the same thing. And we'll try to be more precise in the class.  Most simple actuators control a single degree of freedom, i.e., a single motion (e.g., up-down, left-right, in-out, etc.). A motor shaft controls one rotational degree of freedom, for example. A sliding part on a plotter controls one translational degree of freedom.  How many degrees of freedom (DOF) a robot has is going to be very important in determining how it can affect its world, and therefore how well, if at all, it can accomplish its task. Just as we said many times before that sensors must be matched to the robot's task, similarly, effectors must be well matched to the robot's task also.

    In general, a free body in space as 6 DOF: three for translation (x,y,z), and three for orientation/rotation (roll, pitch, and yaw). We'll go back to DOF in a bit. You need to know, for a given effector (and actuator/s), how many DOF are available to the robot, as well as how many total DOF any given robot has. If there is an actuator for every DOF, then all of the DOF are controllable. Usually not all DOF are controllable, which makes robot control harder.  A car has 3 DOF: position (x,y) and orientation (theta). But only 2 DOF are controllable: driving: through the gas pedal and the forward-reverse gear; steering: through the steering wheel. Since there are more DOF than are controllable, there are motions that cannot be done, like moving sideways (that's why parallel parking is hard).  We need to make a distinction between what an actuator does (e.g., pushing the gas pedal) and what the robot does as a result (moving forward). A car can get to any 2D position but it may have to follow a very complicated trajectory. Parallel parking requires a discontinuous trajectory w.r.t. velocity, i.e., the car has to stop and go.  When the number of controllable DOF is equal to the total number of DOF on a robot, it is holonomic(for more information about holonomic). If the number of controllable DOF is smaller than total DOF, the robot is non-holonomic. If the number of controllable DOF is larger than the total DOF, the robot is redundant.  A human arm has 7 DOF (3 in the shoulder, 1 in the elbow, 3 in the wrist), all of which can be controlled. A free object in 3D space (e.g., the hand, the finger tip) can have at most 6 DOF! So there are redundant ways of putting the hand at a particular position in 3D space. This is the core of why manipulations is very hard!

Two basic ways of using effectors:

  • to move the robot around =>locomotion

  • to move other object around =>manipulation

These divide robotics into two mostly separate categories:

  • mobile robotics

  • manipulator robotics

Mobility end effectors are discussed in more detail in the mobility section of this web site.

In contrast to locomotion, where the body of the robot is moved to get to a particular position and orientation, a manipulator moves itself typically to get the end effector (e.g., the hand, the finger, the fingertip) to the desired 3D position and orientation. So imagine having to touch a specific point in 3D space with the tip of your index finger; that's what a typical manipulator has to do.  Of course, largely manipulators need to grasp and move objects, but those tasks are extensions of the basic reaching above. The challenge is to get there efficiently and safely. Because the end effector is attached to the whole arm, we have to worry about the whole arm; the arm must move so that it does not try to violate its own joint limits and it must not hit itself or the rest of the robot, or any other obstacles in the environment.  Thus, doing autonomous manipulation is very challenging. Manipulation was first used in tele-operation, where human operators would move artificial arms to handle hazardous materials. It turned out that it was quite difficult for human operators to learn how to tele-operate complicated arms (such as duplicates of human arms, with 7 DOF). One alternative today is to put the human arm into an exo-skeleton (see lecture 1), in order to make the control more direct. Using joy-sticks, for example, is much harder for high DOF.  Why is this so hard? Because even as we saw with locomotion, there is typically no direct and obvious link between what the effector needs to do in physical space and what the actuator does to move it. In general, the correspondence between actuator motion and the resulting effector motion is called kinematics. In order to control a manipulator, we have to know its kinematics (what is attached to what, how many joints there are, how many DOF for each joint, etc.). We can formalize all of this mathematically, and get an equation which will tell us how to convert from, say, angles in each of the joints, to the Cartesian positions of the end effector/point. This conversion from one to the other is called computing the manipulator kinematics and inverse kinematics.

    The process of converting the Cartesian (x,y,z) position into a set of joint angles for the arm (thetas) is called inverse kinematics. Kinematics are the rules of what is attached to what, the body structure. Inverse kinematics is computationally intense. And the problem is even harder if the manipulator (the arm) is redundant.

Manipulation involves

  • trajectory planning (over time)

  • inverse kinematics

  • inverse dynamics

  • dealing with redundancy

      Manipulators are effectors. Joints connect parts of manipulators. The most common joint types are:

  • rotary (rotation around a fixed axis)

  • prismatic (linear movement)

These joints provide the DOF for an effector, so they are planned carefully.

Robot manipulators can have one or more of each of those joints. Now recall that any free body has 6 DOF; that means in order to get the robot's end effector to an arbitrary position and orientation, the robot requires a minimum of 6 joints.  As it turns out, the human arm (not counting the hand!) has 7 DOF. That's sufficient for reaching any point with the hand, and it is also redundant, meaning that there are multiple ways in which any point can be reached. This is good news and bad news; the fact that there are multiple solutions means that there is a larger space to search through to find the best solution.  Now consider end effectors. They can be simple pointers (i.e., a stick), simple 2D grippers, screwdrivers for attaching tools (like welding guns, sprayer, etc.), or can be as complex as the human hand, with variable numbers of fingers and joints in the fingers.   Problems like reaching and grasping in manipulation constitute entire subareas of robotics and AI. Issues include: finding grasp-points (COG, friction, etc.); force/strength of grasp; compliance (e.g., in sliding, maintaining contact with a surface); dynamic tasks (e.g., juggling, catching). Other types of manipulation, such as carefully controlling force, as in grasping fragile objects and maintaining contact with a surface (so-called compliant motion), are also being actively researched. Finally, dynamic manipulation tasks, such as juggling, throwing, catching, etc., are already being demonstrated on robot arms.

    Having talked about navigation and manipulation, think about what types of sensors (external and proprioceptive) would be useful for these general robotic tasks. Proprioceptive sensors sense the robot's actuators (e.g., shaft encoders, joint angle sensors, etc.); they sense the robot's own movements. You can think of them as perceiving internal state instead of external state. External sensors are helpful but not necessary or as commonly used.

 


   

 

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