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Robotics 101

My Robotics 101 course terms

TermDefinition
Intelligent Agent An entity which observes through sensors and acts upon an environment using actuators.
The 4 D's We use robots for missions that are Dirty, Dangerous, Distant and Dull.
The 3T Architecture Planner <-> Execution <-> Control and Sensing affects them all.
Planner High level mission planner that plans as a response to Execution request.
Execution Manages task execution. Ask for a plan from the planner, decomposes into primitive actions for the motors, decides what to do next. Sends primitive actions to motors, and receives success/failure.
Control Low level control. Responsible for activation of motors.
Remote Control A human operator who sees the robot and the environment from own eyes.
Teleoperation A human operator who sees the world from the eyes of the robot.
Extereoceptive Sensor A device that is being used to monitor the relationship between the robot kinematics and/or dynamics with its task, surrounding, or the object being manipulated.
Proprioceptive Sensor A device that is being used to measure the position, velocity and acceleration of the robot joint and/or end-effector.
Vision Sensor challenges Lightning changes Scale Moving from 3D to 2D creates loss of information Noise
Laser Scanners (LiDar) Distance sensors that use the same principle as ultrasonic distance sensors, but use light instead of sound. A LiDar sends a laser pulse in a narrow beam towards the object.
Laser Sensors Pros Narrow beam width, higher resolution Not effected by temperature or vacuum Greater accuracy and faster response
Laser Sensors Cons Costly Limited in range in various applications or not safe in others Relatively heavy Works badly in poor visibility and underwater
Kalman Filter Compare where we think we should be, to where our sensors tell us we are, by taking a weighted average of the two, based on their covariance, as the new state.
Holonomic Robots Robots that can move in any direction
Non-Holonomic Robots Robots that are constrained to certain types of movement
Feedback Control Check where located and where should be, calculate the error and take action to minimize the error.
Goal Achievement Once reaching a goal state, job is done.
Goal Maintenance Ongoing active work of staying in the goal state.
Bang-bang Control Simple on/off switch. When error is negative, on When error is positive, off Good for reaching goal, not good for keeping it.
Proportional (P) Control Signal becomes proportional to the error. Output as a function of input and constant. o = K_p * i The larger the error, the greater magnitude of response.
Proportional Derivative (PD) Control Like P control, but taking into account how fast approaching the setpoint o = K_d(1 + (curr_measure - prev_measure)) Commonly used as low-level controller
Integral Control Keep track of steady state errors o = K_f * integ(i(t)dt)
PID Control o = K_p * i + K_d * di / dt + K_f * integ(i(t)dt) It's impractical if the process is complex.
Feedforward Control Does not use sensor input, but it predicts system state. Works only for well calibrated systems.
Sample-based planning Exploring the state space using sample points. The points are acquired by drawing randomly a point in the environment, and connecting it to the closest point(s) in the tree.
Coverage Finding a path for the robot(s), such that each point in the area is visited once [in minimal time].
Dead Reckoning The process of calculating one's current position by using a previously determined position or fix, and advancing that position based upon known estimated speeds over elapsed time and course.
Odometry The use of data from motion sensors to estimate change in position over time.
Separation Bearing Control (SBC) The robot position itself against a single robot
Separation Separation Control (SSC) The robot position itself against several robots
Task Specific Planning Theme For world representation we use something known, the algorithm takes most of the effort, and if the modeling was correct, path tracking is trivial.
General Planning Theme The world representation takes most of the effort, for the algorithm we use something known, and if the modeling was correct, path tracking is trivial.
BDI Architecture Methodology for implementation of practical reasoning agents.
BDI Components Beliefs - knowledge base; Plan library; Goals - a state(s) of the world, the agent wants to achieve; Intentions - an option (a plan) the agent committed to achieve; Options - plans the agent might perform given its beliefs and its previous intentions
Created by: yonicohen97
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