ROBOTICS CHAPTER 25 Chapter 25 1
Robotics Chapter 25 Chapter 25 1
Outline Robots,Effectors,and Sensors Localization and Mapping Motion Planning Motor Control Chapter 25 2
Outline Robots, Effectors, and Sensors Localization and Mapping Motion Planning Motor Control Chapter 25 2
Mobile Robots Chapter 25 3
Mobile Robots Chapter 25 3
Manipulators P Configuration of robot specified by 6 numbers 6 degrees of freedom (DOF) 6 is the minimum number required to position end-effector arbitrarily. For dynamical systems,add velocity for each DOF Chapter 25 4
Manipulators R R R P R R Configuration of robot specified by 6 numbers ⇒ 6 degrees of freedom (DOF) 6 is the minimum number required to position end-effector arbitrarily. For dynamical systems, add velocity for each DOF. Chapter 25 4
Non-holonomic robots (, A car has more DOF(3)than controls(2),so is non-holonomic; cannot generally transition between two infinitesimally close configurations Chapter 25 5
Non-holonomic robots θ (x, y) A car has more DOF (3) than controls (2), so is non-holonomic; cannot generally transition between two infinitesimally close configurations Chapter 25 5
Sensors Range finders:sonar(land,underwater),laser range finder,radar(aircraft), tactile sensors,GPS SICK Imaging sensors:cameras (visual,infrared) Proprioceptive sensors:shaft decoders (joints,wheels),inertial sensors, force sensors,torque sensors Chapter 25 6
Sensors Range finders: sonar (land, underwater), laser range finder, radar (aircraft), tactile sensors, GPS Imaging sensors: cameras (visual, infrared) Proprioceptive sensors: shaft decoders (joints, wheels), inertial sensors, force sensors, torque sensors Chapter 25 6
Localization-Where Am I? Compute current location and orientation (pose)given observations: A-2 Z Chapter 25 7
Localization—Where Am I? Compute current location and orientation (pose) given observations: Xt Xt+1 At−2 At−1 At Zt−1 Xt−1 Zt Zt+1 Chapter 25 7
Localization contd. 0,△ 61+1 h(x) Al +1 Assume Gaussian noise in motion prediction,sensor range measurements Chapter 25 8
Localization contd. xi, yi vt ∆t t ∆t t+1 xt+1 h(xt) xt θt θ ω Z1 Z2 Z3 Z4 Assume Gaussian noise in motion prediction, sensor range measurements Chapter 25 8
Localization contd. Can use particle filtering to produce approximate position estimate Chapter 25 9
Localization contd. Can use particle filtering to produce approximate position estimate Robot position Robot position Robot position Chapter 25 9
Localization contd. Can also use extended Kalman filter for simple cases: :+40民合0 landmark Assumes that landmarks are identifiable-otherwise,posterior is multimodal Chapter 25 10
Localization contd. Can also use extended Kalman filter for simple cases: robot landmark Assumes that landmarks are identifiable—otherwise, posterior is multimodal Chapter 25 10