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# M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion - INTRODUCTION TO ROBOTICS 1. Rotation matrices, solution to the direct kinematics solution.

## Presentation on theme: "M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion - INTRODUCTION TO ROBOTICS 1. Rotation matrices, solution to the direct kinematics solution."— Presentation transcript:

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion - INTRODUCTION TO ROBOTICS 1. Rotation matrices, solution to the direct kinematics solution using geometric and systematic methodologies; tutorial on planar manipulator with two arms 2. Conventions Denavit Hartemberg; inverse kinematics 3. manipulator calibration and uncertainty vector 4. inverse kinematics 5. kinematic differential (analysis of singularities) 6. kinematic differential (redundancy analysis and reverse kinematics) 7. reverse kinematics 8. Introduction to simulation using Simulink. Practice in the classroom by numerical simulation Programma - robot manipolatori

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion Programma - esercitazione In the laboratory of mechatronics is an industrial SCARA robot manipulator with 4 axles... Why not use it for exercises during the course? In 1981, Sankyo Seiki of NEC presented a completely new concept for assembly robots. The robot was called Selective Compliance Assembly Robot Arm, SCARA. Its arm was rigid in Z-axis and flexible in XY-axes, Which allowed it to Adapt to holes in the XY-axes.

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion LABVIEW general concepts 1 LABVIEW general concepts 2 LABVIEW simulation application for obstacle avoidance (using a simulator and the kinematics of the robot that the view with the camera) LABVIEW simulation application for obstacle avoidance LABVIEW verification of laboratory robots start stop The robot, equipped with a camera, must perform a path bypassing an unexpected obstacle Programma - esercitazione

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion Laser Range Finder Camera direct depth measurement direct depth measurement wide accuracy span (till 200 m) wide accuracy span (till 200 m) only 2 or 3 D contour only 2 or 3 D contour illumination dependent illumination dependent accurate only for limited distances accurate only for limited distances info on colour and texture info on colour and texture high computational time high computational time SENSOR FUSION - Laser and Camera Programma - LASER + CAMERA

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion MEASUREMENT BY LASER and CAMERA Laser rangefinders, principles and applications Laser-Camera Calibration MEASUREMENT BY LASER and CAMERA: object recognition Clustering and segmentation of the scene seen by the laser Chamfer distance (or Hausdorff) MEASUREMENT BY LASER and CAMERA: object recognition reprojection of the object model of CCD Corner extraction Matching and acceptance Programma - LASER + CAMERA

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion MEASUREMENT BY LASER and CAMERA: object recognition Practice with real data. The scene will be a box of given size to be recognized MEASUREMENT BY LASER and CAMERA: object recognition Practice with real data. SUPERQUADRICHE General concepts SUPERQUADRICHE Application to object recognition Programma - esercitazione

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion SENSOR FUSION of timeline signals - Complementary Filtering. Theory and applications. Example of simulation of an altimeter baro-inertial. SENSOR FUSION of timeline signals - Simulation PC in the classroom portion of the estimate by filtering between a barometer and an inertial platform SENSOR FUSION of timeline signals - Development of real data: - Measurement of the camera position by means of an object in motion on a plane by means of KLT, after having calibrated the worktop (using a grid placed on the floor) - Combined with the accelerometer data and complementary filtering Telecamera + oggetto sul piano con accelerometro solidale Programma - sensor fusion + esercitazione

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion Programma - sensor fusion + tesina

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion SENSOR FUSION - Statistical concepts accessories, Bayes' Theorem SENSOR FUSION - Application of Bayes' theorem to the fusion of information scalar and vector SENSOR FUSION - Kalman Filter SENSOR FUSION. Tutorial SLAM + Kalman. Mapping with laser scanner or camera SENSOR FUSION. Tutorial SLAM + Kalman. Mapping with laser scanner or camera Programma - sensor fusion + tesina

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion MOBILE ROBOT - Overview of applications. Localization issues, planning and control, holonomic and non-linear differential constraints. Conditions of integrability, Model Differential Drive. Recursive equations for odometry. MOBILE ROBOT - Models kinematic unicycle, bicycle and bicycle trailers with N MOBILE ROBOT - Problem of planning. Classification. Transformation of kinematic models in chained form. MOBILE ROBOT - Planning open-loop. Systems in chained form for the solution of the motion point-to-point with sinusoidal input, wise constant, polynomial. Calculation of Cartesian trajectories eligible MOBILE ROBOT - Planning open-loop. Clothoids and polar spline. Examples of calculation. MOBILE ROBOT - Controllability of systems that are not holonomic. Example of control system in chained form linearized around the desired trajectory Programma - robot mobili

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion Modalità di esame Exam: homework + 1 ORAL ARGUMENTS ON 2 CHOICES (between 4 topics, which does not coincide with that of the homework), 1 topic for mehanics area Homework chose examples : trajectory control of manipulators by inverting the differential kinematics (CLASS) simulation and trajectory control for non-holonomic vehicles processing data for the calibration kinematics of an autonomous vehicle AGV SLAM using a laser scanner at 360 °

M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion L.Sciavicco, B. Siciliano, Robotica - Modellistica, pianificazione e controllo 3/ed, McGraw Mitchell Harvey, "Multi-Sensor Data Fusion: An Introduction" - Springer 2007 Ake Bjork, Numerical methods for least squares problems M. De Cecco, Lucidi del corso di Robotica e Sensor Fusion Luca Baglivo, M. De Cecco, Navigazione di Veicoli Autonomi - Fondamenti di sensor fusion per la localizzazione L. Baglivo, Navigazione di Veicoli Autonomi (Localizzazione, Pianificazione e Controllo traiettoria) Testi Consigliati

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