Transcript 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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - robot manipolatori
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
Programma - esercitazione
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
The robot, equipped with a
camera, must perform a path
start
stop
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - esercitazione
bypassing an unexpected
obstacle
SENSOR FUSION - Laser and Camera
Camera
Laser Range Finder
 direct depth measurement
 illumination dependent
 wide accuracy span (till 200 m)
 accurate only for limited distances
 only 2 or 3 D contour
 info on colour and texture
 high computational time
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - LASER + CAMERA
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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - LASER + CAMERA
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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - esercitazione
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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - sensor fusion + esercitazione
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - sensor fusion + tesina
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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - sensor fusion + tesina
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
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Programma - robot mobili
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
Modalità di esame
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)
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion
Testi Consigliati
VIDEO
M. De Cecco - Lucidi del corso di Robotica e Sensor Fusion