課程概述 |
1. Fundamentals to Robotics
2. Sensor Technologies
Classification of sensors:
-- Active Sensor: An active sensor has a physical input, an electrical
output, and an electrical excitation input (i. e., three energy ports) Examples: electromechanical element, photoelectric element, piezoelectric element and thermoelectric element
-- Passive sensor: A passive, or self-generating, sensor is one which has an input and an output (i.e., two energy ports) Examples: capacitate element, inductive element and potentiometer element.
Sensor characterization:
-- Detection means of sensors:
Biological, Chemical, Electric, Magnetic, or Electromagnetic wave, Heat, Temperature etc.
Conversion phenomena of sensors:
Thermoelectric, Photoelectric, Photomagnetic, Magnetoelectric
Elastomagnetic, Thermoelastic, Elastoelectric
Thermomagnetic ,Thermo-optic, Photoelastic, etc
Technological aspect of sensors:
Ambient conditions allowed, Full-scale output, Hysteresis, Linearity, Measured range, Offset, Operating life, Overload characteristics, Repeatability, Resolution, Selectivity, Sensitivity, Speed of response, Stability, Others
Fundamental circuit of sensors:
3. Robot Sensors
- Force and Tactile Sensors: Sensor type, Tactile information processing, Integration challenges
- Inertial Sensors, GPS, and Odometry
- Sonar Sensors: Sonar Principles, Waveforms, Time of flight ranging,Sonar rings
- Range Sensors: Range sensing basics, registration, navigation
- 3-D Vision and Recognition: Visual SLAM (simultaneous localization and
mapping). Recognition
4. Multisensor Data Fusion and Integration:
- Multisensor fusion methods, Multisensor fusion and integration architectures,
Various multisensor fusion and integration applications
5. Robot Control:
- Principles of robot control, Category of robot control, Joint space versus task
space control, The basic components of visual servo control, Image based visual servo control,Position based visual servo control and target tracking servo control
6. Practical exampl |
課程目標 |
The objectives of this course are to let student understand the robot sensing and control issues, approaches and its practical applications in robot sensing and control.The idea of this course is to convey the concept that usually sensing and control should not be seperated and they are interdependent in dealing with an intelligent systems, such as an intelligent robotics system. Firstly, student will learn fundamental issues of robotics, including introduction to robotics, the need for robot sensors interact with different control aspects. The second focus will be the study of principles of sensor technologies and robot sensors. In addition, synergistic use of multiple sensors by machines and systems enables greater intelligence to be incorporated into their overall operation. Motivation for using multiple sensors can be considered as response to simple question: if a single sensor can increase the capability of a system, would the use of more sensors increase it even further ? In this course, theories of multisensory fusion and its applications to sensory controlled robotics systems which involves mathematical and statistical issues including combining sensor uncertainty methods for sensor fusion includes estimation methods, such as covariance Intersection (CI), Kalman Filtering; Classification methods, such as Support Vector Machine (SVM) etc. will be presented and discussed. The third focus will be the robot control issues. Student will learn control theories in robotics including joint space versus task space control, visual servo control and targettracking servo control etc. Finally, a variety of practical examples of robot sensing and control will be presented through photos and video demonstrations. After taking this course,it is expected that students will have the good knowledge about the core robotics technologies especially in robot sensing and control. |