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Advance Robotics Control
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Advance Robotics Control
Advance Robotics Control Duration: 40 hrs
Introduction to Robotics & Intelligent Systems
- Elements of robots — joints, links, actuators, and sensors
- Kinematics of serial robots
- Kinematics of serial robots
- Kinematics of parallel robots
- Velocity and statics of robot manipulators
- Dynamics of serial and parallel robots
- Motion planning and control
- Modeling and control of flexible robots
Probability & Statistic
- Probability in Robotics
- Uniform Distribution
- Normalize Distribution
- Phit and Pmiss
- Sum of Probabilities
- Sense Function & Move Function
- Exact Motion
- Bayes Rule
- Theorem of Total Probability
Kalman Filters
- Gaussian Intro
- Variance Comparison
- Maximize Gaussian
- Measurement and Motion
- Parameter Update
- New Mean Variance
- Gaussian Motion
- Lab: Kalman Filter Code
- Kalman Prediction
- Lab: Kalman Filter Design
- Kalman Matrices
Search Algorithms Techniques
- Motion Planning
- Compute Cost
- Optimal Path
- First Search Program
- Expansion Grid
- Dynamic Programming
- Lab: Computing Value
- Optimal Policy
PID Control
- The Feedback Control System
- P, PI, PID Control Introduction
- PID parameters
- Robot Motion
- Smoothing Algorithm
- Path Smoothing
- Zero Data Weight
- Lab: Implement P Controller
- Oscillations
- PD Controller
- Systematic Bias
- Lab: PID Implementation
- Parameter Optimization
SLAM (Simultaneous Localization and Mapping)
- Localization
- Planning
- Segmented Ste
- Fun with Parameters
- SLAM
- Graph SLAM
- Implementing Constraints
- Adding Landmarks
- Matrix Modification
- Untouched Fields
- Landmark Position
- Confident Measurements
- Lab: Implementing SLAM
Runaway Robot Final Project
