Self Driving and ROS 2 - Odometry & Control

Intelligent Self-Driving System Development

🚀 Goal

Develop an intelligent self-driving system for a robot by implementing core concepts in motion control, odometry computation, and sensor fusion.

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📌 Objectives and Study Areas

1. Differential Kinematics

• Objective: Study the motion of a self-driving robot.
• Implementation: Develop equations to translate joystick velocity commands into well-controlled motion commands.
• Significance: Differential kinematics is the foundation for all robotic movements and is essential for executing precise maneuvers.

2. Odometry Computation

• Objective: Estimate the robot's motion using feedback from sensors.
• Implementation: Use data from encoder sensors (which convert wheel rotation into digital signals) to calculate the robot's overall movement.
• Significance: Odometry is crucial for tracking the robot's position during its movements.

3. Sensor Fusion

• Objective: Improve the robot's odometry and movement accuracy.
• Implementation:
  • Fuse data from multiple sensors, including encoders, accelerometers, and gyroscopes.
  • Address the issue of sensor noise, which can reduce accuracy.
  • Implement a Kalman Filter to mitigate the effects of sensor noise and enhance motion estimation.
• Significance: Sensor fusion provides more reliable and accurate positioning by combining multiple data sources.

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