Laser-Based Perception with RANSAC Algorithm

• Objective: Implement perception using a laser range finder, applying the RANSAC algorithm to identify obstacles and generate lines visible to the robot.

• Implementation: Developed a custom ROS package named 'lab2' and organized world files in an appropriate sub-folder. Applied RANSAC algorithm iteratively to laser scans, determining inliers and outliers to identify obstacle lines.

• Visualization: Utilized rviz to visualize detected lines in the robot's local frame. Demonstrated correctness by overlapping published lines with laser scans.

• Challenges: Experimented with RANSAC parameters (iterations, inlier threshold, point threshold) for optimal results. Advised on setting up rviz, addressing potential issues with hardware limitations.

Bug2 Algorithm Implementation for Autonomous Navigation

• Objective: Implemented Bug2 algorithm for autonomous robot navigation from a starting point (-8.0, -2.0) to a goal point (4.5, 9.0), avoiding obstacles in the path.

• Implementation: Developed a launch file 'bug2.launch' to integrate world setup, perception node execution, and controller execution. Programmed the robot to switch between 'GOAL SEEK' and 'WALL FOLLOW' states, utilizing previously detected lines for parallel navigation.

• Visualization: Integrated visualization of the Bug2 algorithm's execution in rviz, providing a clear representation of the robot's decision-making process during navigation.

• Lessons Learned: Emphasized the significance of the WALL FOLLOW state and highlighted the pseudocode from class lectures as a key reference for the algorithm's implementation