Camera-Based Wave Prediction and Calm Surface Detection Using Optical Flow and Machine Learning (+ Robotic Arm Control)

Individual Practical (6 ECTS)

Start Date: Flexible

Supervisor: Julian Rasch (julian.rasch ät um.ifi.lmu.de)

This project is a collaboration with a Munich-based artist Philip Gröning and part of a larger initiative.

Project Overview

The objective of this project is to create a camera-based system to predict wave movements and identify the calmest surface point on a defined, square water body. This system will employ computer vision techniques, specifically optical flow, to track wave motion across video frames. Machine learning models will be utilized to predict future wave behavior, consistently detecting regions with minimal motion, representing calm areas. The calmest point will serve as the primary output and will be forwarded to a 7-axis robotic arm. The project includes real-time video processing, optical flow analysis, and machine learning for wave pattern forecasting.

Project Objectives

• Capture video of water surfaces and process it using optical flow to detect wave motion.
• Analyze optical flow data to locate regions with the least movement (calm surface points).
• Utilize machine learning models to predict wave dynamics from historical wave movement patterns, identifying calm surface areas.
• Communicate the identified area to a 7-axis robotic arm for position alignment.
• Optional: Develop a real-time visualization tool to highlight calm areas on the water surface.

Expected Deliverables

• A fully functional system for detecting waves and calm surface regions using live camera footage.
• A machine learning model for wave dynamics prediction.
• ROS (Robot Operating System) communication functionality.
• Optional: A visualization tool for real-time water surface monitoring.

Required Skills & Knowledge

• Python Programming
• Computer Vision (e.g., OpenCV)
• Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Keras)
• Basic understanding of ROS

This project offers practical experience in computer vision and machine learning applied to a real-world problem, making it ideal for students interested in AI, robotics, environmental monitoring, and computational fluid dynamics. As part of a larger art project, an interest in the creative domain is beneficial but not mandatory.

Please send a brief motivation letter, CV, and transcript of records if you are interested in this project.