| Type | Semester project |
| Split | 30% Theory, 20% Literature Review, 50% implementation |
| Knowledge | Programming skills in C++ or Python Experience in robotics, machine learning algorithms, and data analysis |
| Subjects | Trajectory planning, Human skill modeling, Visual servoing |
| Supervision | Rui Wu and Soheil Gholami |
| Published | 13.09.2024 |
Construction environments often expose workers to hazardous conditions, such as dust, and involve repetitive, physically demanding tasks that pose health risks. The integration of robots in tasks like shotcrete and surface finishing presents significant practical and social value, either by replacing or collaborating with human workers. This project focuses on designing and implementing a robotic system that autonomously executes shotcrete and surface finishing operations using visual-force feedback. The system will leverage visual-force feedback for real-time adjustments in trajectory planning and control, utilize machine learning algorithms for feedforward compensation in task execution, and improve the robotic arm’s performance through optimization algorithms.