Master thesis: Comparative Analysis of Control Strategies for Dual Serial Robot Systems in Object Handling Tasks

Project Description:

This project aims to develop a measurable mechanism for comparing various control strategies employed in the context of dual serial robot systems for object-handling tasks. It focuses on designing, implementing, and evaluating a range of control strategies, including traditional methods such as PID and Adaptive Control, as well as newer machine learning algorithms. Through comprehensive comparative analyses, this research endeavors to identify the most suitable control strategy for specific object-handling scenarios. By incorporating a diverse set of control algorithms, the project aims to assess their performance in terms of accuracy, efficiency, robustness, adaptability, and ease of implementation. Additionally, both simulation-based and experimental evaluations will be conducted to provide comprehensive insights into the strengths and limitations of each algorithm in real-world scenarios.

Tasks and Duties:

o   Literature Review:

·       Conduct an extensive review of existing literature on dual serial robot systems, control strategies, and machine learning algorithms applied to robotics.

o   System Design:

·       Design a simulation environment and experimental setup for implementing dual serial robot systems capable of object handling tasks, using ROS.

o   Control Strategy Development:

·       Develop and implement a range of control strategies, including traditional methods like PID and Adaptive Control, as well as machine learning algorithms such as neural networks or reinforcement learning.

o   Performance Evaluation:

·       Design a comprehensive set of performance metrics to objectively evaluate the effectiveness and efficiency of each control strategy, considering factors such as accuracy, efficiency, robustness, adaptability, and ease of implementation.

o   Comparative Analysis:

·       Conduct thorough comparative analyses to identify the most suitable control strategy for specific object handling scenarios, taking into account the performance metrics.

o   Documentation and Reporting:

·       Document the design process and experimental findings, Prepare presentation.

Requirements:

o   Strong background in robotics and control systems.

o   Proficiency in programming languages such as MATLAB, Python, or C++.

o   Familiarity with simulation software like Gazebo, or URSIM.

o   Knowledge of traditional control methods such as PID and Adaptive Control.

o   Interested in machine learning algorithms.

o   Good analytical and problem-solving skills.

Contact Person: Dr. Mohammad Sadeghi (mohammad.sadeghi@tuhh.de)

Desired starting date: As Soon As Possible