LCL Robots
2021-2023, Germany
Soft Skills:
Project Management, Leadership, Cross-functional Team Collaboration, Problem-Solving, Industry Partnerships
Tools:
Microsoft Teams, Microsoft Office, GitLab, Trello, MIRO
Technical Skills:
CAD Modeling, Prototyping, Systems Design, Robotics Engineering, R&D
Tools:
Fusion 360, Solidworks, GitLab, ROS (Robot Operating System), ANSYS, MATLAB
Description
The Low-Cost-Lightweight (LCL) Robots on Demand funded by the Bavarian Ministry of Economic Affairs, Regional Development and Energy is an ambitious project that stands at the forefront of innovation in robotic systems design, pushing the boundaries of traditional methodologies. This initiative distinguishes itself by exploring a diverse range of architectural modalities, including the use of classical aluminium tubes, cutting-edge carbon fibre-reinforced polymers (CFRP), and various 3D printed materials to construct robotic links. The project's exploration extends to different methods of actuation, to investigate alternative technologies that could offer advantages in specific applications. The project's cornerstone is its novel end-to-end design process that leverages computational design and optimization to produce robots that are not only affordable but also customized to perform specific tasks with high efficiency. Unlike traditional robotics design, which often involves significant manual input and iteration, the LCL approach is distinguished by its use of advanced computational techniques to automate much of the design process.
A key aspect of the project is its commitment to capturing and analyzing environmental data to derive precise design requirements. This approach ensures that the resulting robot architectures are not only tailored to the tasks they will perform but are also optimized for the environments in which they will operate. The project employs novel methods to generate non-intuitive robot architecture designs, which are further refined through co-design optimizers that simultaneously consider control and mechanical aspects. This holistic approach to design results in robots that are optimized for both performance and control, leading to more efficient and effective robotic systems.
Results
The project has successfully demonstrated the feasibility of its design process by automatically generating robotic arm architectures that are task-specific and optimized for cost and weight. The use of modular components has been a game-changer, allowing for rapid assembly and iteration of design concepts. The project's approach to topology optimization has resulted in the creation of structural components that are not only lightweight but also robust enough to handle dynamic operational stresses. A practical testament to the project's success is the development of a robot capable of efficiently manipulating a 1 kg payload, which serves as a benchmark for the project's design capabilities. This achievement showcases the project's potential to make sophisticated robotics technology more accessible and adaptable to a wide range of applications.
Publications:
Anand Vazhapilli Sureshbabu, Sathuluri, Akhil, Jintin Frank, Maximilian Amm, and Markus Zimmermann. "Computational Systems Design of Low-Cost Lightweight Robots." Robotics 12, no. 4 (2023): 91.
Krischer, Lukas, Anand Vazhapilli Sureshbabu, and Markus Zimmermann. "Active-Learning Combined with Topology Optimization for Top-Down Design of Multi-Component Systems." Proceedings of the Design Society 2 (2022): 1629-1638.
