Successful early career researchers typically show high intrinsic motivation and identification with their academic work. In order to encourage and honor these attributes, the Cluster of Excellence IntCDC has established several early career grants.
IntCDC BEST PUBLICATION AWARD
The IntCDC Best Publication Award recognizes up two excellent publications per year to honor and strengthen early academic independence and dedication to academic work.
All publications authored or co-authored by IntCDC early career researchers, published or formally accepted to be published, are eligible for the award.
IntCDC “BLUE SKY” PROJECT GRANT
The IntCDC “Blue Sky” Project Grant is awarded annually in recognition of a research idea that stands out in terms of its originality, its quality and the readiness to assume risk. The grant is endowed with 10.000€ enabling the grant recipient to test the feasibility of their research idea in preparation for a full dissertation or postdoctoral project.
All IntCDC early career researchers as well as advanced master’s students of the institutes involved in IntCDC are eligible to apply.
IntCDC MASTER’S THESIS GRANTS
The IntCDC Master’s Thesis Grant aims to encourage excellent master’s students to pursue an academic career by supporting independent master’s thesis research. We award up to two IntCDC Master’s Thesis Grants per year.
All master’s students who are writing their master’s theses at one of the institutes involved in IntCDC are eligible. Candidates must be nominated by their thesis advisors.
GRANT WINNERS 2020
Joint Effort: Robot team enabled carbon fibre joining strategies for lightweight wood construction
Simon Lut, Lasath Siriwardena und Tim Stark
Supervisors: Achim Menges, Jan Knippers
Tutors: Hans Jakob Wagner, Simon Bechert, Mathias Maierhofer
Collective robotic construction is a contemporary research field in which multi-robot systems modify their shared environments to materialize structures. Current research is primarily focussed on the positioning of elements and tends to disregard connection strategies, limiting scalability and structural viability of autonomously built structures. This study demonstrates methods by which a heterogeneous team of robots connects discrete timber elements by winding carbon fibre through pre-routed grooves to establish a structurally performant joint. In contrast to current human-centric steel fasteners, CFRPs are flexible, compact and can be easily integrated into mobile robots, enabling the exploration of novel robot-orientated connection typologies. By regarding the timber as an integral part of the robotic system, assembly information is pre-programmed into the material, including instructions for navigation, localization and construction. This substantially reduces robot complexity, weight, size, cost and allows for decentralized control of the connection agents. Through cooperation between different robotic and material species, a fully autonomous assembly choreography can be performed, leveraging the task-specific capabilities of each agent in the team. This building framework demonstrates the utility of heterogeneous robot teams in facilitating novel construction methods that could eventually mount a challenge to the reliance on existing humancentric connection strategies in timber assemblies.
Working with Uncertainties: An adaptive fabrication system for bamboo structures utilizing computer vision
Yue Qi und Ruqing Zhong
Supervisors: Achim Menges, Alexander Verl
Tutors: Hans Jakob Wagner, Yasaman Tahouni, Benjamin Kaiser
The group investigates an adaptive fabrication system that is able to work with cumulative deviations. During fabrication, the deviation can be caused by material- and fabrication-related uncertainties. All these factors exist in the bamboo constructions significantly, which limits the application of this high performance and sustainable material. To address the challenge, the proposed method leverages vision-based feedback to update future fabrication instructions and provide guidance for manual assembly, thereby compensating for the error in every iteration of the building process. This workflow effectively improves the accuracy of manually fabricated structure with natural imperfect material, allowing it to predictably interface with prefabricated building components.