Building and Navigation Strategies for On-Site Robotics

Research Project 17-1 (RP 17-1)

IntCDC Research Project 17-1 – Building and Navigation Strategies for On-Site Robotics
BUILDING AND NAVIGATION STRATEGIES FOR ON-SITE ROBOTIC CONSTRUCTION

Building construction typically relies on a centralised design, where information is used to generate the most appropriate sequence of assembly actions, which is assumed to be the unambiguous blueprint for building execution. However, uncertainties in the actions which leading to tolerance violations, the true dimensions of the fabricated modules, and changes in the environment can require frequent replanning. The resulting costs can be prohibitive for construction projects that have reached a level of complexity that is increasingly difficult to plan. For similar reasons, on-site assembly is often reduced to purely reactive and manual operating sequences.

The central aim of this research project is to address the increasing complexity of the on-site construction of high-performance building systems through decentralised planning strategies that compute smart assembly sequences based on AI reasoning, thus enabling new processes in the research area of large-scale on-site robotic construction. This research project makes a significant contribution to the research network RN 2-1 as it is the only project within the cluster that focuses on high-level planning for building execution.

 

PRINCIPAL INVESTIGATORS

Prof. Dr. Marc Toussaint
Machine Learning and Robotics Lab (IPVS-MLR), University of Stuttgart
Prof. Dr.-Ing. Dr. h.c. Oliver Sawodny
Institute for System Dynamics (ISYS), University of Stuttgart

TEAM

Dr.-Ing. Salih Özgür Ögüz
Theresa Baumann (ISYS)
Valentin Noah Hartmann (IPVS-MLR)

 

PEER-REVIEWED PUBLICATIONS

  1. 2022

    1. Grothe, F., Hartmann, V. N., Orthey, A., & Toussaint, M. (2022). ST-RRT*: Asymptotically-Optimal Bidirectional Motion Planning through Space-Time. Proc. of the IEEE Int. Conf. on Robotics and Automation (ICRA).
    2. Hartmann, V. N., Strub, M. P., Toussaint, M., & Gammell, J. D. (2022). Effort Informed Roadmaps (EIRM*): Efficient asymptotically optimal multiquery planning by actively reusing validation effort. Proceedings of the International Symposium on Robotics Research (ISRR). https://doi.org/10.1007/978-3-031-25555-7_37
    3. Hartmann, V. N., Orthey, A., Driess, D., Oguz, O. S., & Toussaint, M. (2022). Long-Horizon Multi-Robot Rearrangement Planning for Construction Assembly. IEEE Transactions on Robotics, 1–14. https://doi.org/10.1109/TRO.2022.3198020

  2. 2021

    1. Ortiz-Haro, J., Hartmann, V. N., Oguz, O. S., & Toussaint, M. (2021). Learning Efficient Constraint Graph Sampling for Robotic Sequential Manipulation. 2021 IEEE International Conference on Robotics and Automation (ICRA), 4606–4612. https://doi.org/10.1109/ICRA48506.2021.9560978
    2. Thomas, M., Qiu, J., & Sawodny, O. (2021). Trajectory sequence generation and static obstacle avoidance for automatic positioning tasks with a tower crane. Proceedings of the 47th Annual Conference of the IEEE IES (IECON).

  3. 2020

    1. Hartmann, V. N., Oguz, O. S., & Toussaint, M. (2020). Planning Planning: The Path Planner as a Finite State Machine. Workshop on Planning and Robotics (PlanRob).
    2. Hartmann, V. N., Oguz, O. S., Driess, D., Toussaint, M., & Menges, A. (2020). Robust Task and Motion Planning for Long-Horizon Architectural Construction Planning. Proc. of the IEEE Int. Conf. on Intelligent Robots and Systems (IROS).

OTHER PUBLICATIONS

  1. 2023

    1. Hartmann, V. N., & Toussaint, M. (2023). Towards computing low-makespan solutions for multi-arm multi-task planning problems. https://doi.org/10.48550/arXiv.2305.17527

DATA SETS

        

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