Mark Burkhardt defended his doctoral dissertation

Automatisierung eines Turmdrehkrans mit Endeffektor für Handhabungs- und Positionieraufgaben (Automation of a Tower Crane with End Effector for Handling and Positioning Tasks)

On 28 July 2025 Mark Burkhardt defended his doctoral research titled "Automatisierung eines Turmdrehkrans mit Endeffektor für Handhabungs- und Positionieraufgaben (Automation of a Tower Crane with End Effector for Handling and Positioning Tasks)" in front of the doctoral committee. The doctoral committee consisted of Univ.-Prof. Dr.-Ing. Robert Schulz (IFT) as chair, Prof. Dr.-Ing. Dr. h.c. Oliver Sawodny (ISYS) as supervisor and first examiner, and Prof. Dr.-Ing. Christoph Ament (chair Regelungstechnik, Uni Regensburg) as second examiner.

© IntCDC / Foto: Tampe-Mai

Abstract of Doctoral Research

While production output on construction sites is stagnating, the demand for building new housing and improving infrastructure is increasing. One reason for this is the lack of automation and digitalization on construction sites. Improving the operational efficiency of tower cranes is a key aspect in this context. While research into tower cranes has been limited for decades to the development of assistance systems for crane operators, full automation offers opportunities to further improve safety and handling performance. For this reason, this dissertation investigates automation functions that enable the fully automated pick-up and deposit of payloads with subsequent automated transportation for a tower crane with a sway damping assistance system and a novel end effector.

In order to achieve a collision-free payload transport from the starting point to the destination in the local coordinate system of the construction site, the tower crane control is extended by a path planning module and a trajectory generation module. The path planning is based on an environment model reconstructed from camera data in which optimal paths are computed using a graph-based search algorithm. To calculate a path that compensates for tracking errors, path deviations are predicted using a data-based regression model. The trajectory generation algorithm addresses the specific case in which the payload moves along a sequence of straight-line segments connecting waypoints. At the intersection points of these segments, time-efficient smoothing of the trajectory is achieved by repeatedly solving the optimization problem for the subsequent segment. The automation of payload pick-up and deposit is implemented using a newly developed end effector. The orientation of the end effector is controlled through an adaptive two-degree-of-freedom structure that estimates both the unknown moment of inertia and the center of gravity of the load in real time. The automation functions are validated on the test crane. Based on the functionality of the end effector’s pick-up mechanism, a camera-based positioning approach for the end effector and a superimposed feed-forward control concept for the pick-up and deposit motion are derived in the final step.

The thesis is available here
The chapter overview is available here

Papers of which the dissertation consisted 

1. Burkhardt, M. and Sawodny, O. (2020). A graph-based path planning algorithm for the control of tower cranes. In 2020 IEEE American Control Conference (ACC), pp. 1-6. doi:10.23919/ACC50511.2021.9482797 (Link)
2. Burkhardt, M. and Sawodny, O. (2021). Towards Modeling and Control of a Crane-Collaboration for the Automated Assembly of Timber Structures. In IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society, pp. 1-6. doi: 10.1109/IECON48115.2021.9589787 (Link)
3. Burkhardt, M., Joachim, L., Gienger, A., Haala, N., Sörgel, U., and Sawodny, O. (2023). Rotation Control of a Novel Crane Gripper with Visual-Inertial Feedback. In 2023 IEEE 19th International Conference on Automation Science and Engineering (CASE), Auckland, New Zealand, pp. 1-6. doi: 10.1109/CASE56687.2023.10260451 (Link)
4. Burkhardt, M., Gienger, A., Joachim, L., Haala, N., Sörgel, U., and Sawodny, O. (2023). Data-based error compensation for georeferenced payload path tracking of automated tower cranes. In Mechatronics, vol. 94, 103028, doi: 10.1016/j.mechatronics.2023.103028 (Link)
5. Burkhardt, M., Gienger, A., and Sawodny, O. (2023). Optimization-Based Multipoint Trajectory Planning Along Straight Lines for Tower Cranes. In IEEE Transactions on Control Systems Technology, vol. 32, no. 1, pp. 290-297, doi: 10.1109/TCST.2023.3308762 (Link)