Two mobile manipulators are standing in a factory hall. The left mobile manipulator has two manipulators. The right mobile manipulator has one manipulator attached to the mobile platform via a linear axis.

IntCDC Research Project 26 - AI-supported Collaborative Control and Trajectory Generation of Mobile Manipulators for Indoor Construction Tasks

The goal is to develop AI-supported methods for collaborative trajectory generation for a group of heterogeneous mobile manipulators for (partially) automated construction in existing buildings.

Research Mission Cluster of Excellence IntCDC

The vision of the Cluster of Excellence Integrative Computational Design and Construction for Architecture (IntCDC) is to harness the full potential of digital technologies in order to rethink design, fabrication and construction based on integration and interdisciplinarity, with the goal of enabling game-changing innovation in the building sector as it can only occur through highly integrative fundamental research in an interdisciplinary, large-scale research undertaking.

Project description RP 26

The overall goal of RP 26 is to increase the efficiency, accuracy, and quality of exemplary indoor construction tasks with a group of collaborative heterogeneous mobile manipulators. Artificial Intelligence (AI) methods are used for path and trajectory generation and environment perception.
Optimization-based methods enable path and trajectory generation based on an environment model, minimizing execution time and maximizing accuracy. However, the complexity of the design scenarios, nonlinearities, the number of optimization variables, and uncertainties inhibit the real-time capability of the trajectory generation problem. For this reason, we are investigating decentralized approaches and the application of methods from AI (e.g., regression models, imitation learning, or reinforcement learning) to enable real-time trajectory generation and the consideration of sensor feedback for adaptive replanning.

This requires the development of indoor positioning and environmental sensing algorithms, using e.g. total stations, laser scanners, cameras and IMUs. We will exploit the capability of simultaneous localization and mapping (SLAM) algorithms for both positioning of mobile manipulators and acquisition of 3D information of the respective indoor environment. The goal is to modify and further develop high-precision SLAM approaches for real-time positioning and data acquisition for a digital twin of the construction site. This digital twin consists of an up-to-date, semantically enriched, consistent 3D model represented by a labeled 3D point cloud or a 3D voxel grid.

Projectpartner: Ifp, IIGS


This image shows Alice Hierholz

Alice Hierholz


Research Assistant

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