Depth-camera-based rebar detection and digital reconstruction for robotic concrete spraying

In this paper, we tackle the challenge of detection and accurate digital reconstruction of steel rebar meshes using a set of industrial depth cameras. A construction example under investigation in this paper is robotic concrete spraying, where material is sprayed onto double-curved single layered rebar meshes. Before the spraying process can start, the location and geometry of the rebar mesh needs to be accurately know. We present an automatic image-based processing approach of depth images for grid point extraction at an accuracy of a few mm. Furthermore, we propose a sequence of execution steps in a robotic setup, including the hand–eye calibration, which enables the direct georeferencing of multiple data sets acquired from various poses into a common coordinate system. With the proposed approach we are able to digitally reconstruct a mesh of an unknown geometry in under 10 min with an accuracy better than 5 mm. The digitally reconstructed mesh allows for computation of material needed for its construction, enabling sustainable use of concrete in digital fabrication. The accurately reconstructed digital mesh, generated based on the proposed approach in this paper, is the input for the following spraying step, allowing for generation of accurate spray trajectories.

Robotic embankment

Automating earth-moving tasks has the potential to resolve labour-shortage, allow for unseen designs and foster sustainability through using on-site materials. In this interdisciplinary project involving robotics and landscape architecture, we combine our previous work on autonomous excavation of free-form shapes, dynamic landscape design and terrain modelling tools into a robotic landscape system. It tightly connects survey, design and fabrication to exchange information in real-time during fabrication. We purposely built a LiDAR survey drone for tight integration. The design environment contains terrain modelling tools to balance cut and fill volumes for material-neutral, on-site construction. Its parametric nature allows it to adapt the geometry to changing site conditions during fabrication. Our autonomous walking excavator is used to create these free-form shapes in natural granular material. We propose an excavation planner for free-form embankments that computes the next excavation location and subsequently the location where the excavated soil should be dumped. This robotic excavation system achieves the world’s first autonomous completion of free-form embankments with high accuracy. A $$20\hbox { m}$$ 20 m long S-shaped and a two-faced embankment with a corner with roughly 0.03–0.05 m average error were created.

Improving data communication on construction sites via LoRaWAN

Easily-accessible and reliable data communication in construction processes ensures high building quality, efficient workflow and secure working environments. The setup of network infrastructure on construction sites provides the necessary condition for timely and effective data communication. This paper researches a solution for on-site networking by implementing an IoT network on a reference construction site in Germany. In contrast to high-cost and high-bandwidth network infrastructure, a Long Range Wide Area Network–LoRaWAN with low cost and low bandwidth was deployed on the site. With additional IoT servers and LoRa-enabled devices, the reference construction site is able to communicate remotely with a robotic lab. In order to validate this concept of LoRaWAN on construction sites, an intra-site logistics and task scheduling system was developed to test the network performance. This paper conducts a preliminary study on the application of the IoT network technology–LoRaWAN in the logistics automation in construction. The test results can be used as references for other automation applications, such as internet of robot, intelligent process management, decision making system, etc.

Optimal trajectory planning of complicated robotic timber joints based on particle swarm optimization and an adaptive genetic algorithm

In this paper, a methodology for path distance and time synthetic optimal trajectory planning is described in order to improve the work efficiency of a robotic chainsaw when dealing with cutting complex timber joints. To demonstrate this approach one specific complicated timber joint is used as an example. The trajectory is interpolated in the joint space by using a quantic polynomial function which enables the trajectory to be constrained in the kinematic limits of velocity, acceleration, and jerk. The particle swarm optimization (PSO) is applied to optimize the path of all cutting surfaces of the timber joint in operating space to achieve the shortest path. Based on the optimal path, an adaptive genetic algorithm (AGA) is used to optimize the time interval of interpolation points of every joint to realize the time-optimal trajectory. The results of the simulation show that the PSO method shortens the distance of the trajectory and that the AGA algorithm reduces time intervals and helps to obtain smooth trajectories, validating the effectiveness and practicability of the two proposed methodology on path and time optimization for 6-DOF robots when used in cutting tasks.