Robotic embankment

Abstract

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.

A framework for robotic excavation and dry stone construction using on-site materials

“…HEAP ( 66 ) is additionally capable of autonomously excavating free-form embankments with high precision ( 72 ), given a designed target geometry defined as a 2.5D height map. Our design process generally begins with a map captured by surveying equipment or by aerial or excavator-mounted LiDAR.…”

“…We refined the stone poses of the final structure with a high-density point cloud produced by a surveying laser scanner (Leica RTC360) to assess the median positional error between planned and placed poses as 0.115 m and the mean pose error as (0.135 ± 0.089, 0.089 ± 0.111). 66) is additionally capable of autonomously excavating freeform embankments with high precision (72), given a designed target geometry defined as a 2.5D height map. Our design process generally begins with a map captured by surveying equipment or by aerial or excavator-mounted LiDAR.…”

Vision-Based UAV-UGV Collaboration for Autonomous Construction Site Preparation

“…While a static map can be generated from georeferenced aerial images [32]- [34], using an offline map is not possible for construction tasks in which robotic platforms actively change their environment. This phenomenon can be seen by moving bricks around during robotic wall building [35] or by altering the entire topography during autonomous robotic excavation [5].…”

“…The associate editor coordinating the review of this manuscript and approving it for publication was Abderrahmane Lakas . with agile control [5]. Incorporating robotic platforms in construction can not only compensate for the shortage of workers but also aid in performing complex tasks that require high skill, such as preparing wire mesh reinforcements for concrete or carving stone [6], [7].…”

Graph-based Multi-sensor Fusion for Consistent Localization of Autonomous Construction Robots

“…The used reference frames are defined as: the fixed-world frame (W), the local odometry frame (O), the IMU frame (I), the lidar frame (L), and the GNSS frame (G). Furthermore, the excavator chassis base frame (B) and cabin frame (C) are defined, as they are required for generating driving motions and controlling the chassis [1], the control of the arm [27] and the cabin [28], respectively. These two frames are rotated against each other through the cabin turn joint.…”