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

Abstract

Automated building processes that enable efficient in situ resource utilization can facilitate construction in remote locations while simultaneously offering a carbon-reducing alternative to commonplace building practices. Toward these ends, we present a robotic construction pipeline that is capable of planning and building freeform stone walls and landscapes from highly heterogeneous local materials using a robotic excavator equipped with a shovel and gripper. Our system learns from real and simulated data to facilitate the online detection and segmentation of stone instances in spatial maps, enabling robotic grasping and textured 3D scanning of individual stones and rubble elements. Given a limited inventory of these digitized stones, our geometric planning algorithm uses a combination of constrained registration and signed-distance-field classification to determine how these should be positioned toward the formation of stable and explicitly shaped structures. We present a holistic approach for the robotic manipulation of complex objects toward dry stone construction and use the same hardware and mapping to facilitate autonomous terrain-shaping on a single construction site. Our process is demonstrated with the construction of a freestanding stone wall (10 meters by 1.7 meters by 4 meters) and a permanent retaining wall (65.5 meters by 1.8 meters by 6 meters) that is integrated with robotically contoured terraces (665 square meters). The work illustrates the potential of autonomous heavy construction vehicles to build adaptively with highly irregular, abundant, and sustainable materials that require little to no transportation and preprocessing.

Contemporary Strategies for the Structural Design of Multi-Story Modular Timber Buildings: A Comprehensive Review

“…Automated building processes facilitating efficient in situ resource utilization hold the potential to streamline construction operations in remote areas while offering a carbonreducing alternative to conventional building practices [72] by minimizing transport distances, costs, and associated pollutant emissions. Importantly, it ensures that the ecological value associated with timber usage remains intact, as it mitigates concerns arising from long-distance transportation.…”

Autonomous construction of lunar infrastructure with in-situ boulders

“…A global planner then searches for the shortest path to the target using the A* algorithm introduced by Hart et al (1968). The A* algorithm is an adjusted version of the Dijkstra algorithm (Dijkstra, 1959), which, for example, was used by Pena-Asensio et al (2023) for the path planning of lunar EVA's. The distance to collect the boulders at the target site itself then is estimated using a formula proposed by Few (1955).…”

“…For the Apollo 15 mission, the average velocity was 9.2 km / h according to Jones and Nola (1971). The calculation of the handling time is based on the value 21 min / stone by Johns et al (2023). The exact formulas are shown in the Supplementary Material.…”

3D Point Cloud Dataset of Heavy Construction Equipment

“…Previous construction automation research has primarily focused on hardware advancements, such as highway road surface crack inspection and sealing equipment [6,7], bricklaying robots [8], autonomous robotic excavation for dry stone construction [9], and machine guidance (MG) and machine control (MC) for excavators [10,11]. However, the rapid development of Building Information Modeling (BIM) and Artificial Intelligence (AI) technologies has resulted in a significant increase in software-centric construction automation research [12][13][14][15][16][17][18][19].…”