AI Robot Excavator Makes Six-meter-high Dry-stone Walls

ETH Zurich researchers taught an autonomous excavator to construct dry stone walls itself using boulders weighing several tonnes and demolition debris.

* dry stone wall construction has involved vast amounts of manual labor.
* A multidisciplinary team of ETH Zurich researchers developed a method of using an autonomous excavator to construct a dry-​stone wall that is six meters high and sixty-​five meters long.
* Dry stone walls are resource efficient as they use locally sourced materials, such as concrete slabs that are low in embodied energy.

Using sensors, the excavator can autonomously draw a 3D map of the construction site and localise existing building blocks and stones for the wall’s construction. Specifically designed tools and machine vision approaches enable the excavator to scan and grab large stones in its immediate environment. It can also register their approximate weight as well as their centre of gravity. An algorithm determines the best position for each stone, and the excavator then conducts the task itself by placing the stones in the desired location. The autonomous machine can place 20 to 30 stones in a single consignment – about as many as one delivery could supply.

Science Robotics – 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.