Ryan Luke Johns

Last Name

Johns

First Name

Ryan Luke

ORCID

0000-0002-2004-0291

Show all metadata

Search Results

Publications 1 - 7 of 7

Grasping and Object Reorientation for Autonomous Construction of Stone Structures
Wermelinger, Martin; Johns, Ryan Luke; Gramazio, Fabio; et al. (2021)
IEEE Robotics and Automation Letters
Building large and stable structures from highly irregular stones is among the most challenging construction tasks with excavators. In this paper, we present a method for grasp planning and object manipulation that enables the world’s first autonomous assembly of a large-scale stone wall with an unmanned hydraulic excavator system. Our method utilizes point clouds and mesh surface reconstruction of stones in order to plan grasp configurations with a 2-jaw gripper mounted on the excavator. Besides considering the geometry of the stone to sample force closure grasps, the grasp planner also takes into account collision constraints during object pick and place, informed by a LiDAR based point cloud map. Furthermore, we show an approach to reorient arbitrarily shaped objects that are not feasible to be directly placed at the desired location without violating collision constraints. Using a physics engine, we find a settled intermediate pose that allows direct placement and is reachable from the initial stone pose. The applicability of the proposed grasp planning method is demonstrated with the construction of a dry stone wall composed of over one hundred boulders using an autonomous excavator. We show a high primary grasp success rate (82.2 %) and illustrate how the system recovers from slippage by relocating the object and re-planning the grasp correspondingly.
Autonomous Dry Stone
Johns, Ryan Luke (2023)
On-site robotic construction not only has the potential to enable architectural assemblies that exceed the size and complexity practical with laboratory-based prefabrication methods, but also offers the opportunity to leverage context-specific, locally-sourced materials that are inexpensive, abundant, and low in embodied energy. Toward these ends, this doctoral research is focused on developing a novel process for the robotic construction of dry stone walls in situ, bounded by design constraints and facilitated by a customized autonomous hydraulic excavator. These walls are built using as-found natural stones and reclaimed demolition debris, using a construction pipeline that automatically collects an inventory of these materials by detecting, grasping, and 3D-scanning them directly on site. Given a limited inventory of these digitized stones, a geometric planning algorithm determines how each of these objects should be positioned toward the formation of stable and explicitly-shaped structures. By adapting knowledge from traditional stone masonry practices, this planning algorithm uses a combination of geometric features to seed hypothesis stone placement candidates. These candidates are then refined toward stable and geometrically-aligned solutions using a combination of torque- and penetration- constrained iterative closest point registration, and physics simulation. Ultimately, these solutions are classified for placement viability, using a supervised model that considers a 3-channel signed-distance-field data-representation of each solution that encapsulates the candidate stone, the local context of terrain and previously-placed stones, and the freeform target-wall geometry. To accommodate settling and process tolerances, the geometric planner works iteratively, using information from an accumulated LiDAR map to regularly update the as-built structure after each stone is placed—and before each successive search for new candidate placements. Using this approach, the planner is able to inform the construction of double-layer walls, using highly nonstandard stones and debris—creating structures with a 60% fill-to-void ratio within arbitrarily-defined wall boundaries. This process is further informed by large-scale, outdoor physical experiments. These experiments resulted in the construction of three robotically-constructed dry stone walls, that are built using gneiss boulders, erratics unearthed on construction sites, and salvaged concrete demolition debris. These demonstrators include a 40-stone s-curved wall (5 x 1.6 x 3 m), and a linear freestanding wall (10 x 1.7 x 4 m) constructed with 24% reclaimed concrete. At the last stage of development, this work is evaluated through the first robotic construction of a permanent and publicly-accessible stone retaining wall (65.5 x 1.8 x 6 m) consisting of 938 unique elements—and that is integrated with robotic landscape features based on the doctoral research of Dominic Jud (Robotic Systems Lab) and Ilmar Hurkxkens (Chair of Landscape Architecture). Collectively, these demonstrations saw the robotic placement of over one thousand coarse boulders, with each weighing an average of one tonne. The physical testing conducted during these experiments revealed shortcomings and necessary improvements to the process, and allowed us to provide the first benchmarks for large-scale robotic assembly with nonstandard stones. These studies demonstrated robotic stone placement rates up to 12.2 min/stone, and quantified the ability of this method to reduce emissions by upwards of 40% when compared to equivalently performing concrete structures. This 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—creating structures which benefit aesthetically and environmentally from the properties of regionally-specific natural materials.
Autonomous Dry Stone
Johns, Ryan Luke; Wermelinger, Martin; Mascaro, Ruben; et al. (2020)
Construction Robotics
On-site robotic construction not only has the potential to enable architectural assemblies that exceed the size and complexity practical with laboratory-based prefabrication methods, but also offers the opportunity to leverage context-specific, locally sourced materials that are inexpensive, abundant, and low in embodied energy. We introduce a process for constructing dry stone walls in situ, facilitated by a customized autonomous hydraulic excavator. Cabin-mounted LiDAR sensors provide for terrain mapping, stone localization and digitization, and a planning algorithm determines the placement position of each stone. As the properties of the materials are unknown at the beginning of construction, and because error propagation can hinder the efficacy of pre-planned assemblies with non-uniform components, the structure is planned on-the-fly: the desired position of each stone is computed immediately before it is placed, and any settling or unexpected deviations are accounted for. We present the first result of this geometric- and motion-planning process: a 3-m-tall wall composed of 40 stones with an average weight of 760 kg.
Deep Sandscapes: Design Tool for Robotic Sand-Shaping with GAN-Based Heightmap Predictions
Tsuruta, Ko; Griffioen, Simon Joris; Medina Ibáñez, Jesús; et al. (2022)
2022 Annual Modeling and Simulation Conference (ANNSIM)
The aim of this research is to develop an adaptive and interactive design workflow for robotic sand-shaping. One of the challenges of working with natural materials is the increased level of complexity due to uncertain material behaviour. In this work, a generative adversarial network (GAN) is used to expedite material simulations. We train an image-to-image GAN to learn the relationship between planned excavation trajectories in existing sand states (input) and the modified sand states after excavation (ground truth). Data is collected prior to learning by an autonomous excavation routine. This routine (1) generates random trajectories that are executed by a robotic-arm; (2) uses an RGB-D camera to capture sand states as heightmaps before and after robotic interaction. Our GAN-assisted design tool predicts rearranged sandscapes by providing robotic excavation trajectories. Integrated into CAD software, an interactive and iterative design environment is realised for robotic sand-shaping.
Autonomous construction of lunar infrastructure with in-situ boulders
Walther, Jonas; Johns, Ryan Luke; Kolvenbach, Hendrik; et al. (2024)
Frontiers in Space Technologies
Significant infrastructure is required to establish a long-term presence of humans on the lunar surface. In-situ resource utilization (ISRU) is a fundamental approach to ensure the viability of such construction. Here, we investigate the feasibility of constructing blast shields as one example of lunar infrastructure using unprocessed lunar boulders and an autonomous robotic excavator. First, we estimate the volume of unprocessed material required for the construction of blast shield segments. Secondly, we quantify the amount of available boulders in two exploration zones (located at the Shackleton-Henson Connecting Ridge and the Aristarchus Plateau pyroclastic deposit) using LRO NAC images and boulder size-frequency distribution laws. In addition, we showcase an alternative approach that relies on Diviner rock abundance data. Thirdly, we use a path planning algorithm to derive the distance, energy, and time required to collect local material and construct blast shield elements. Our results show that our construction method requires two orders of magnitudes less energy than alternative ISRU construction methods, while maintaining realistic mission time and payload capacity margins.
Circular Robotic Construction
Vasey, Lauren; Aejmelaeus-Lindström, Petrus; Jenny, David; et al. (2024)
Circular Economy and Sustainability ~ A Circular Built Environment in the Digital Age
In situ robotic construction is a type of construction where mobile robotic systems build directly on the building site. To enable on-site navigation, industrial robots can be integrated with mobile bases, while mobile, high-payload construction machines can be adapted for autonomous operation. With parallel advances in sensor processing, these robotic construction processes can become robust and capable of handling non-standard, local, as-found materials. The potential of using autonomous, mobile robotic systems for the development of innovative circular construction processes is presented in three exemplary case studies:(i) robotically jammed structures from bulk materials, (ii) robotic earthworks with local and upcycled materials, and (iii) robotic additive manufacturing with earth-based materials. These processes exemplify key strategies for a circular industry through the utilisation of materials with low embodied greenhouse gas emissions and the implementation of fully reversible construction processes. For each case study, we describe the robotic building process, the enabling technologies and workflows, and the major sustainability and circularity benefits compared to conventional construction methods. Moreover, we discuss the difficulty of industry transfer, considering challenges such as detailing, integration, and engineering validation. We conclude with an outlook towards future research avenues and industry adoption strategies.
A framework for robotic excavation and dry stone construction using on-site materials
Johns, Ryan Luke; Wermelinger, Martin; Mascaro, Ruben; et al. (2023)
Science Robotics
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.

Publications 1 - 7 of 7

Ryan Luke Johns

Last Name

First Name

ORCID

Organisational unit

Filters

Search Results