A high payload aerial platform for infrastructure repair and manufacturing

The use of aerial robots in construction is an area of general interest in the robotics community. Autonomous aerial systems have the potential to improve safety, efficiency and sustainability of industrial construction and repair processes. Several solutions have been deployed in this domain focusing on problems in aerial manipulation and control using existing aerial platforms which are not specialised for the specific challenges in operating on a construction site. This paper presents a new compact, high thrust aerial platform that can act as a modular, application agnostic base for demonstrating a wide variety of capabilities. The platform has been built and tested flying both with manual controls and autonomously in a motion tracking arena while carrying a payload of up to 7.3 kg with a maximum flight time between 10–34 mins (payload dependent). In the future, this platform will be combined with vision based tracking sensors, manipulators and other hardware to operate in and interact with an outdoor environment. Future applications may include manipulation of heavy objects, deposition of material and navigating confined spaces

Modelling the influence of material and process parameters on Shotcrete 3D Printed strands - cross-section adjustment for automatic robotic manufacturing

Due to its high interlayer strength and application flexibility, Shotcrete 3D Printing (SC3DP) is a promising method for the additive manufacturing of structural concrete components. The printing process is based on a layer-wise material application, conducted along a pre-designed printing path. However, material batch inhomogeneities and environmental alteration lead to varying concrete properties over the production processes. These material irregularities stochastically affect the layer geometry and thus limit the achievable reproducibility and accuracy. To enhance the process stability and improve the dimensional component quality in case of environmental changes, a reliable mapping between the strand geometry and the process and material parameters is fundamental for systematic cross-section adjustment. In this paper, we present an experimental-based approach for attaining a flexible regression model of the cross-section of Shotcrete 3D Printed concrete strands. The width and height of the layer are chosen for the strand representation, which we considered as the main factors for the printing-path planning. Regarding the modelling parameters, we focus on the volume flow parameters of concrete and air, and on the accelerator dosage. These inertia afflicted parameters can provide a consistent strand geometry, while factors of lower latency such as printing speed or spray distance are conserved for online adaptation. Based on the presented proceeding, an adjustable layer height and width model has been successfully used to predict the strand properties. The production of a medium sized sample wall further proves the applicability to the production process. In addition, we demonstrated that the chosen parameters not only affect the geometry but also the mechanical performance of SC3DP-specimens. This is evaluated based on flexural strength measurements. Given the geometrical and mechanical properties, the study defines applicable limits for the investigated parameters

System identification and nonlinear model predictive control with collision avoidance applied in Hexacopters UAVs

Accurate trajectory tracking is a critical property of unmanned aerial vehicles (UAVs) due to system nonlinearities, under-actuated properties and constraints. Specifically, the use of unmanned rotorcrafts with accuracy trajectory tracking controllers in dynamic environments has the potential to improve the fields of environment monitoring, safety, search and rescue, border surveillance, geology and mining, agriculture industry, and traffic control. Monitoring operations in dynamic environments produce significant complications with respect to accuracy and obstacles in the surrounding environment and, in many cases, it is difficult to perform even with state-of-the-art controllers. This work presents a nonlinear model predictive control (NMPC) with collision avoidance for hexacopters’ trajectory tracking in dynamic environments, as well as shows a comparative study between the accuracies of the Euler–Lagrange formulation and the dynamic mode decomposition (DMD) models in order to find the precise representation of the system dynamics. The proposed controller includes limits on the maneuverability velocities, system dynamics, obstacles and the tracking error in the optimization control problem (OCP). In order to show the good performance of this control proposal, computational simulations and real experiments were carried out using a six rotary-wind unmanned aerial vehicle (hexacopter—DJI MATRICE 600). The experimental results prove the good performance of the predictive scheme and its ability to regenerate the optimal control policy. Simulation results expand the proposed controller in simulating highly dynamic environments that showing the scalability of the controller

Order and information in the patterns of spinning magnetic micro-disks at the air-water interface.

The application of the Shannon entropy to study the relationship between information and structures has yielded insights into molecular and material systems. However, the difficulty in directly observing and manipulating atoms and molecules hampers the ability of these systems to serve as model systems for further exploring the links between information and structures. Here, we use, as a model experimental system, hundreds of spinning magnetic micro-disks self-organizing at the air-water interface to generate various spatiotemporal patterns with varying degrees of order. Using the neighbor distance as the information-bearing variable, we demonstrate the links among information, structure, and interactions. We establish a direct link between information and structure without using explicit knowledge of interactions. Last, we show that the Shannon entropy by neighbor distances is a powerful observable in characterizing structural changes. Our findings are relevant for analyzing natural self-organizing systems and for designing collective robots

Industrial, Collaborative and Mobile Robotics in Latin America: Review of Mechatronic Technologies for Advanced Automation

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PD

A reinforcement learning approach to multi-robot planar construction

We consider the problem of shape formation in a decentralised swarm of robots trained using a subfield of machine learning called reinforcement learning. Shapes are formed from ambient objects which are pushed into a desired pattern. The shape is specified using a projected scalar field that the robots can locally sample. This scalar field plays a similar role to the pheromone gradients used by social insects such as ants and termites to guide the construction of their sophisticated nests. The overall approach is inspired by the previously developed orbital construction algorithm. Reinforcement learning allows one or more agents to learn the best action to take in a given situation by interacting with their environment and learning a mapping from states to actions. Such systems are well-suited to robotics, as robots often interact with complex environments through a variety of sensors and actuators. When reinforcement learning is applied to a multi-agent system, it is called 'multi-agent reinforcement learning' (MARL). The main feature that MARL offers is flexibility | a multi-agent decentralised system can have agents added, removed, or reconstructed without need for rewriting the system. This allows for more robust solutions due to its ability to cope with failure. With the use of simulators paired with MARL, we can effectively learn policies that result in the formation of unique shapes. This is a vast improvement over hand-coded solutions, as it removes dependence on hard-coded actions. Reinforcement learning eliminates the need for writing control algorithms in the first place | which tend to be be extremely task-specific and time-consuming

Large language model-based code generation for the control of construction assembly robots:A hierarchical generation approach

Offline programming (OLP) is a mainstream approach for controlling assembly robots at construction sites. However, existing methods are tailored to specific assembly tasks and workflows, and thus lack flexibility. Additionally, the emerging large language model (LLM)-based OLP cannot effectively handle the code logic of robot programming. Thus, this paper addresses the question: How can robot control programs be generated effectively and accurately for diverse construction assembly tasks using LLM techniques? This paper describes a closed user-on-the-loop control framework for construction assembly robots based on LLM techniques. A hierarchical strategy to generate robot control programs is proposed to logically integrate code generation at high and low levels. Additionally, customized application programming interfaces and a chain of action are combined to enhance the LLM's understanding of assembly action logic. An assembly task set was designed to evaluate the feasibility and reliability of the proposed approach. The results show that the proposed approach (1) is widely applicable to diverse assembly tasks, and (2) can improve the quality of the generated code by decreasing the number of errors. Our approach facilitates the automation of construction assembly tasks by simplifying the robot control process

Path and Motion Planning for Autonomous Mobile 3D Printing

Autonomous robotic construction was envisioned as early as the ‘90s, and yet, con- struction sites today look much alike ones half a century ago. Meanwhile, highly automated and efficient fabrication methods like Additive Manufacturing, or 3D Printing, have seen great success in conventional production. However, existing efforts to transfer printing technology to construction applications mainly rely on manufacturing-like machines and fail to utilise the capabilities of modern robotics. This thesis considers using Mobile Manipulator robots to perform large-scale Additive Manufacturing tasks. Comprised of an articulated arm and a mobile base, Mobile Manipulators, are unique in their simultaneous mobility and agility, which enables printing-in-motion, or Mobile 3D Printing. This is a 3D printing modality, where a robot deposits material along larger-than-self trajectories while in motion. Despite profound potential advantages over existing static manufacturing-like large- scale printers, Mobile 3D printing is underexplored. Therefore, this thesis tack- les Mobile 3D printing-specific challenges and proposes path and motion planning methodologies that allow this printing modality to be realised. The work details the development of Task-Consistent Path Planning that solves the problem of find- ing a valid robot-base path needed to print larger-than-self trajectories. A motion planning and control strategy is then proposed, utilising the robot-base paths found to inform an optimisation-based whole-body motion controller. Several Mobile 3D Printing robot prototypes are built throughout this work, and the overall path and motion planning strategy proposed is holistically evaluated in a series of large-scale 3D printing experiments

パラメトリック設計とロボットによる自動化施工の実用化に影響を与える要因に関する研究

The construction industry has always been an important part of human economic activity. As time goes on, building techniques and construction methods are changing. However, with the increasing acceleration of social development, traditional construction methods have reached their limits. Both in China and Japan are facing the problem of aging population structure and low birth rate. The human demand for complex forms of construction is growing, yet experienced workers are in short supply. The construction industry continues to undergo industrial upgrading, while the rise of digital design and the widespread use of robotics point the way to the future of the construction industry. To explore the possibilities of parametric design and robotic automated construction through two practical projects. We also explore the factors affecting the application of robotic automated construction technology by building an evolutionary game model to provide a policy reference for the government, construction companies and public universities.北九州市立大