A new pilot just put a robot out front of a major construction challenge in Massachusetts: the building of a new headquarters for a major life sciences company. The robot's assignment was to draw the all-important layout grid at the job site, a kind of paint-by-numbers life-sized blue print that's an integral part of the building process. The robot, by Rugged Robotics, can autonomously mark fully coordinated designs directly on concrete floors. The process, called field layout, is ordinarily done by people in much the same way it has been for the last hundred years, using tape measures, chalk-lines, and surveying equipment to manually mark the location of walls and mechanical systems. In an industry marked by project overruns and blown budgets, this critical step is often a source of future downstream errors that cost time and money.
Researchers from Baidu Research Robotics and Auto-Driving Lab (RAL) and the University of Maryland, College Park, have introduced an autonomous excavator system (AES) that can perform material loading tasks for a long duration without any human intervention while offering performance closely equivalent to that of an experienced human operator. AES is among the world's first uncrewed excavation systems to have been deployed in real-world scenarios and continuously operating for over 24 hours, bringing about industry-leading benefits in terms of enhanced safety and productivity. The researchers described their methodology in a research paper published on June 30, 2021, in Science Robotics. "This work presents an efficient, robust, and general autonomous system architecture that enables excavators of various sizes to perform material loading tasks in the real world autonomously," said Dr. Liangjun Zhang, corresponding author and the Head of Baidu Research Robotics and Auto-Driving Lab. Excavators are vital for infrastructure construction, mining, and rescue applications.
Digital Twin is an emerging technology at the forefront of Industry 4.0, with the ultimate goal of combining the physical space and the virtual space. To date, the Digital Twin concept has been applied in many engineering fields, providing useful insights in the areas of engineering design, manufacturing, automation, and construction industry. While the nexus of various technologies opens up new opportunities with Digital Twin, the technology requires a framework to integrate the different technologies, such as the Building Information Model used in the Building and Construction industry. In this work, an Information Fusion framework is proposed to seamlessly fuse heterogeneous components in a Digital Twin framework from the variety of technologies involved. This study aims to augment Digital Twin in buildings with the use of AI and 3D reconstruction empowered by unmanned aviation vehicles. We proposed a drone-based Digital Twin augmentation framework with reusable and customisable components. A proof of concept is also developed, and extensive evaluation is conducted for 3D reconstruction and applications of AI for defect detection.
Multi working-machines pathfinding solution enables more mobile machines simultaneously to work inside of a working site so that the productivity can be expected to increase evolutionary. To date, the potential cooperation conflicts among construction machinery limit the amount of construction machinery investment in a concrete working site. To solve the cooperation problem, civil engineers optimize the working site from a logistic perspective while computer scientists improve pathfinding algorithms' performance on the given benchmark maps. In the practical implementation of a construction site, it is sensible to solve the problem with a hybrid solution; therefore, in our study, we proposed an algorithm based on a cutting-edge multi-pathfinding algorithm to enable the massive number of machines cooperation and offer the advice to modify the unreasonable part of the working site in the meantime. Using the logistic information from BIM, such as unloading and loading point, we added a pathfinding solution for multi machines to improve the whole construction fleet's productivity. In the previous study, the experiments were limited to no more than ten participants, and the computational time to gather the solution was not given; thus, we publish our pseudo-code, our tested map, and benchmark our results. Our algorithm's most extensive feature is that it can quickly replan the path to overcome the emergency on a construction site.
AI that can follow a person seems like a simple enough task. It's certainly a simple thing to ask a human to do, but what if people or objects get in the way of the robot following behind a person? How do you navigate an environment that's in a constant state of change? About a year ago at a robotics conference TechCrunch held at UC Berkeley, AI startup founders explored solutions for common problems encountered when trying to automate construction projects. Tessa Lau, CEO of Dusty Robotics, called attention to the challenge of moving machines in an unstructured environment filled with people.
This paper presents a smart contract-based solution for autonomous administration of construction progress payments. It bridges the gap between payments (cash flow) and the progress assessments at job sites (product flow) enabled by reality capture technologies and building information modeling (BIM). The approach eliminates the reliance on the centralized and heavily intermediated mechanisms of existing payment applications. The construction progress is stored in a distributed manner using content addressable file sharing; it is broadcasted to a smart contract which automates the on-chain payment settlements and the transfer of lien rights. The method was successfully used for processing payments to 7 subcontractors in two commercial construction projects where progress monitoring was performed using a camera-equipped unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) equipped with a laser scanner. The results show promise for the method's potential for increasing the frequency, granularity, and transparency of payments. The paper is concluded with a discussion of implications for project management, introducing a new model of project as a singleton state machine.
We present a novel concept of a heterogeneous, distributed platform for autonomous 3D construction. The platform is composed of two types of robots acting in a coordinated and complementary fashion: (i) A collection of communicating smart construction blocks behaving as a form of growable smart matter, and capable of planning and monitoring their own state and the construction progress; and (ii) A team of inchworm-shaped builder robots designed to navigate and modify the 3D structure, following the guidance of the smart blocks. We describe the design of the hardware and introduce algorithms for navigation and construction that support a wide class of 3D structures. We demonstrate the capabilities of our concept and characterize its performance through simulations and real-robot experiments.
Robots have continued to make inroads into the construction industry. Whether due to a lack of skilled labor, or simply new tech becoming commercially available, companies continue to market robots and drones to contractors as options to perform important tasks faster and more efficiently. Here, Construction Dive looks at recent innovations in robotics that could potentially pick up extra slack or speed up jobsite work.
"The AI chip can perform the many calculations needed in just milliseconds," Thon explains. This type of chip is also known as "acceleration hardware" as a result. For the first demonstration, the researchers chose an application using an autonomous robot. The machine-learning algorithms and their implementation for the gripping process are the result of a collaboration between researchers from Corporate Technology in Berkeley and the University of California, Berkeley. The algorithm uses data from the 3D camera mounted on the robot arm to calculate the ideal points for grasping the target object.
While most of the buzz around artificial intelligence (AI) may seem new, the concept has been around for more than 60 years. American computer scientist John McCarthy, known as the "Father of AI," coined the term "artificial intelligence" in the 1950s, leading researchers across the United States to dig into the computer learning for processing equations and theorems. In the 1960s, computer scientists began creating machines similar to robots, and the first humanoid robot was built in Japan in 1972. Unfortunately, it was difficult for scientists to go any further due to the lack of advancement in data technology, and the period between the mid 1970s and early 1990s saw a drop-off in serious development. But in the 1990s, computers became more advanced.