Autonomous Scene Understanding, Motion Planning, and Task Execution for Geometrically Adaptive Robotized Construction Work

The construction industry suffers from such problems as high cost, poor quality, prolonged duration, and substandard safety. Robots have the potential to help alleviate such problems by becoming construction co-workers, yet they are seldom found operating on today’s construction sites. This is primarily due to the industry’s unstructured nature, substantial scale, and loose tolerances, which present additional challenges for robot operation. To help construction robots overcome such challenges and begin functioning as useful partners in human-robot construction teams, this research focuses on advancing two fundamental capabilities: enabling a robot to determine where it is located as it moves about a construction site, and enabling it to determine the actual pose and geometry of its workpieces so it can adapt its work plan and perform work. Specifically, this research first explores the use of a camera-marker sensor system for construction robot localization. To provide a mobile construction robot with the ability to estimate its own pose, a camera-marker sensor system was developed that is affordable, reconfigurable, and functional in GNSS-denied locations, such as urban areas and indoors. Excavation was used as a case study construction activity, where bucket tooth pose served as the key point of interest. The sensor system underwent several iterations of design and testing, and was found capable of estimating bucket tooth position with centimeter-level accuracy. This research also explores a framework to enable a construction robot to leverage its sensors and Building Information Model (BIM) to perceive and autonomously model the actual pose and geometry of its workpieces. Autonomous motion planning and execution methods were also developed and incorporated into the adaptive framework to enable a robot to adapt its work plan to the circumstances it encounters and perform work. The adaptive framework was implemented on a real robot and evaluated using joint filling as a case study construction task. The robot was found capable of identifying the true pose and geometry of a construction joint with an accuracy of 0.11 millimeters and 1.1 degrees. The robot also demonstrated the ability to autonomously adapt its work plan and successfully fill the joint. In all, this research is expected to serve as a basis for enabling robots to function more effectively in challenging construction environments. In particular, this work focuses on enabling robots to operate with greater functionality and versatility using methods that are generalizable to a range of construction activities. This research establishes the foundational blocks needed for humans and robots to leverage their respective strengths and function together as effective construction partners, which will lead to ubiquitous collaborative human-robot teams operating on actual construction sites, and ultimately bring the industry closer to realizing the extensive benefits of robotics.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149785/1/klundeen_1.pd

IKUWA6. Shared Heritage

Celebrating the theme ‘Shared heritage’, IKUWA6 (the 6th International Congress for Underwater Archaeology), was the first such major conference to be held in the Asia-Pacific region, and the first IKUWA meeting hosted outside Europe since the organisation’s inception in Germany in the 1990s. A primary objective of holding IKUWA6 in Australia was to give greater voice to practitioners and emerging researchers across the Asia and Pacific regions who are often not well represented in northern hemisphere scientific gatherings of this scale; and, to focus on the areas of overlap in our mutual heritage, techniques and technology. Drawing together peer-reviewed presentations by delegates from across the world who converged in Fremantle in 2016 to participate, this volume covers a stimulating diversity of themes and niche topics of value to maritime archaeology practitioners, researchers, students, historians and museum professionals across the world

Optimization of Operation Sequencing in CAPP Using Hybrid Genetic Algorithm and Simulated Annealing Approach

In any CAPP system, one of the most important process planning functions is selection of the operations and corresponding machines in order to generate the optimal operation sequence. In this paper, the hybrid GA-SA algorithm is used to solve this combinatorial optimization NP (Non-deterministic Polynomial) problem. The network representation is adopted to describe operation and sequencing flexibility in process planning and the mathematical model for process planning is described with the objective of minimizing the production time. Experimental results show effectiveness of the hybrid algorithm that, in comparison with the GA and SA standalone algorithms, gives optimal operation sequence with lesser computational time and lesser number of iterations