Construction site preparation tasks rely on experienced operators and heavy machinery for clearing debris, earthmoving, leveling, and soil stabilization. These actions require complex collaboration between human teams to survey the site, estimate the material condition, and guide the operators accordingly. In recent years there has been a critical labor shortage due to increasing demands in construction. Integrating autonomous systems can mitigate this gap by replacing traditional methods with robotic solutions. However, while ideal conditions for automatic systems are static and highly controlled, construction sites are dynamic and unstructured environments. The ability of autonomous systems to overcome these conditions during outdoor construction site preparation tasks relies on their capacity to map the material on-site and continuously perform localization. This study suggests a solution to these problems by collaborating between an Unmanned Aerial Vehicle (UAV) and an Unmanned Ground Vehicle (UGV). In this method, the UAV produces a material map and monitors the UGV’s location relative to known static landmarks. These measurements are then sent to the ground vehicle and are added to the onboard sensors using the Extended Kalman Filter (EKF) approach. Thus, the UAV enhances the operation of the UGV by providing an accurate localization and mapping from the air and allowing it to perform a site-preparation task beyond mere sensing. This approach is examined with simulation and validated by outdoor experiments. Additionally, this method is integrated within Shepherd, a custom-developed plugin for computer-aided design applications.

Human activities are the principal contributors to oil pollution in marine ecosystems, thereby causing severe ecological damage. The high volume of vessel traffic operating in these areas contributes to the rapid contamination of the marine ecosystem, leading to frequent oil spill events, particularly near ports where congestion is prevalent. Addressing this issue today necessitates the involvement of numerous skilled personnel committed to the task. This team undertakes the repetitive and tedious work of surveying the area, detecting spills, and employing various techniques to address each oil slick. The emergence of Unmanned Surface Vehicle (USV) technology has introduced a promising alternative capable of alleviating the process of continuous monitoring and cleaning operations in proximal shoreline areas. This paper addresses the problem of USV cleaning operations near the port. The proposed method synthesizes a hierarchical architecture that integrates traditional global path planning for multi-destination oil spills, along with coverage path planning based on reinforcement learning, to adapt to dynamically changing oil spills. This combined architecture results in a comprehensive solution, allowing navigation within the port’s vicinity to address each occurrence of oil pollution. To evaluate the effectiveness of this approach, we conducted an elaborate simulation designed to replicate port activities. The findings of this paper indicate a significant reduction in pollution levels due to USV operation and underscore the ability to acquire complex policies for dynamic coverage planning through the use of a reinforcement learning framework.

Catastrophic gas leak events require human First Responder Teams (FRTs) to map hazardous areas (red zones). The initial task of FRT in such events is to assess the risk according to the pollution level and to quickly evacuate civilians to prevent casualties. These teams risk their lives by manually mapping the gas dispersion. This process is currently performed using hand-held gas detectors and requires dense and exhaustive monitoring to achieve reliable maps. However, the conventional mapping process is impaired due to limited human mobility and monitoring capacities. In this context, this paper presents a method for gas sensing using unmanned aerial vehicles. The research focuses on developing a custom path planner—Boundary Red Emission Zone Estimation (BREEZE). BREEZE is an estimation approach that allows efficient red zone delineation by following its boundary. The presented approach improves the gas dispersion mapping process by performing adaptive path planning, monitoring gas dispersion in real time, and analyzing the measurements online. This approach was examined by simulating a cluttered urban site in different environmental conditions. The simulation results show the ability to autonomously perform red zone estimation faster than methods that rely on predetermined paths and with a precision higher than ninety percent.

Research in advanced construction and autonomous earthworks begins to explore the shaping of aggregates for construction, military, and environmental purposes. However, there is currently no existing work on moving aggregates to form specific shapes on a surface. This action can aid in gathering aggregates for on-site construction of re-configurable formations, performing architectural tasks, or piling material reservoirs for concrete production. To support aggregate-forming, an autonomous agent is required to move non-labeled aggregates from multiple locations to numerous proximate target points in a predefined desired shape. In this process, the agent needs to push the aggregates, handle material spills, and update both the material location and the outcome formation. The paper presents a planner for autonomous aggregate shaping to support this task. The path generation is first demonstrated in a simulation environment and then validated in an in-lab experimental setup. The results show that employing the planner can assist in advancing the manipulation of aggregates beyond pushing and grading and toward the on-site shaping of aggregates into desired forms.

Maritime transport, responsible for delivering over eighty percent of the world’s goods, is the backbone of the global delivery industry. However, it also presents considerable environmental risks, particularly regarding aquatic contamination. Nearly ninety percent of marine oil spills near shores are attributed to human activities, highlighting the urgent need for continuous and effective surveillance. To address this pressing issue, this paper introduces a novel technique named OS-BREEZE. This method employs an Unmanned Surface Vehicle (USV) for assessing the extent of oil pollution on the sea surface. The OS-BREEZE algorithm directs the USV along the spill edge, facilitating rapid and accurate assessment of the contaminated area. The key contribution of this paper is the development of this novel approach for monitoring and managing marine pollution, which significantly reduces the path length required for mapping and estimating the size of the contaminated area. Furthermore, this paper presents a scale model experiment executed at the Coastal and Marine Engineering Research Institute (CAMERI). This experiment demonstrated the method’s enhanced speed and efficiency compared to traditional monitoring techniques. The experiment was methodically conducted across four distinct scenarios: the initial and advanced stages of an oil spill at the outer anchoring, as well as scenarios at the inner docking on both the stern and port sides.