The rising use of robots in construction aims to ease labor-intensive and hazardous tasks. Ensuring safety in human-robot collaboration at construction sites is crucial, necessitating robust safety protocols and smooth interaction. This work aims to develop an open-source framework for monitoring and verifying safety in construction scenarios involving humans and robots. Our proposed framework includes the co-design of two modules: runtime monitoring against Signal Temporal Logic (STL) requirements and real-time reachability analysis using ProbStar. The runtime monitoring module effectively detects, localizes, and predicts human movements within the robot’s operational field. By employing a Kalman filter, we accurately estimate the future paths of workers, which facilitates proactive monitoring of worker safety. This approach enables dynamic adjustments to the robot’s trajectory, guided by quantitatively calculating robustness values of STL specifications in real-time. Our approach leverages real-time data from an RGB-D camera to promptly identify any deviations from expected behavior, further enhancing safety measures. To address uncertainties in localization that make the monitoring results inconclusive for safety judgments, the verification module employs real-time probabilistic reachability analysis to evaluate the likelihood of collisions between robots and obstacles within the robot’s local view. We evaluate the proposed framework across various human-robot interaction scenarios at construction sites.

Pose estimation is a building block for many location-based applications, such as safety applications in a construction site. Ultra-WideBand (UWB) Radios have been widely used for localization and can be used in pose (location and orientation angle of the object) estimation primarily because of the accuracy with which these radios can estimate the arrival time of radio signals. Current UWB pose estimation solutions do not perform adequately in Non-Line of Sight (NLoS) conditions. Some of these existing solutions in pose estimation rely on two or more types of sensors to tackle the NLoS challenge. These methods suffer from data fusion complexity, making the system not generalizable and limited to some specific simple environments, such as labs. In this paper, we propose ViPER, a UWB-based pose estimating system using only UWB radios. Our goal is to reduce the effects of the NLoS without the inclusion of any auxiliary sensors. ViPER uses low-pass filter, anchor and reference selection method to reduce the effect of NLoS in the measurements. It also estimates the pose of the entities using an optimization problem. We have evaluated ViPER in real- world highway construction and parking lot setting. We find that it improves the average packet reception ratio by 117% and decreases the error rate by 70% over the state of the art in Non-Line of Sight situation.

Overhead cranes are utilized for transporting loads in various workplaces while avoiding spatial conflicts with workers. Many researchers have investigated the control of cranes to increase productivity and safety. LQR is an often-studied controller used for this purpose. In practice, the weighting matrices of the LQR controller are tuned manually utilizing a trial and error method. In this paper, the use of the Simulated Annealing Algorithm (SAA) to tune the weighting matrices of the LQR is investigated. A new objective function is proposed which incorporates important criteria which are: minimization of overshoot, rise time, and settling time. The performance of the algorithm for crane control is evaluated in both simulations and lab experiments.

Utilizing autonomous robots to perform repetitive and labor-intensive tasks in the construction industry is one of the most promising directions to explore in order to enhance productivity, safety/health, and quality of construction projects. Such robots must have construction-related knowledge and skills in order to generate task plans capable of dealing with the unique and highly dynamic work environment of typical construction sites. However, autonomous and flexible behavior is currently impossible due to the lack of a robotics-compatible construction knowledge base. To overcome this bottleneck, this study proposes the establishment and utilization of such a construction knowledge base for use in generating autonomous behavior in robots. Specifically, this study provides an implementation of a small, mobile, autonomous robotics platform capable of performing fine-grained construction tasks in dynamic environments. Such tasks include painting, drilling screws, and transporting material and equipment. The platform is tested with a simulated robot based on the KUKA youBot tasked with painting walls in a room containing obstacles. In the simulation results, the proposed approach shows promise in being able to achieve autonomous operation of construction robots. Further development of this study will include implementing a more diverse set of skills, expanding the construction knowledge base, and tailoring localization, navigation planning, and task planning algorithms for the characteristics of the construction sites and the hardware tools used.

In this paper, we utilize the Microsoft HoloLens, a wearable augmented-reality (AR) device, to investigate how well an AR-based assessment tool measures a student's comprehension of, skill in, and aptitude for a given subject matter. We added assessment capabilities to a serious game prototype built in collaboration with Construction Management faculty for their Occupational Safety and Health Administration (OSHA) safety course. In an effort to verify if these assessment elements are effective, we hosted a trial with sixteen university students who were enrolled in the OSHA safety course.