Mobile robotic systems are increasingly used in various work environments to support productivity. However, deploying robots in workplaces crowded by human workers and interacting with them results in safety challenges and concerns, namely robot-worker collisions and worker distractions in hazardous environments. Moreover, the literature on risk assessment as well as the standard specific to mobile platforms is rather limited. In this context, this paper first conducts a review of the relevant standards and methodologies and then proposes a risk assessment for the safe deployment of mobile robots on construction sites. The approach extends relevant existing safety standards to encompass uncovered scenarios. Safety recommendations are made based on the framework, after its validation by field experts.

The construction industry has long explored robotics and computer vision, yet their deployment on construction sites remains very limited. These technologies have the potential to revolutionize traditional workflows by enhancing accuracy, efficiency, and safety in construction management. Ground robots equipped with advanced vision systems could automate tasks such as monitoring mechanical, electrical, and plumbing (MEP) systems. The present research evaluates the applicability of open-vocabulary vision-language models compared to fine-tuned, lightweight, closed-set object detectors for detecting MEP components using a mobile ground robotic platform. A dataset collected with cameras mounted on a ground robot was manually annotated and analyzed to compare model performance. The results demonstrate that, despite the versatility of vision-language models, fine-tuned lightweight models still largely outperform them in specialized environments and for domain-specific tasks.

-- In industrial environments, predicting human actions is essential for ensuring safe and effective collaboration between humans and robots. This paper introduces a perception framework that enables mobile robots to understand and share information about human actions in a decentralized way. The framework first allows each robot to build a spatial graph representing its surroundings, which it then shares with other robots. This shared spatial data is combined with temporal information to track human behavior over time. A swarm-inspired decision-making process is used to ensure all robots agree on a unified interpretation of the human's actions. Results show that adding more robots and incorporating longer time sequences improve prediction accuracy. Collaborative robots are poised to become a cornerstone of Industry 5.0 [1], emphasizing human-centric design solutions to meet the flexibility demands of hyper-customized industrial processes [2]. Significant efforts have been directed toward identifying key enabling technologies to enhance robotic systems with advanced situational awareness and robust safety features for human coworkers. Two pivotal technologies stand out: individualized human-machine interaction systems that merge the strengths of humans and machines, and the application of AI to improve workplace safety [3].

The manufacturing sector like many other sectors was recently affected by workforce shortages, a problem that automation and robotics can heavily minimize Kugler (2022). Simultaneously, Reinforcement learning (RL) offers a promising solution where robots can learn to perform tasks through interaction and feedback from the environment Singh et al. (2022). However, despite their success in numerous simulation environments, we still don't see many real-world deployments of RL robotic solutions. In fact, many researchers either oversimplify the targeted real-world scenario such as Wu et al. (2023) or do not even evaluate their model in physical robots Lu et al. (2022); Na et al. (2022). It is known that training RL policies directly in real robots can be expensive, timeconsuming, labor-intensive, and maybe even dangerous, that is why it makes sense to try to leverage training in simulation.

Abstract-- Deploying mobile robots in construction sites to collaborate with workers or perform automated tasks such as surveillance and inspections carries the potential to greatly increase productivity, reduce human errors, and save costs. However ensuring human safety is a major concern, and the rough and dynamic construction environments pose multiple challenges for robot deployment. In this paper, we present the insights we obtained from our collaborations with construction companies in Canada and discuss our experiences deploying a semi-autonomous mobile robot in real construction scenarios. I. INTRODUCTION The imperative of ensuring human safety in workplaces shared with robotic systems presents a significant challenge for the field deployment of such systems. Risks associated with human-robot collaboration encompass both direct and indirect hazards.

Artistic performances involving robotic systems present unique technical challenges akin to those encountered in other field deployments. In this paper, we delve into the orchestration of robotic artistic performances, focusing on the complexities inherent in communication protocols and localization methods. Through our case studies and experimental insights, we demonstrate the breadth of technical requirements for this type of deployment, and, most importantly, the significant contributions of working closely with non-experts.

Spherical robots have garnered increasing interest 1 INTRODUCTION for their applications in exploration, tunnel inspection, Spherical rolling robots (SRRs) are a fascinating category and extraterrestrial missions. Diverse designs of robots characterized by their ability to move by have emerged, including barycentric configurations, rolling on themselves, owing to their unique spherical pendulum-based mechanisms, etc. However, beneath this seemingly simple concept wide spectrum of control strategies has been proposed, lies a plethora of sophisticated mechanisms and control ranging from traditional PID approaches to cutting-edge strategies that enable such motion. Our systematic review aims to comprehensively ago, NASA introduced the idea of "Beach-Ball" Robotic identify and categorize locomotion systems and Rovers Notably, the Rollo, designed the years 1996 to 2023. A meticulous search across five in 1996 at Finland's Helsinki University of Technology databases yielded a dataset of 3189 records. As a result [1], stands as one of the pioneering spherical of our exhaustive analysis, we identified a collection robots aimed at operating in hostile environments. Leveraging the inherent protective nature of their spherical shell renders insights garnered, we provide valuable recommendations them well-suited for challenging terrains, safeguarding for optimizing the design and control aspects of spherical sensitive mechatronics, including sensors and actuators. Furthermore, we illuminate [2], surveys of dusty construction sites, tracking key research directions that hold the potential to crop yields in muddy fields, and even missions in extreme unlock the full capabilities of spherical robots. Barycentric spherical robots (BSRs) manipulate reviews exist in the literature, they fail to encapsulate the the center of mass to achieve desired motion, exemplified latest advancements in this field. For instance, a comprehensive by wheeled mechanisms within a spherical shell examination of rolling in robotics [6] delves into or popular pendulum-driven spherical robots.