In the evolving Industry 5.0 landscape, human-robot collaboration (HRC) is crucial to improve safety and productivity in shared industrial workspaces. This paper presents a cost-effective solution to track the human skeleton and identify the body parts that are most exposed to collision risk during a HRC task, i.e. the head and the hands, using a RGB-D camera. The depth images are processed through artificial intelligence (AI) algorithms, ensuring an accurate estimation of human motion and proximity to the robot. Furthermore, a robot control module has been developed to modulate the robot’s speed based on the risk assessment calculated in real time, implementing a collaborative Speed and Separation Monitoring (SSM) scenario. To improve the efficiency of the workcell, the research proposes a multiplication coefficient to compute the minimum protective separation distance suggested by the ISO 10218-2, which weights the actual risk based on the biomechanical limits of the human part involved, according to the maximum permissible forces provided by the standard. Experimental results in a real robotic work cell demonstrate that the proposed system can achieve safety performance comparable to traditional motion capture systems, such as the OptiTrack; meanwhile it does not require operators to wear additional equipment and offers the advantages of reduced costs and a simpler setup, which is more affordable and adaptable to a wider range of industrial environments.

This article investigates the problem of controlling the speed of robots in collaborative workcells for automated manufacturing. The solution is tailored to robotic cells for cooperative assembly of aircraft fuselage panels, where only structural elements are present and robots and humans can share the same workspace, but no physical contact is allowed, unless it happens at zero robot speed. The proposed approach addresses the problem of satisfying the minimal set of requirements of an industrial human–robot collaboration (HRC) task: precision and reliability of human detection and tracking in the shared workspace; correct robot task execution with minimum cycle time while assuring safety for human operators. These requirements are often conflicting with each other. The former does not only concern with safety only but also with the need of avoiding unnecessary robot stops or slowdowns in case of false-positive human detection. The latter, according to the current regulations, concerns with the need of computing the minimum protective separation distance between the human operator and the robots by adjusting their speed when dangerous situations happen. This article proposes a novel fuzzy inference approach to control robot speed enforcing safety while maximizing the level of productivity of the robot minimizing cycle time as well. The approach is supported by a sensor fusion algorithm that merges the images acquired from different depth sensors with those obtained from a thermal camera, by using a machine learning approach. The methodology is experimentally validated in two experiments: the first one at a lab-scale and the second one performed on a full-scale robotic workcell for cooperative assembly of aeronautical structural parts. Note to Practitioners —This article discusses a way to handle human safety specifications versus production requirements in collaborative robotized assembly systems. State-of-the-art (SoA) approaches cover only a few aspects of both human detection and robot speed scaling. The present research work proposes a complete pipeline that starts from a robust human tracking algorithm and scales the robot speed in real time. An innovative multimodal perception system composed of two depth cameras and a thermal camera monitors the collaborative workspace. The speed scaling algorithm is optimized to take on different human behaviors during less risky situations or more dangerous ones to guarantee both operator safety and minimum production time with the aim of better profitability and efficiency for collaborative workstations. The algorithm estimates the operator intention for real-time computation of the minimum protective distance according to the current safety regulations. The robot speed is smoothly changed for the psychological advantages of operators, both in the case of single and multiple workers. The result is a complete system, easily implementable on a standard industrial workcell.

A robust grasp controller for both slipping avoidance and controlled sliding is proposed based on force/tactile feedback only. The model-based algorithm exploits a modified LuGre friction model to consider rotational frictional sliding motions. The modification relies on the Limit Surface concept where a novel computationally efficient method is introduced to compute in real-time the minimum grasping force to balance tangential and torsional loads. The two control modalities are considered by the robot motion planning algorithm that automatically generates robot motions and gripper commands to solve complex manipulation tasks in a material handling application.

Workspace monitoring is a critical hw/sw component of modern industrial work cells or in service robotics scenarios, where human operators share their workspace with robots. Reliability of human detection is a major requirement not only for safety purposes but also to avoid unnecessary robot stops or slowdowns in case of false positives. The present paper introduces a novel multimodal perception system for human tracking in shared workspaces based on the fusion of depth and thermal images. A machine learning approach is pursued to achieve reliable detection performance in multi-robot collaborative systems. Robust experimental results are finally demonstrated on a real robotic work cell.

The work implements a new real-time flexible motion planning method used for reactive object manipulation in pick and place tasks typical of in-store logistics scenarios such as shelf replenishment of retail stores.This method uses a new hybrid pipeline to recognize and localize an object observed through a depth camera, by integrating and optimizing state of the art techniques.The proposed algorithm guarantees recognition robustness and localization accuracy.The desired object is then manipulated.The motion planner, based on the obstacles detected in the scene, plans a collision-free path towards the target pose.The planned trajectory optimizes a cost function that reflects the best solution among those available and produces natural and smooth path through a smart IK constrained solution which avoids robot unnecessary reconfigurations.A reactive control based on distributed proximity sensors is finally adopted to locally modify the planned trajectory in real time to avoid collisions with uncertain or dynamic obstacles.Experimental results in a supermarket scenario populated with cluttered obstacles demonstrate smoothness of the robot motions and reactive capabilities in a typical fetch and carry task.