In this article, an optimal controller for achieving constrained admittance control is proposed. This controller strictly adheres to the constraint boundaries while ensuring minimal variations in kinematic energy. The proposed method integrates admittance control for human-robot interaction with the Udwadia-Kalaba equations for constrained motion into a unified framework. The proposed architecture has been tested and validated both with simulations and real tests on a 6-DoF UR5e robot. The results demonstrate that the proposed architecture outperforms virtual fixtures, one of the most commonly used techniques to implement effective path-following control.

Learning by demonstration techniques are gaining popularity within the human-robot collaboration (HRC) scenarios. This is because they allow to deeply exploit the versatility of collaborative robots. In this context, dynamic motion primitives (DMPs) have become a standard method for enabling human operators to easily teach tasks to robots. However, DMPs have two main limitations. First, they may encounter difficulties in generalizing some tasks, which can lead to non-intuitive behavior. Second, it is not guaranteed that the output of DMPs is compliant with ISO/TS 15066, which provides guidelines for assessing safety in collaborative scenarios. This work aims to address these two issues by introducing a novel control pipeline. This pipeline leverages a new variant of DMPs, called Swap DMPs (SDMPs), introduced in this work. The SDMPs enable a more intuitive behavior when the robot reproduces the learned task. Subsequently, SDMPs are encoded into a new optimization problem that ensures the robot complies with the Speed and Separation Monitoring (SSM) collaborative mode. The proposed approach has been experimentally validated and compared with traditional DMPs in both simulation and a real scenario, where a UR5e and a human operator collaborate on a polishing task.

In this paper, we propose a closed-loop sensitivity-based approach to enhance the robustness of robotic non-prehensile dynamic manipulation tasks. The proposed method aims at fulfilling the transportation of an object, that is free to move on a tray-shaped robot end-effector, in face of not perfectly known nominal dynamic parameters. The approach is built up on taking the parameterized reference trajectory to be tracked as the optimization variable minimizing a norm of the task closed-loop sensitivity. The resulting optimal reference trajectory is inherently more robust to the parametric variations of object dynamic properties compared to a baseline trajectory execution. The tracking performance is assessed and validated along hardware experiments and an extensive simulation campaign assessing the superior robustness of our approach.

Ensuring safe and effective physical human-robot interaction (pHRI) remains a critical challenge in industrial robotics, particularly in ensuring compliance with ISO/TS 15066 safety standards. This paper proposes a novel framework to achieve a safe and robust physical human-robot interaction (pHRI). The framework adapts the parameters of a variable admittance controller online in order to guarantee passivity and compliance with ISO/TS 15066. Passivity is guaranteed using an energy tank, while a safety constraint explicitly handles the Power and Force Limiting (PFL) energy limit. Experimental validation on an industrial robot demonstrates the effectiveness of the framework.

Collaborative robots (cobots) have seen widespread adoption in industrial applications over the last decade. Cobots can be placed outside protective cages and are generally regarded as much more intuitive and easy to program compared to larger classical industrial robots. However, despite the cobots’ widespread adoption, their collaborative potential and opportunity to aid flexible production processes seem hindered by a lack of training and understanding from shop floor workers. Researchers have focused on technical solutions, which allow novice robot users to more easily train collaborative robots. However, most of this work has yet to leave research labs. Therefore, training methods are needed with the goal of transferring skills and knowledge to shop floor workers about how to program collaborative robots. We identify general basic knowledge and skills that a novice must master to program a collaborative robot. We present how to structure and facilitate cobot training based on cognitive apprenticeship and test the training framework on a total of 20 participants using a UR10e and UR3e robot. We considered two conditions: adaptive and self-regulated training. We found that the facilitation was effective in transferring knowledge and skills to novices, however, found no conclusive difference between the adaptive or self-regulated approach. The results demonstrate that, thanks to the proposed training method, both groups are able to significantly reduce task time, achieving a reduction of 40%, while maintaining the same level of performance in terms of position error.Note to Practitioners—This paper was motivated by the fact that the adoption of smaller, so-called collaborative robots is increasing within manufacturing but the potential for a single robot to be used flexibly in multiple places of a production seems unfulfilled. If more unskilled workers understood the collaborative robots and received structured training, they would be capable of programming the robots independently. This could change the current landscape of stationary collaborative robots towards more flexible robot use and thereby increase companies’ internal overall equipment efficiency and competencies. To this end, we identify general skills and knowledge for programming a collaborative robot, which helps increase the transparency of what novices need to know. We show how such knowledge and skills may be facilitated in a structured training framework, which effectively transfers necessary programming knowledge and skills to novices. This framework may be applied to a wider scope of knowledge and skills as the learner progresses. The skills and knowledge that we identify are general across robot platforms, however, collaborative robot interfaces differ. Therefore, a practical limitation to the approach includes the need for a knowledgeable person on the specific collaborative robot in question in order to create training material in areas specific to that model. However, with our list of identified skills, it provides an easier starting point. We show that relatively few skills and knowledge areas can enhance a novice’s programming capability.

The integration of robots into shared workspaces alongside humans is the basis of Human-Robot Collaboration (HRC). This field of research has changed the paradigm of the industrial context, making HRC of pivotal importance for both researchers and the industry. In this context, a suitable task scheduling and trajectory planning strategy are crucial to achieve good performances and create a synergy between the two actors. Indeed, the task scheduling should be able to optimally distribute the tasks between the actors and recover from possible failures, i.e. by rescheduling the tasks. The trajectory planning strategy must comply with the safety standards that impose a reduction of velocity based on human behaviour. To this end, the monitoring system must also be safe-certified; otherwise, safety cannot be guaranteed. This paper proposes a novel architecture that integrates a dynamic task scheduling module with a dynamic trajectory planning module that explicitly considers ISO/TS 15066. For this purpose, the framework exploits a secure and certified monitoring system capable of tracking the human operator even in case of occlusions. The overall platform has been extensively validated both in a real and complex industrial scenario within the context of the ROSSINI EU project, where a dual-arm mobile robot collaborates with a human operator in an automatic machine-tending operation, and in a mock-up scenario.

The human-robot collaboration scenarios are characterized by the presence of human operators and robots that work in close contact with each other. As a consequence, the safety regulations have been updated in order to provide guidelines on how to asses safety in these new scenarios. In particular, Power and Force Limiting (PFL) collaborative mode describes how the energy should be regulated during the collaboration. Based on these guidelines, we propose a new optimal trajectory planner which, by exploiting the variability of the robot’s inertia as a function of its configuration, is able to return trajectories that can be travelled at greater speed and in less time, while guaranteeing the safety limits according to the standard. The proposed planner was validated first in simulation, comparing completion times with other state-of-the-art planning algorithms, and then experimentally, demonstrating the performance of the planned trajectories during physical interaction with the environment. Both validations confirm the effectiveness of the proposed planner, which returns shorter completion times while ensuring safe interaction.

In the actual industrial scenarios, human operators and robots work together sharing the workspace. Such proximity requires special attention in ensuring safety for the human operator, which is often translated in collision avoidance behaviour or high speed reduction. Adhering safety however is not the only aspect that must be taken into account. For many tasks, such as welding, it is crucial to ensure that the robot performs exactly the planned path. To optimize robot performance while complying with safety regulations, this work introduces a novel optimal nonlinear control problem. It prioritizes path preservation, exploiting redundancy to minimize task execution time, while explicitly adhering to the constraints imposed by ISO/TS 15066. To achieve high-performance outcomes, the control problem is addressed using the Model Predictive Control (MPC) approach. The proposed strategy has been experimentally validated in both simulations and a real-world industrial task involving a Kuka LWR4+ robot.

In the context of Human-Robot Collaboration (HRC), it is crucial that the two actors are able to communicate with each other in a natural and efficient manner. The absence of a communication interface is often a cause of undesired slowdowns. On one hand, this is because unforeseen events may occur, leading to errors. On the other hand, due to the close contact between humans and robots, the speed must be reduced significantly to comply with safety standard ISO/TS 15066. In this paper, we propose a novel architecture that enables operators and robots to communicate efficiently, emulating human-to-human dialogue, while addressing safety concerns. This approach aims to establish a communication framework that not only facilitates collaboration but also reduces undesired speed reduction. Through the use of a predictive simulator, we can anticipate safety-related limitations, ensuring smoother workflows, minimizing risks, and optimizing efficiency. The overall architecture has been validated with a UR10e and compared with a state of the art technique. The results show a significant improvement in user experience, with a corresponding 23% reduction in execution times and a 50% decrease in robot downtime.

This paper presents a Human-Robot Blind Handover architecture within the context of Human-Robot Collaboration (HRC). The focus lies on a blind handover scenario where the operator is intentionally faced away, focused in a task, and requires an object from the robot. In this context, it is imperative for the robot to autonomously manage the entire handover process. Key considerations include ensuring safety while handing the object to the operator’s hand, and detect the proper timing to release the object. The article explores strategies to navigate these challenges, emphasizing the need for a robot to operate safely and independently in facilitating blind handovers, thereby contributing to the advancement of HRC protocols and fostering a natural and efficient collaboration between humans and robots.

Energy tanks have gained popularity inside the robotics and control communities over the last years, since they represent a formidable tool to enforce passivity (and, thus, input/output stability) of a controlled robot, possibly interacting with uncertain environments. One weak point of passification strategies based on energy tanks concerns, however, their initialization. Indeed, a too large initial energy can cause practical unstable behaviors, while a too low initial energy level can prevent the correct execution of the task. This shortcoming becomes even more relevant in presence of uncertainties in the robot model and/or environment, since it may be hard to predict in advance the correct (safe) amount of initial tank energy for a successful task execution. In this paper we then propose a new strategy for addressing this issue. The recent notion of closed-loop state sensitivity is exploited to derive precise bounds (tubes) on the tank energy behavior by assuming parametric uncertainty in the robot model. These tubes are then exploited in a novel nonlinear optimization problem aiming at finding both the best trajectory and the minimal initial tank energy that allow executing a positioning task for any value of the uncertain parameters in a given range. The approach is finally validated via a statistical analysis in simulation and experiments on real robot hardware.

In collaborative robotic applications, human and robot have to work together to accomplish a common job, composed by a set of tasks. In order to achieve an efficient human-robot collaboration (HRC), it is important to have an integration between a proper task scheduling strategy and a task execution strategy. The first must deal with the variability of the two agents, while the second must deal with the safety standards. In this paper, we propose an integrated architecture for task scheduling and execution in a collaborative cell. The tasks are dynamically scheduled handling the uncertainity in both the human and the robot behaviors. Subsequently, at the execution level, the task is accomplished computing trajectories comply with the safety regulations. The planning information are mutually integrated in real-time with the scheduling procedure in order improve the HRC.