Compliant Blind Handover Control for Human-Robot Collaboration

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.

The Critical Role of Effective Communication in Human-Robot Collaborative Assembly

In the rapidly evolving landscape of Human-Robot Collaboration (HRC), effective communication between humans and robots is crucial for complex task execution. Traditional request-response systems often lack naturalness and may hinder efficiency. This study emphasizes the importance of adopting human-like communication interactions to enable fluent vocal communication between human operators and robots simulating a collaborative human-robot industrial assembly. We propose a novel approach that employs human-like interactions through natural dialogue, enabling human operators to engage in vocal conversations with robots. Through a comparative experiment, we demonstrate the efficacy of our approach in enhancing task performance and collaboration efficiency. The robot's ability to engage in meaningful vocal conversations enables it to seek clarification, provide status updates, and ask for assistance when required, leading to improved coordination and a smoother workflow. The results indicate that the adoption of human-like conversational interactions positively influences the human-robot collaborative dynamic. Human operators find it easier to convey complex instructions and preferences, resulting in a more productive and satisfying collaboration experience.

Collaborative Conversation in Safe Multimodal Human-Robot Collaboration

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.

Facilitating Human-Robot Collaboration through Natural Vocal Conversations

In the rapidly evolving landscape of human-robot collaboration, effective communication between humans and robots is crucial for complex task execution. Traditional request-response systems often lack naturalness and may hinder efficiency. In this study, we propose a novel approach that employs human-like conversational interactions for vocal communication between human operators and robots. The framework emphasizes the establishment of a natural and interactive dialogue, enabling human operators to engage in vocal conversations with robots. Through a comparative experiment, we demonstrate the efficacy of our approach in enhancing task performance and collaboration efficiency. The robot's ability to engage in meaningful vocal conversations enables it to seek clarification, provide status updates, and ask for assistance when required, leading to improved coordination and a smoother workflow. The results indicate that the adoption of human-like conversational interactions positively influences the human-robot collaborative dynamic. Human operators find it easier to convey complex instructions and preferences, fostering a more productive and satisfying collaboration experience.

Beyond Jacobian-based tasks: Extended set-based tasks for multi-task execution and prioritization

The ability of executing multiple tasks simultaneously is an important feature of redundant robotic systems. As a matter of fact, complex behaviors can often be obtained as a result of the execution of several tasks. Moreover, in safety-critical applications, tasks designed to ensure the safety of the robot and its surroundings have to be executed along with other nominal tasks. In such cases, it is also important to prioritize the former over the latter. In this paper, we formalize the definition of extended set-based tasks, i.e., tasks which can be executed by rendering subsets of the task space asymptotically stable or forward invariant. We propose a mathematical representation of such tasks that allows for the execution of more complex and time-varying prioritized stacks of tasks using kinematic and dynamic robot models alike. We present and analyze an optimization-based framework which is computationally efficient, accounts for input bounds, and allows for the stable execution of time-varying prioritized stacks of extended set-based tasks. The proposed framework is validated using extensive simulations and experiments with robotic manipulators.

Safe Multimodal Communication in Human-Robot Collaboration

The new industrial settings are characterized by the presence of human and robots that work in close proximity, cooperating in performing the required job. Such a collaboration, however, requires to pay attention to many aspects. Firstly, it is crucial to enable a communication between this two actors that is natural and efficient. Secondly, the robot behavior must always be compliant with the safety regulations, ensuring always a safe collaboration. In this paper, we propose a framework that enables multi-channel communication between humans and robots by leveraging multimodal fusion of voice and gesture commands while always respecting safety regulations. The framework is validated through a comparative experiment, demonstrating that, thanks to multimodal communication, the robot can extract valuable information for performing the required task and additionally, with the safety layer, the robot can scale its speed to ensure the operator's safety.

Energy Tank-based Control Framework for Satisfying the ISO/TS 15066 Constraint

The technical specification ISO/TS 15066 provides the foundational elements for assessing the safety of collaborative human-robot cells, which are the cornerstone of the modern industrial paradigm. The standard implementation of the ISO/TS 15066 procedure, however, often results in conservative motions of the robot, with consequently low performance of the cell. In this paper, we propose an energy tank-based approach that allows to directly satisfy the energetic bounds imposed by the ISO/TS 15066, thus avoiding the introduction of conservative modeling and assumptions. The proposed approach has been successfully validated in simulation.

A Null-space based Approach for a Safe and Effective Human-Robot Collaboration

During physical human robot collaboration, it is important to be able to implement a time-varying interactive behaviour while ensuring robust stability. Admittance control and passivity theory can be exploited for achieving these objectives. Nevertheless, when the admittance dynamics is time-varying, it can happen that, for ensuring a passive and stable behaviour, some spurious dissipative effects have to be introduced in the admittance dynamics. These effects are perceived by the user and degrade the collaborative performance. In this paper we exploit the task redundancy of the manipulator in order to harvest energy in the null space and to avoid spurious dynamics on the admittance. The proposed architecture is validated by simulations and by experiments onto a collaborative robot.

Whole-Body Control of a Mobile Manipulator for Passive Collaborative Transportation

Human-robot collaborative tasks foresee interactions between humans and robots with various degrees of complexity. Specifically, for tasks which involve physical contact among the agents, challenges arise in the modelling and control of such interaction. In this paper we propose a control architecture capable of ensuring a flexible and robustly stable physical human-robot interaction, focusing on a collaborative transportation task. The architecture is deployed onto a mobile manipulator, modelled as a whole-body structure, which aids the operator during the transportation of an unwieldy load. Thanks to passivity techniques, the controller adapts its interaction parameters online while preserving robust stability for the overall system, thus experimentally validating the architecture.

Bidirectional Communication Control for Human-Robot Collaboration

A fruitful collaboration is based on the mutual knowledge of each other skills and on the possibility of communicating their own limits and proposing alternatives to adapt the execution of a task to the capabilities of the collaborators. This paper aims at reproducing such a scenario in a human-robot collaboration setting by proposing a novel communication control architecture. Exploiting control barrier functions, the robot is made aware of its (dynamic) skills and limits and, thanks to a local predictor, it is able to assess if it is possible to execute a requested task and, if not, to propose alternative by relaxing some constraints. The controller is interfaced with a communication infrastructure that enables human and robot to set up a bidirectional communication about the task to execute and the human to take an informed decision on the behavior of the robot. A comparative experimental validation is proposed.