Survey on human–robot collaboration in industrial settings: Safety, intuitive interfaces and applications

Although the concept of industrial cobots dates back to 1999, most present day hybrid human-machine assembly systems are merely weight compensators. Here, we present results on the development of a collaborative human-robot manufacturing cell for homokinetic joint assembly. The robot alternates active and passive behaviours during assembly, to lighten the burden on the operator in the first case, and to comply to his/her needs in the latter. Our approach can successfully manage direct physical contact between robot and human, and between robot and environment. Furthermore, it can be applied to standard position (and not torque) controlled robots, common in the industry. The approach is validated in a series of assembly experiments. The human workload is reduced, diminishing the risk of strain injuries. Besides, a complete risk analysis indicates that the proposed setup is compatible with the safety standards, and could be certified.

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Collaborative manufacturing with physical human–robot interaction

Foundations of Robotics presents the fundamental concepts and methodologies for the analysis, design, and control of robot manipulators It explains the physical meaning of the concepts and equations used, and it provides, in an intuitively clear way, the necessary background in kinetics, linear algebra, and control theory Illustrative examples appear throughoutThe author begins by discussing typical robot manipulator mechanisms and their controllers He then devotes three chapters to the analysis of robot manipulator mechanisms He covers the kinematics of robot manipulators, describing the motion of manipulator links and objects related to manipulation A chapter on dynamics includes the derivation of the dynamic equations of motion, their use for control and simulation and the identification of inertial parameters The final chapter develops the concept of manipulabilityThe second half focuses on the control of robot manipulators Various position-control algorithms that guide the manipulator's end effector along a desired trajectory are described Two typical methods used to control the contact force between the end effector and its environments are detailed For manipulators with redundant degrees of freedom, a technique to develop control algorithms for active utilization of the redundancy is described Appendixes give compact reviews of the function atan2, pseudo inverses, singular-value decomposition, and Lyapunov stability theoryTsuneo Yoshikawa teaches in the Division of Applied Systems Science in Kyoto University's Faculty of Engineering

Foundations of robotics : analysis and control

The wide interest of research and industry in the human–robot interaction HRI related topics is proportional to the increased productivity and flexibility of the production lines, as it combines human and robot capabilities. This paper presents a review of recent research and progress on HRI, related to task planning/coordination and programming with emphasis on the manufacturing/production environment. Human–robot task allocation and scheduling, metrics for HRI, as well as the social aspects are reviewed. The role of digital human modelling systems for human–robot task planning related issues is also discussed. The process of learning by demonstration as well as the instructive systems is reviewed, focussing mainly on programming through visual guidance and imitation, voice commands and haptic interaction. The aspect of physical HRI as well as the safety related issues are also discussed. Additionally, a survey on multimodal communication frameworks is presented. Challenges encountered and directions for future research are discussed.

Human–robot interaction review and challenges on task planning and programming

Symbiotic human-robot collaborative assembly

Motion planning and scheduling for human and industrial-robot collaboration

Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human’s fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved human-robot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system.

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

The standard EN ISO10218 is fostering the implementation of hybrid production systems, i.e., production systems characterized by a close relationship among human operators and robots in cooperative tasks. Human-robot hybrid systems could have a big economic benefit in small and medium sized production, even if this new paradigm introduces mandatory, challenging safety aspects. Among various requirements for collaborative workspaces, safety-assurance involves two different application layers; the algorithms enabling safe space-sharing between humans and robots and the enabling technologies allowing acquisition data from sensor fusion and environmental data analysing. This paper addresses both the problems: a collision avoidance strategy allowing on-line re-planning of robot motion and a safe network of unsafe devices as a suggested infrastructure for functional safety achievement.

https://journals.sagepub.com/doi/pdf/10.5772/53939

Safe Human-Robot Cooperation in an Industrial Environment

The letter presents a force-tracking impedance controller granting a free-overshoots contact force (mandatory performance for many critical interaction tasks such as polishing) for partially unknown interacting environments (such as leather or hard-fragile materials). As in many applications, the robot has to gently approach the target environment (whose position is usually not well-known), then execute the interaction task. Therefore, the algorithm has been designed to deal with both the free space approaching motion (phase a.) and the succeeding contact task (phase b.) without switching from different control logics. Control gains have to be properly calculated for each phase in order to achieve the target force tracking performance (i.e., free-overshoots contact force). In detail, phase a. control gains are optimized based on the impact collision model to minimize the force error during the following contact task, while phase b. control gains are analytically calculated based on the solution of the LQR optimal control problem. The analytical solution grants the continuous adaptation of the control gains during the contact phase on the estimated value of the environment stiffness (obtained through an on-line extended Kalman filter). A probing task has been carried out to validate the performance of the control with partially unknown contact environment properties. Results show the avoidance of force overshoots and instabilities.

Optimal Impedance Force-Tracking Control Design With Impact Formulation for Interaction Tasks

The paper focuses on industrial interaction robotics tasks, investigating a control approach involving multiples learning levels for training the manipulator to execute a repetitive (partially) changeable task, accurately controlling the interaction. Based on compliance control, the proposed approach consists of two main control levels: 1) iterative friction learning compensation controller with reinforcement and 2) iterative force-tracking learning controller with reinforcement. The learning algorithms rely on the iterative learning and reinforcement learning procedures to automatize the controllers parameters tuning. The proposed procedure has been applied to an automotive industrial assembly task. A standard industrial UR 10 Universal Robot has been used, equipped by a compliant pneumatic gripper and a force/torque sensor at the robot end-effector.

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Nicola Pedrocchi

Papers

Motion planning and scheduling for human and industrial-robot collaboration

Model-Based Reinforcement Learning Variable Impedance Control for Human-Robot Collaboration

Safe Human-Robot Cooperation in an Industrial Environment

Optimal Impedance Force-Tracking Control Design With Impact Formulation for Interaction Tasks

Iterative Learning Procedure With Reinforcement for High-Accuracy Force Tracking in Robotized Tasks