Human-Robot Collaboration for Kinesthetic Teaching

Recent industrial interest in producing smaller volumes of products in shorter time frames, in contrast to mass production in previous decades, motivated the introduction of human–robot collaboration (HRC) in industrial settings, as an attempt to increase flexibility in manufacturing applications by incorporating human intelligence and dexterity to these processes. This thesis presents methods for improving the involvement of human operators in industrial settings where robots are present, with a particular focus on kinesthetic teaching, i.e., manually guiding the robot to define or correct its motion, since it can facilitate non-expert robot programming.To increase flexibility in the manufacturing industry implies a loss of a fixed structure of the industrial environment, which increases the uncertainties in the shared workspace between humans and robots. Two methods have been proposed in this thesis to mitigate such uncertainty. First, null-space motion was used to increase the accuracy of kinesthetic teaching by reducing the joint static friction, or stiction, without altering the execution of the robotic task. This was possible since robots used in HRC, i.e., collaborative robots, are often designed with additional degrees of freedom (DOFs) for a greater dexterity. Second, to perform effective corrections of the motion of the robot through kinesthetic teaching in partially-unknown industrial environments, a fast identification of the source of robot–environment contact is necessary. Fast contact detection and classification methods in literature were evaluated, extended, and modified to use them in kinesthetic teaching applications for an assembly task. For this, collaborative robots that are made compliant with respect to their external forces/torques (as an active safety mechanism) were used, and only embedded sensors of the robot were considered.Moreover, safety is a major concern when robotic motion occurs in an inherently uncertain scenario, especially if humans are present. Therefore, an online variation of the compliant behavior of the robot during its manual guidance by a human operator was proposed to avoid undesired parts of the workspace of the robot. The proposed method used safety control barrier functions (SCBFs) that considered the rigid-body dynamics of the robot, and the method’s stability was guaranteed using a passivity-based energy-storage formulation that includes a strict Lyapunov function.All presented methods were tested experimentally on a real collaborative robot

Bayesian estimation of human impedance and motion intention for human-robot collaboration

This article proposes a Bayesian method to acquire the estimation of human impedance and motion intention in a human-robot collaborative task. Combining with the prior knowledge of human stiffness, estimated stiffness obeying Gaussian distribution is obtained by Bayesian estimation, and human motion intention can be also estimated. An adaptive impedance control strategy is employed to track a target impedance model and neural networks are used to compensate for uncertainties in robotic dynamics. Comparative simulation results are carried out to verify the effectiveness of estimation method and emphasize the advantages of the proposed control strategy. The experiment, performed on Baxter robot platform, illustrates a good system performance

Working together: a review on safe human-robot collaboration in industrial environments

After many years of rigid conventional procedures of production, industrial manufacturing is going through a process of change toward flexible and intelligent manufacturing, the so-called Industry 4.0. In this paper, human-robot collaboration has an important role in smart factories since it contributes to the achievement of higher productivity and greater efficiency. However, this evolution means breaking with the established safety procedures as the separation of workspaces between robot and human is removed. These changes are reflected in safety standards related to industrial robotics since the last decade, and have led to the development of a wide field of research focusing on the prevention of human-robot impacts and/or the minimization of related risks or their consequences. This paper presents a review of the main safety systems that have been proposed and applied in industrial robotic environments that contribute to the achievement of safe collaborative human-robot work. Additionally, a review is provided of the current regulations along with new concepts that have been introduced in them. The discussion presented in this paper includes multidisciplinary approaches, such as techniques for estimation and the evaluation of injuries in human-robot collisions, mechanical and software devices designed to minimize the consequences of human-robot impact, impact detection systems, and strategies to prevent collisions or minimize their consequences when they occur

Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement

[EN] Due to the elasticity of their joints, collaborative robots are seldom used in applications with force control. Besides, the industrial robot controllers are closed and do not allow the user to access the motor torques and other parameters, hindering the possibility of carrying out a customized control. A good alternative to achieve a custom force control is sending the output of the force regulator to the robot controller through motion commands (inner/outer loop control). There are different types of motion commands (e.g., position or velocity). They may be implemented in different ways (Jacobian inverse vs. Jacobian transpose), but this information is usually not available for the user. This article is dedicated to the analysis of the effect of different inner loops and their combination with several external controllers. Two of the most determinant factors found are the type of the inner loop and the stiffness matrix. The theoretical deductions have been experimentally verified on a collaborative robot UR3, allowing us to choose the best behaviour in a polishing operation according to pre-established criteria.The authors are grateful for the financial support of the Spanish Ministry of Economy and European Union, grant DPI2016-81002-R (AEI/FEDER, UE), to the research work here published. Rodrigo Perez-Ubeda is grateful to the Ph.D. Grant CONICYT PFCHA/DOCTORADO BECAS CHILE/2017-72180157.Pérez-Ubeda, R.; Zotovic Stanisic, R.; Gutiérrez, SC. (2020). Force Control Improvement in Collaborative Robots through Theory Analysis and Experimental Endorsement. Applied Sciences. 10(12):1-24. https://doi.org/10.3390/app10124329S1241012Top Trends Robotics 2020—International Federation of Robotics https://ifr.org/ifr-press-releases/news/top-trends-robotics-2020Gaz, C., Magrini, E., & De Luca, A. (2018). A model-based residual approach for human-robot collaboration during manual polishing operations. 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Choosing Poses for Force and Stiffness Control. IEEE Transactions on Robotics, 33(6), 1483-1490. doi:10.1109/tro.2017.2708087Magrini, E., & De Luca, A. (2016). Hybrid force/velocity control for physical human-robot collaboration tasks. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). doi:10.1109/iros.2016.7759151Ahmad, S. (1993). Constrained motion (force/position) control of flexible joint robots. IEEE Transactions on Systems, Man, and Cybernetics, 23(2), 374-381. doi:10.1109/21.229451Calanca, A., & Fiorini, P. (2018). Understanding Environment-Adaptive Force Control of Series Elastic Actuators. IEEE/ASME Transactions on Mechatronics, 23(1), 413-423. doi:10.1109/tmech.2018.2790350Oh, S., & Kong, K. (2017). High-Precision Robust Force Control of a Series Elastic Actuator. IEEE/ASME Transactions on Mechatronics, 22(1), 71-80. doi:10.1109/tmech.2016.2614503Yin, H., Li, S., & Wang, H. (2016). Sliding mode position/force control for motion synchronization of a flexible-joint manipulator system with time delay. 2016 35th Chinese Control Conference (CCC). doi:10.1109/chicc.2016.7554329Ma, Z., Hong, G.-S., Ang, M. H., Poo, A.-N., & Lin, W. (2018). A Force Control Method with Positive Feedback for Industrial Finishing Applications. 2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). doi:10.1109/aim.2018.8452689Huang, L., Ge, S. S., & Lee, T. H. (2006). Position/force control of uncertain constrained flexible joint robots. Mechatronics, 16(2), 111-120. doi:10.1016/j.mechatronics.2005.10.002Chiaverini, S., Siciliano, B., & Villani, L. (1999). A survey of robot interaction control schemes with experimental comparison. IEEE/ASME Transactions on Mechatronics, 4(3), 273-285. doi:10.1109/3516.789685Winkler, A., & Suchy, J. (2016). Explicit and implicit force control of an industrial manipulator — An experimental summary. 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). doi:10.1109/mmar.2016.7575081Neranon, P., & Bicker, R. (2016). Force/position control of a robot manipulator for human-robot interaction. Thermal Science, 20(suppl. 2), 537-548. doi:10.2298/tsci151005036nChen, S., Zhang, T., & Zou, Y. (2017). Fuzzy-Sliding Mode Force Control Research on Robotic Machining. Journal of Robotics, 2017, 1-8. doi:10.1155/2017/8128479Lin, H.-I., & Dubey, V. (2018). Design of an Adaptive Force Controlled Robotic Polishing System Using Adaptive Fuzzy-PID. Advances in Intelligent Systems and Computing, 825-836. doi:10.1007/978-3-030-01370-7_64Perez-Vidal, C., Gracia, L., Sanchez-Caballero, S., Solanes, J. E., Saccon, A., & Tornero, J. (2019). Design of a polishing tool for collaborative robotics using minimum viable product approach. International Journal of Computer Integrated Manufacturing, 32(9), 848-857. doi:10.1080/0951192x.2019.1637026Chen, F., Zhao, H., Li, D., Chen, L., Tan, C., & Ding, H. (2019). Contact force control and vibration suppression in robotic polishing with a smart end effector. Robotics and Computer-Integrated Manufacturing, 57, 391-403. doi:10.1016/j.rcim.2018.12.019Mohammad, A. E. K., Hong, J., & Wang, D. (2018). Design of a force-controlled end-effector with low-inertia effect for robotic polishing using macro-mini robot approach. Robotics and Computer-Integrated Manufacturing, 49, 54-65. doi:10.1016/j.rcim.2017.05.011Xiao, C., Wang, Q., Zhou, X., Xu, Z., Lao, X., & Chen, Y. (2019). Hybrid Force/Position Control Strategy for Electromagnetic based Robotic Polishing Systems. 2019 Chinese Control Conference (CCC). doi:10.23919/chicc.2019.8865183Li, J., Zhang, T., Liu, X., Guan, Y., & Wang, D. (2018). A Survey of Robotic Polishing. 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). doi:10.1109/robio.2018.8664890Zollo, L., Siciliano, B., De Luca, A., Guglielmelli, E., & Dario, P. (2004). Compliance Control for an Anthropomorphic Robot with Elastic Joints: Theory and Experiments. Journal of Dynamic Systems, Measurement, and Control, 127(3), 321-328. doi:10.1115/1.1978911Han, D., Duan, X., Li, M., Cui, T., Ma, A., & Ma, X. (2017). Interaction Control for Manipulator with compliant end-effector based on hybrid position-force control. 2017 IEEE International Conference on Mechatronics and Automation (ICMA). doi:10.1109/icma.2017.8015929Schindlbeck, C., & Haddadin, S. (2015). Unified passivity-based Cartesian force/impedance control for rigid and flexible joint robots via task-energy tanks. 2015 IEEE International Conference on Robotics and Automation (ICRA). doi:10.1109/icra.2015.7139036Zotovic Stanisic, R., & Valera Fernández, Á. (2009). 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The International Journal of Robotics Research, 19(9), 835-847. doi:10.1177/02783640022067201Institute of Robotics and Mechatronics DLR Light Weight Robot III https://www.dlr.de/rm/en/desktopdefault.aspx/tabid-12464/#gallery/2916

Towards the development of safe, collaborative robotic freehand ultrasound

The use of robotics in medicine is of growing importance for modern health services, as robotic systems have the capacity to improve upon human tasks, thereby enhancing the treatment ability of a healthcare provider. In the medical sector, ultrasound imaging is an inexpensive approach without the high radiation emissions often associated with other modalities, especially when compared to MRI and CT imaging respectively. Over the past two decades, considerable effort has been invested into freehand ultrasound robotics research and development. However, this research has focused on the feasibility of the application, not the robotic fundamentals, such as motion control, calibration, and contextual awareness. Instead, much of the work is concentrated on custom designed robots, ultrasound image generation and visual servoing, or teleoperation. Research based on these topics often suffer from important limitations that impede their use in an adaptable, scalable, and real-world manner. Particularly, while custom robots may be designed for a specific application, commercial collaborative robots are a more robust and economical solution. Otherwise, various robotic ultrasound studies have shown the feasibility of using basic force control, but rarely explore controller tuning in the context of patient safety and deformable skin in an unstructured environment. Moreover, many studies evaluate novel visual servoing approaches, but do not consider the practicality of relying on external measurement devices for motion control. These studies neglect the importance of robot accuracy and calibration, which allow a system to safely navigate its environment while reducing the imaging errors associated with positioning. Hence, while the feasibility of robotic ultrasound has been the focal point in previous studies, there is a lack of attention to what occurs between system design and image output. This thesis addresses limitations of the current literature through three distinct contributions. Given the force-controlled nature of an ultrasound robot, the first contribution presents a closed-loop calibration approach using impedance control and low-cost equipment. Accuracy is a fundamental requirement for high-quality ultrasound image generation and targeting. This is especially true when following a specified path along a patient or synthesizing 2D slices into a 3D ultrasound image. However, even though most industrial robots are inherently precise, they are not necessarily accurate. While robot calibration itself has been extensively studied, many of the approaches rely on expensive and highly delicate equipment. Experimental testing showed that this method is comparable in quality to traditional calibration using a laser tracker. As demonstrated through an experimental study and validated with a laser tracker, the absolute accuracy of a collaborative robot was improved to a maximum error of 0.990mm, representing a 58.4% improvement when compared to the nominal model. The second contribution explores collisions and contact events, as they are a natural by-product of applications involving physical human-robot interaction (pHRI) in unstructured environments. Robot-assisted medical ultrasound is an example of a task where simply stopping the robot upon contact detection may not be an appropriate reaction strategy. Thus, the robot should have an awareness of body contact location to properly plan force-controlled trajectories along the human body using the imaging probe. This is especially true for remote ultrasound systems where safety and manipulability are important elements to consider when operating a remote medical system through a communication network. A framework is proposed for robot contact classification using the built-in sensor data of a collaborative robot. Unlike previous studies, this classification does not discern between intended vs. unintended contact scenarios, but rather classifies what was involved in the contact event. The classifier can discern different ISO/TS 15066:2016 specific body areas along a human-model leg with 89.37% accuracy. Altogether, this contact distinction framework allows for more complex reaction strategies and tailored robot behaviour during pHRI. Lastly, given that the success of an ultrasound task depends on the capability of the robot system to handle pHRI, pure motion control is insufficient. Force control techniques are necessary to achieve effective and adaptable behaviour of a robotic system in the unstructured ultrasound environment while also ensuring safe pHRI. While force control does not require explicit knowledge of the environment, to achieve an acceptable dynamic behaviour, the control parameters must be tuned. The third contribution proposes a simple and effective online tuning framework for force-based robotic freehand ultrasound motion control. Within the context of medical ultrasound, different human body locations have a different stiffness and will require unique tunings. Through real-world experiments with a collaborative robot, the framework tuned motion control for optimal and safe trajectories along a human leg phantom. The optimization process was able to successfully reduce the mean absolute error (MAE) of the motion contact force to 0.537N through the evolution of eight motion control parameters. Furthermore, contextual awareness through motion classification can offer a framework for pHRI optimization and safety through predictive motion behaviour with a future goal of autonomous pHRI. As such, a classification pipeline, trained using the tuning process motion data, was able to reliably classify the future force tracking quality of a motion session with an accuracy of 91.82 %

Human-robot interaction using a behavioural control strategy

PhD ThesisA topical and important aspect of robotics research is in the area of human-robot interaction (HRI), which addresses the issue of cooperation between a human and a robot to allow tasks to be shared in a safe and reliable manner. This thesis focuses on the design and development of an appropriate set of behaviour strategies for human-robot interactive control by first understanding how an equivalent human-human interaction (HHI) can be used to establish a framework for a robotic behaviour-based approach. To achieve the above goal, two preliminary HHI experimental investigations were initiated in this study. The first of which was designed to evaluate the human dynamic response using a one degree-of-freedom (DOF) HHI rectilinear test where the handler passes a compliant object to the receiver along a constrained horizontal path. The human dynamic response while executing the HHI rectilinear task has been investigated using a Box-Behnken design of experiments [Box and Hunter, 1957] and was based on the McRuer crossover model [McRuer et al. 1995]. To mimic a real-world human-human object handover task where the handler is able to pass an object to the receiver in a 3D workspace, a second more substantive one DOF HHI baton handover task has been developed. The HHI object handover tests were designed to understand the dynamic behavioural characteristics of the human participants, in which the handler was required to dexterously pass an object to the receiver in a timely and natural manner. The profiles of interactive forces between the handler and receiver were measured as a function of time, and how they are modulated whilst performing the tasks, was evaluated. Three key parameters were used to identify the physical characteristics of the human participants, including: peak interactive force (fmax), transfer time (Ttrf), and work done (W). These variables were subsequently used to design and develop an appropriate set of force and velocity control strategies for a six DOF Stäubli robot manipulator arm (TX60) working in a human-robot interactive environment. The optimal design of the software and hardware controller implementation for the robot system has been successfully established in keeping with a behaviour-based approach. External force control based on proportional plus integral (PI) and fuzzy logic control (FLC) algorithms were adopted to control the robot end effector velocity and interactive force in real-time. ii The results of interactive experiments with human-to-robot and robot-to-human handover tasks allowed a comparison of the PI and FLC control strategies. It can be concluded that the quantitative measurement of the performance of robot velocity and force control can be considered acceptable for human-robot interaction. These can provide effective performance during the robot-human object handover tasks, where the robot was able to successfully pass the object from/to the human in a safe, reliable and timely manner. However, after careful analysis with regard to human-robot handover test results, the FLC scheme was shown to be superior to PI control by actively compensating for the dynamics in the non-linear system and demonstrated better overall performance and stability. The FLC also shows superior performance in terms of improved sensitivity to small error changes compared to PI control, which is an advantage in establishing effective robot force control. The results of survey responses from the participants were in agreement with the parallel test outcomes, demonstrating significant satisfaction with the overall performance of the human-robot interactive system, as measured by an average rating of 4.06 on a five point scale. In brief, this research has contributed the foundations for long-term research, particularly in the development of an interactive real-time robot-force control system, which enables the robot manipulator arm to cooperate with a human to facilitate the dextrous transfer of objects in a safe and speedy manner.Thai government and Prince of Songkla University (PSU