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Industrial Robotics: Design and Applications
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Industrial Robotics: Design and Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Robotics and Automation".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 17474

Special Issue Editors

Dr. Alessandro Umbrico

E-Mail Website
Guest Editor
National Research Council of Italy, Institute of Cognitive Sciences and Technologies, 00185 Rome, Italy
Interests: automated planning and scheduling; timeline-based planning; robust execution; human–robot collaboration; knowledge representation and reasoning; cognitive architectures
Special Issues, Collections and Topics in MDPI journals
Dr. Marco Faroni

E-Mail Website
Guest Editor
Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA), National Research Council (CNR), via Corti 12, 20133 Milan, Italy
Interests: industrial robotics; human–robot collaboration; motion planning; task planning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Industrial robots play a key role in modern industrial automation where devices such as, e.g., robotic arms and automated guided vehicles (AGV) populate shop floors and (together with human workers) actively participate in the production life cycle. Robotic arms, or collaborative robots (cobots) more generally, for example, are used for assembly, inspection, and many other “common” tasks, with the aim of increasing the throughput of production systems as well as enhancing working conditions of human workers (e.g., alleviate cognitive of physical fatigue, reduce risks). Nevertheless, industrial implementations often do not fully exploit the great advancements made in research fields such as robotics and artificial intelligence (AI). The widespread use of results in AI and their integration with robotics, in particular, can significantly advance industrial robots by designing novel solutions that achieve higher levels of flexibility, dependability, autonomy, etc. These are indeed qualities that are crucial in the Industry 4.0, where production systems are characterized by flexible production needs, which requires industrial robots to work in full autonomy by understanding the production needs, goals, and states of a production environment (perception and cognition), continuously interact with production resources (autonomy and control), and cooperate with other actors, e.g., human workers, machines, and other robots, to carry out various production processes (coordination and collaboration).

This Special Issue invites paper presenting novel methodologies, methods, and software tools pushing the synergetic interaction (and integration) between AI and robotics in industrial scenarios. We particularly welcome papers that investigate the use of AI to enhance the “cognitive capabilities” of industrial robots to achieve increasingly flexible and “intelligent” behaviors. Examples are: (i) the use of AI to allow industrial robots to dynamically adapt their skills to the different and evolving production needs of a shop floor; (ii) the use of AI to enhance perception capabilities and allow industrial robots to track human operators, recognize human tasks, and dynamically configure and/or adapt robot behaviors to the inferred “intentions” of a worker; (iii) the use of AI to realize innovative control approaches that increase the dependability and flexibility of industrial robots and allow them to effectively (and efficiently) work in production scenarios affected by uncertainty, such as in human–robot collaboration.

Topics of interest (but not limited to):

- Applied Artificial Intelligence
- Long-term Autonomy of Industrial Robots in Dynamic Environments 
- Human-aware Motion planning and Control
- Combined Task and Motion Planning
- Multi-robot and/or Multi-agent Coordination 
- novel Sensing and Grasping Technologies for HRI
- Human-centered Design of Industrial robots and Robotized Workcells
- Cybersecurity and Safety Issues of Industrial Robots for HRI 
- Environment Cognition for Advanced Control 
- Human-factor and Experiments
 

Dr. Alessandro Umbrico
Dr. Marco Faroni
Guest Editors

Manuscript Submission Information

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Published Papers (6 papers)

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26 pages, 1211 KiB  
Article
Series PIDA Controller Design for IPDT Processes
by Mikulas Huba, Pavol Bistak and Damir Vrancic
Appl. Sci. 2023, 13(4), 2040; https://doi.org/10.3390/app13042040 - 4 Feb 2023
Cited by 10 | Viewed by 1796
Abstract
This paper discusses optimal design of the series proportional–integral–derivative–accelerative (PIDA) controller for integral-plus-dead-time (IPDT) plants. The article starts with the design of disturbance reconstruction and compensation based on proportional-derivative-accelerative (PDA) stabilizing controllers. It shows that by introducing positive feedback by a low-pass filter [...] Read more.
This paper discusses optimal design of the series proportional–integral–derivative–accelerative (PIDA) controller for integral-plus-dead-time (IPDT) plants. The article starts with the design of disturbance reconstruction and compensation based on proportional-derivative-accelerative (PDA) stabilizing controllers. It shows that by introducing positive feedback by a low-pass filter from the (limited) output of the stabilizing PDA controller, one gets disturbance observer (DOB) for the reconstruction and compensation of input disturbances. Thereby, the DOB functionality is based on evaluating steady-state controller output. This DOB interpretation is in full agreement with the results of the analysis of the optimal setting of the stabilizing PDA controller and of its expanded PIDA version with positive feedback from the controller output. By using the multiple real dominant pole (MRDP) method, it confirms that the low-pass filter time constant in positive feedback must be much longer than the dominant time constant of the stabilized loop. This paper also shows that the constrained PIDA controller with the MRDP setting leads to transient responses with input and output overshoots. Experimentally, such a constrained series PIDA controller can be shown as equivalent to a constrained MRDP tuned parallel PIDA controller in anti-windup connection using conditional integration. Next, the article explores the possibility of removing overshoots of the output and input of the process achieved for MRDP tuning by interchanging the parameters of the controller transfer function, which was proven as very effective in the case of the series PID controller. It shows that such a modification of the controller can only be implemented approximately, when the factorization of the controller numerator, which gives complex conjugate zeros, will be replaced by a double real zero. Neglecting the imaginary part and specifying the feedback time constant with a smaller approximative time constant results in the removal of overshoots, but the resulting dynamics will not be faster than for the previously mentioned solutions. A significant improvement in the closed-loop performance can finally be achieved by the optimal setting of the constrained series PIDA controller calculated using the performance portrait method. This article also points out the terminologically incorrect designation of the proposed structure as series PIDA controller, because it does not contain any explicit integral action. Instead, it proposes a more thorough revision of the interpretation of controllers based on automatic reset from the controller output, which do not contain any integrator, but at the same time represent the core of the most used industrial automation. In the end, constrained structures using automatic reset of the stabilizing controller output can ensure a higher performance of transient responses than the usually preferred solutions based on parallel controllers with integral action that, in order to respect the control signal limitation, must be supplemented with anti-windup circuitry. The excellent properties of the constrained series PIDA controller are demonstrated by an example of controlling a thermal process and proven by the circle criterion of absolute stability. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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18 pages, 2423 KiB  
Article
PickingDK: A Framework for Industrial Bin-Picking Applications
by Marco Ojer, Xiao Lin, Antonio Tammaro and Jairo R. Sanchez
Appl. Sci. 2022, 12(18), 9200; https://doi.org/10.3390/app12189200 - 14 Sep 2022
Cited by 3 | Viewed by 2009
Abstract
This work presents an industrial bin-picking framework for robotics called PickingDK. The proposed framework employs a plugin based architecture, which allows it to integrate different types of sensors, robots, tools, and available open-source software and state-of-the-art methods. It standardizes the bin-picking process with [...] Read more.
This work presents an industrial bin-picking framework for robotics called PickingDK. The proposed framework employs a plugin based architecture, which allows it to integrate different types of sensors, robots, tools, and available open-source software and state-of-the-art methods. It standardizes the bin-picking process with a unified workflow based on generally defined plugin interfaces, which promises the hybridization of functional/virtual plugins for fast prototyping and proof-of-concept. It also offers different levels of controls according to the user’s expertise. The presented use cases demonstrate flexibility when building bin-picking applications under PickingDK framework and the convenience of exploiting hybrid style prototypes for evaluating specific steps in a bin-picking system, such as parameter fine-tuning and picking cell design. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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22 pages, 9156 KiB  
Article
Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations
by Alessandro Umbrico, Andrea Orlandini, Amedeo Cesta, Marco Faroni, Manuel Beschi, Nicola Pedrocchi, Andrea Scala, Piervincenzo Tavormina, Spyros Koukas, Andreas Zalonis, Nikos Fourtakas, Panagiotis Stylianos Kotsaris, Dionisis Andronas and Sotiris Makris
Appl. Sci. 2022, 12(14), 6839; https://doi.org/10.3390/app12146839 - 6 Jul 2022
Cited by 12 | Viewed by 2933
Abstract
Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The [...] Read more.
Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The coexistence of human and autonomous robotic agents raises several methodological and technological challenges for the design of effective, safe, and reliable control paradigms. This work proposes the integration of novel technologies from Artificial Intelligence, Control and Augmented Reality to enhance the flexibility and adaptability of collaborative systems. We present the basis to advance the classical human-aware control paradigm in favor of a user-aware control paradigm and thus personalize and adapt the synthesis and execution of collaborative processes following a user-centric approach. We leverage a manufacturing case study to show a possible deployment of the proposed framework in a real-world industrial scenario. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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15 pages, 834 KiB  
Article
Robot-Agnostic Interaction Controllers Based on ROS
by Federica Storiale, Enrico Ferrentino and Pasquale Chiacchio
Appl. Sci. 2022, 12(8), 3949; https://doi.org/10.3390/app12083949 - 13 Apr 2022
Cited by 1 | Viewed by 2848
Abstract
In robotized industrial scenarios, the need for efficiency and flexibility is increasing, especially when tasks must be executed in dangerous environments and/or require the simultaneous manipulation of dangerous/fragile objects by multiple heterogeneous robots. However, the underlying hardware and software architecture is typically characterized [...] Read more.
In robotized industrial scenarios, the need for efficiency and flexibility is increasing, especially when tasks must be executed in dangerous environments and/or require the simultaneous manipulation of dangerous/fragile objects by multiple heterogeneous robots. However, the underlying hardware and software architecture is typically characterized by constraints imposed by the robots’ manufacturers, which complicates their integration and deployment. This work aims to demonstrate that widely used algorithms for robotics, such as interaction control, can be made independent of the hardware architecture, abstraction level, and functionality provided by the low-level proprietary controllers. As a consequence, a robot-independent control framework can be devised, which reduces the time and effort needed to configure the robotic system and adapt it to changing requirements. Robot-agnostic interaction controllers are implemented on top of the Robot Operating System (ROS) and made freely available to the robotic community. Experiments were performed on the Universal Robots UR10 research robot, the Comau Smart-Six industrial robot, and their digital twins, so as to demonstrate that the proposed control algorithms can be easily deployed on different hardware and simulators without reprogramming. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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19 pages, 5383 KiB  
Article
Engineering Method and Tool for the Complete Virtual Commissioning of Robotic Cells
by Roberto Raffaeli, Pietro Bilancia, Federico Neri, Margherita Peruzzini and Marcello Pellicciari
Appl. Sci. 2022, 12(6), 3164; https://doi.org/10.3390/app12063164 - 20 Mar 2022
Cited by 8 | Viewed by 3065
Abstract
Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a [...] Read more.
Intelligent robotic manufacturing cells must adapt to ever-varying operating conditions, developing autonomously optimal manufacturing strategies to achieve the best quality and overall productivity. Intelligent and cognitive behaviors are realized by using distributed controllers, in which complex control logics must interact and process a wide variety of input/output signals. In particular, programmable logic controllers (PLCs) and robot controllers must be coordinated and integrated. Then, there is the need to simulate the robotic cells’ behavior for performance verification and optimization by evaluating the effects of both PLC and robot control codes. In this context, this work proposes a method, and its implementation into an integrated tool, to exploit the potential of ABB RobotStudio software as a virtual prototyping platform for robotic cells, in which real robots control codes are executed on a virtual controller and integrated with Beckhoff PLC environment. For this purpose, a PLC Smart Component was conceived as an extension of RobotStudio functionalities to exchange signals with a TwinCAT instance. The new module allows the virtual commissioning of a complete robotic cell to be performed, assessing the control logics effects on the overall productivity. The solution is demonstrated on a robotic assembly cell, showing its feasibility and effectiveness in optimizing the final performance. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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26 pages, 2396 KiB  
Article
Modelling Automated Planning Problems for Teams of Mobile Manipulators in a Generic Industrial Scenario
by Stefan-Octavian Bezrucav and Burkhard Corves
Appl. Sci. 2022, 12(5), 2319; https://doi.org/10.3390/app12052319 - 23 Feb 2022
Cited by 7 | Viewed by 2020
Abstract
Flexible control strategies are required in industrial scenarios to coordinate the actions of mobile manipulators (e.g., robots and humans). Temporal planning approaches can be used as such control strategies because they can generate those actions for the agents that must be executed to [...] Read more.
Flexible control strategies are required in industrial scenarios to coordinate the actions of mobile manipulators (e.g., robots and humans). Temporal planning approaches can be used as such control strategies because they can generate those actions for the agents that must be executed to reach the goals, from any given state of the world. To deploy such approaches, planning models must be formulated. Although available in the literature, these models are not generic enough such that they can be easily transferred to new use cases. In this work, a generic industrial scenario is derived from real scenarios. For this scenario, a generic planning problem is developed. To demonstrate their generality, the two constructs are configured for a new scenario, where custom grippers are assembled. Lastly, a validation methodology is developed for the generic planning problem. The results show that the generic industrial scenario and the generic planning problem can be easily instantiated for new use cases, without any new modelling. Further, the proposed validation methodology guarantees that these planning problems are complete enough to be used in industrial use cases. The generic scenario, the planning problems, and the validation methodology are proposed as standards for use when deploying temporal planning in industrial scenarios with mobile manipulators. Full article
(This article belongs to the Special Issue Industrial Robotics: Design and Applications)
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