Machine Learning Based Methods for Safety and Control of Human–Robot Interaction

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Robotics, Mechatronics and Intelligent Machines".

Deadline for manuscript submissions: closed (30 June 2023) | Viewed by 10476

Special Issue Editors


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Mechatronics Engineering, Mechanical Engineering Department, Faculty of Engineering, South Valley University, Qena 83523, Egypt
Interests: robotics; mechatronics; control theory; system modeling; artificial intelligence; MATLAB simulation; control and instrumentation

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Robotics Group, Mechanical Engineering and Aeronautics Dept., University of Patras, Patras, Greece
Interests: computational intelligence; fuzzy logic; artificial intelligence; neural networks; mechatronics; soft computing; machine learning; robotics; machines; artificial neural networks

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Department of Mechanical, Energy and Management Engineering, Università della Calabria, 87036 Rende, Italy
Interests: robotics; robot design; mechatronics; walking hexapod; design procedure; mechanics of machinery; leg–wheel
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Institut PPRIME, CNRS, Université de Poitiers, ISAE-ENSMA, UPR 3346 Poitiers, France
Interests: robotics; biomechanical engineering; rehabilitation; biomimicry; mechanical design; service robotics; human–robot collaboration; compliant joint
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Human–robot Interaction (HRI) research concerns the understanding, design, and evaluation of robotic systems for use by or with humans. The introduction of robots into environments shared with humans could improve the efficiency of both human and robot systems. Using robots to assist humans could increase their precision, speed, and force. Moreover, robots could reduce the stress and tiredness of the human operator, improving working conditions. On the other hand, humans contribute to collaboration in terms of experience, knowledge of how to execute a task, intuition, easy learning and adaptation, and easy understanding of control strategies.

When robots and humans share a workspace, safety is a very important factor due to the operator’s proximity to the robot, which could potentially lead to injuries. Therefore, safety must be considered when designing any HRI control system. Conventional and advanced control methods have been introduced during the last few years, with promising results. The combination of safety with sophisticated controllers based on machine learning approaches will boost the entry of HRI systems in manufacturing, as well as everyday robotic applications.

The existing HRI research is extensive. However, some aspects require special investigation and bridging of the segmented presented research works. Concerning safety methods, a point of interest is the effectiveness of current approaches for detecting collisions (magnitude, direction, position, etc.) with the robot. The human operator can have infinitely different cases of collisions with the robot manipulator; effective identification of these collisions is a crucial point in HRI, which should be thoroughly investigated. This could help to expand current research from robotic manufacturing/factory applications to other robotic sectors, which is a necessity for the robotics community. Furthermore, most of the existing approaches depend on joint torques signals and less on other conventional signals (e.g., joint position or current signals). Hence, there are great HRI systems that could be applied only to collaborative robots, which are more expensive, and less to conventional industrial robots. Concerning control methods, developing controllers for manipulators based on soft computing techniques is required to improve the human–robot co-manipulation. These new approaches should avoid large numbers of computations or those that are complex, and should also avoid expert knowledge for intuitive cooperation. Concerning methods combining both safety and control features, there is a gap where cutting-edge research could be applied. When the implementations of this merging are based on AI and machine-learning algorithms, advanced HRI systems can be achieved. For such systems, effectiveness under extensive different conditions and applicability of the methods to different types of robots and applications could be a key factor of the expected research.

Considering the safety and control of HRI, the focus of this Research Topic is on applying machine learning algorithms, such as supervised learning, unsupervised learning, and reinforcement learning, to the following (and other) topics:

  • HRI (physical, social, cognitive).
  • Human–robot collaboration (coexistence, synchronization, cooperation, collaboration, co-manipulation).
  • Safety in HRI.
  • Innovative control approaches.
  • Advanced control methods for HRI and HRC.
  • Admittance/impedance control.
  • Tasks and trajectory planning in HRI.
  • Collision detection and reaction.
  • Systems for improving HRI performance.
  • Measurements in HRI.
  • Rehabilitation applications.
  • Ergonomics in HRI.
  • Human–robot system: productivity, efficiency, and reliability.
  • Innovative robotic architectures.
  • Safety-related issues.
  • Variable stiffness joint.
  • Passive/active compliance.

Dr. Abdel-Nasser Sharkawy
Dr. Panagiotis N. Koustoumpardis
Prof. Dr. George Nikolakopoulos
Prof. Dr. Giuseppe Carbone
Dr. Med Amine Laribi
Guest Editors

Manuscript Submission Information

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Keywords

  • human–robot interaction
  • machine learning
  • safety
  • collision
  • control methods
  • collaboration
  • coexistence
  • cooperative manipulation

Published Papers (2 papers)

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Research

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14 pages, 4312 KiB  
Article
Dynamic Human–Robot Collision Risk Based on Octree Representation
by Nikolaos Anatoliotakis, Giorgos Paraskevopoulos, George Michalakis, Isidoros Michalellis, Evangelia I. Zacharaki, Panagiotis Koustoumpardis and Konstantinos Moustakas
Machines 2023, 11(8), 793; https://doi.org/10.3390/machines11080793 - 1 Aug 2023
Cited by 1 | Viewed by 1076
Abstract
The automation of manufacturing applications where humans and robots operate in a shared environment imposes new challenges for presenting the operator’s safety and robot’s efficiency. Common solutions relying on isolating the robots’ workspace from human access during their operation are not applicable for [...] Read more.
The automation of manufacturing applications where humans and robots operate in a shared environment imposes new challenges for presenting the operator’s safety and robot’s efficiency. Common solutions relying on isolating the robots’ workspace from human access during their operation are not applicable for HRI. This paper presents an extended reality-based method to enhance human cognitive awareness of the potential risk due to dynamic robot behavior towards safe human–robot collaborative manufacturing operations. A dynamic and state-aware occupancy probability map indicating the forthcoming risk of human–robot accidental collision in the 3D workspace of the robot is introduced. It is determined using octrees and is rendered in a virtual or augmented environment using Unity 3D. A combined framework allows the generation of both static zones (taking into consideration the entire configuration space of the robot) and dynamic zones (generated in real time by fetching the occupancy data corresponding to the robot’s current configuration), which can be utilized for short-term collision risk prediction. This method is then applied in a virtual environment of the workspace of an industrial robotic arm, and we also include the necessary technical adjustments for the method to be applied in an AR setting. Full article
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Review

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24 pages, 9858 KiB  
Review
Human–Robot Interaction: A Review and Analysis on Variable Admittance Control, Safety, and Perspectives
by Abdel-Nasser Sharkawy and Panagiotis N. Koustoumpardis
Machines 2022, 10(7), 591; https://doi.org/10.3390/machines10070591 - 20 Jul 2022
Cited by 21 | Viewed by 7806
Abstract
Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first [...] Read more.
Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems. Full article
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