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Machines
Special Issues
Collaborative Robotics and Adaptive Machines
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Collaborative Robotics and Adaptive Machines

A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 15296

Special Issue Editor

Dr. S. M. Mizanoor Rahman

E-Mail Website
Guest Editor
Mechanical Engineering, The Pennsylvania State University, Dunmore, PA 18512, USA
Interests: assistive robotics; collaborative robotics; artificial intelligence; machine learning; control system; human factors; mechanical design; human-robot interaction
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Robots in traditional industrial applications are usually separated from humans. This is done partly for safety for humans and partly due to the perception that the capabilities of robots and the abilities of humans are largely mutually exclusive. However, this perception has significantly changed in recent years with the prevailing view that the capabilities of robots and the abilities of humans are largely complementary if exploited properly. This is the notion behind ‘collaborative robotics’ where robots and human co-workers share the same workspace and perform together on the same tasks with the same goals augmenting their mutual strengths and counteracting their inherent limitations. While collaboration between robots and humans is the central theme of collaborative robotics, collaboration between robots or other artificial agents is also promising. Currently, there is no doubt about the importance of collaborations in robotics. However, the enhancement of effectiveness, safety, and performance of collaborative robots is still a demanding issue. Especially, the collaborators require perceiving the situations and adjusting with it to smoothly accomplish the collaborative tasks. It is assumed that different approaches to make the collaborations more adaptive to prevailing situations and uncertainties should receive priority in research to make the collaborations more effective. Thus, the objective of this Special Issue is to accumulate various innovative strategies that make the collaborative robots and other similar machines more effective, safe, efficient, and adaptive with respect to prevailing task situations and uncertainties.

The Special Issue will cover topics in the context of recent advances and future trends in collaborative robotics and adaptive machines including, but are not limited to the following:

  • Collaboration modeling and collaboration dynamics
  • Intelligent control strategies especially adaptive and predictive controls
  • Artificial intelligence, machine learning, and vision-based algorithms
  • Subtasks allocation and scheduling optimization
  • Autonomy, team fluency, mixed initiatives, and mutual trust
  • Bio-inspiration in human-robot or robot-robot collaboration
  • Transparency, engagement, communication, safety, and efficiency
  • Mechanisms and interface design, and passive compliance
  • Security in collaborative robotics
  • Task and motion planning in constrained spaces
  • Collaboration or symbiosis in the forms of cyber-physical system (CPS) and internet of robotic things (IoRT)
  • Ambient, embedded and cloud-based intelligence, and adaptive robotic ecology
  • Collaborations in/with heterogeneous realities (e.g., real robot–virtual human collaboration)
  • Performance assessment methods and metrics and benchmarking
  • Education in/with collaborative robotics

Dr. S. M. Mizanoor Rahman
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human–robot/machine collaboration
  • robot–robot collaboration
  • human-machine interface
  • artificial intelligence
  • machine learning
  • machine vision
  • intelligent controls
  • collaboration modeling
  • performance evaluation
  • task allocation
  • mixed initiatives
  • trust
  • scheduling optimization
  • team fluency
  • task and motion planning
  • cyber-physical system (CPS)
  • internet of robotic things (IoRT)

Published Papers (4 papers)

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Research

19 pages, 9063 KiB  
Article
Synergistic Motion Stability of a Scorpion-like Composite Robot
by Qiang Gao, Jiaolong Xue and Hongwei Yan
Machines 2022, 10(10), 834; https://doi.org/10.3390/machines10100834 - 21 Sep 2022
Cited by 1 | Viewed by 1395
Abstract
In this paper, a compliant control scheme based on the optimization of the contact force of the robot leg is proposed to improve the stability of the whole moving process of the robot. Firstly, according to the motion state of the robot, the [...] Read more.
In this paper, a compliant control scheme based on the optimization of the contact force of the robot leg is proposed to improve the stability of the whole moving process of the robot. Firstly, according to the motion state of the robot, the change of its center of gravity is analyzed, then the stable gait of the robot is determined by the stability margin, and the smooth control of the robot’s foot trajectory is realized. Finally, the compliant control model of the robot leg is established. In the process of moving, the contact force between the legs and the ground is optimized in real-time, so that the composite robot can walk steadily on uneven terrain. The 3-D model of the scorpion composite robot was built with ADAMS software, and dynamics simulation was carried out according to the compliant control scheme. This paper takes the robot’s walking speed and torso angle as performance evaluation indexes and verifies the effectiveness of the compliant control scheme. The cooperative motion stability test is carried out on the actual uneven terrain. The test results show that the robot’s pitch angle and roll angle are between ±0.5°, which meets the motion stability requirements of the robot and verifies the correctness of the compliant control scheme and control model proposed in this paper. Full article
(This article belongs to the Special Issue Collaborative Robotics and Adaptive Machines)
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20 pages, 5187 KiB  
Communication
Autonomous Mobile Robot Implemented in LEGO EV3 Integrated with Raspberry Pi to Use Android-Based Vision Control Algorithms for Human-Machine Interaction
by Hernando León Araujo, Jesús Gulfo Agudelo, Richard Crawford Vidal, Jorge Ardila Uribe, John Freddy Remolina, Claudia Serpa-Imbett, Ana Milena López and Diego Patiño Guevara
Machines 2022, 10(3), 193; https://doi.org/10.3390/machines10030193 - 07 Mar 2022
Cited by 4 | Viewed by 4090
Abstract
Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. [...] Read more.
Robotic applications, such as educational programs, are well-known. Nonetheless, there are challenges to be implemented in other settings, e.g., mine detection, agriculture support, and tasks for industry 4.0. The main challenge consists of robotic operations supported by autonomous decision using sensed-based features extraction. A prototype of a robot assembled using mechanical parts of a LEGO MINDSTORMS Robotic Kit EV3 and a Raspberry Pi controlled through servo algorithms of 2D and 2D1/2 vision approaches was implemented to tackle this challenge. This design is supported by simulations based on image, position, and a hybrid scheme for visual servo controllers. Practical implementation is operated using navigation guided by running up image-based visual servo control algorithms embedded in a Raspberry Pi that uses a control criterion based on error evolution to compute the difference between a target and sensed image. Images are collected by a camera installed on a mobile robotic platform manually and automatically operated and controlled using the Raspberry Pi. An Android application to watch the images by video streaming is shown here, using a smartphone and a video related to the implemented robot’s operation. This kind of robot might be used to complete field reactive tasks in the settings mentioned above, since the detection and control approaches allow self-contained guidance. Full article
(This article belongs to the Special Issue Collaborative Robotics and Adaptive Machines)
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13 pages, 2581 KiB  
Article
Adaptive Obstacle Avoidance for a Class of Collaborative Robots
by Giorgia Chiriatti, Giacomo Palmieri, Cecilia Scoccia, Matteo Claudio Palpacelli and Massimo Callegari
Machines 2021, 9(6), 113; https://doi.org/10.3390/machines9060113 - 03 Jun 2021
Cited by 17 | Viewed by 4578
Abstract
In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or [...] Read more.
In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system. Full article
(This article belongs to the Special Issue Collaborative Robotics and Adaptive Machines)
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21 pages, 3323 KiB  
Article
Machine Learning-Based Cognitive Position and Force Controls for Power-Assisted Human–Robot Collaborative Manipulation
by S. M. Mizanoor Rahman
Machines 2021, 9(2), 28; https://doi.org/10.3390/machines9020028 - 03 Feb 2021
Cited by 13 | Viewed by 3889
Abstract
Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human–robot systems, such as [...] Read more.
Manipulation of heavy objects in industries is very necessary, but manual manipulation is tedious, adversely affects a worker’s health and safety, and reduces efficiency. On the contrary, autonomous robots are not flexible to manipulate heavy objects. Hence, we proposed human–robot systems, such as power assist systems, to manipulate heavy objects in industries. Again, the selection of appropriate control methods as well as inclusion of human factors in the controls is important to make the systems human friendly. However, existing power assist systems do not address these issues properly. Hence, we present a 1-DoF (degree of freedom) testbed power assist robotic system for lifting different objects. We also included a human factor, such as weight perception (a cognitive cue), in the robotic system dynamics and derived several position and force control strategies/methods for the system based on the human-centric dynamics. We developed a reinforcement learning method to predict the control parameters producing the best/optimal control performance. We also derived a novel adaptive control algorithm based on human characteristics. We experimentally evaluated those control methods and compared the system performance between the control methods. Results showed that both position and force controls produced satisfactory performance, but the position control produced significantly better performance than the force controls. We then proposed using the results to design control methods for power assist robotic systems for handling large and heavy materials and objects in various industries, which may improve human–robot interactions (HRIs) and system performance. Full article
(This article belongs to the Special Issue Collaborative Robotics and Adaptive Machines)
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