Special Issue "Human-Robot Physical Interaction"

A special issue of Robotics (ISSN 2218-6581). This special issue belongs to the section "Industrial Robots and Automation".

Deadline for manuscript submissions: closed (10 April 2022) | Viewed by 2744

Special Issue Editors

Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Scuola Universitaria Professionale della Svizzera Italiana (SUPSI), Università della Svizzera Italiana (USI) IDSIA-SUPSI, Manno, Switzerland
Interests: industrial robots; collaborative robots; control theory; wearable robotics; interaction control; human-robot collaboration; AI; ML
Special Issues, Collections and Topics in MDPI journals
Department of Mechanical Engineering, Politecnico di Milano, 20133 Milano, Italy
Interests: mobile robots; motion control, road traffic control; global positioning system; driver information systems; electric motors, energy management systems; frequency response; fuel economy; hybrid electric vehicles
Special Issues, Collections and Topics in MDPI journals
Laboratory for Manufacturing Systems and Automation (LMS), Department of Mechanical Engineering and Aeronautics, University of Patras, 26504 Rio Patras, Greece
Interests: smart intralogistics, robotics; virtual reality; augmented reality; virtual collaborative environments; AI
Special Issues, Collections and Topics in MDPI journals
Faculty of Mechanical Engineering, Cyber Manufacturing Systems Laboratory (CMSysLab), University of Belgrade, Beograd, Serbia
Interests: collaborative robotics; human-robot cognitive and physical interaction; tactile and force feedback and sensory systems; VR/AR and Digital Twin technology; embedded systems and real-time control; fuzzy systems and approximative reasoning; robotic assembly systems design (including robotic welding)

Special Issue Information

Dear Colleagues,

Industrial robotics is facing an epochal change, requiring to move from standard, fixed and pre-programmed solutions to advanced, re-configurable, flexible and intelligent systems. In fact, industrial environments are moving to dynamic production plants, requiring robots to deal with daily-changing processes and highly customizable goods. The role of industrial robots is dual: taking charge of heavy, repetitive and non-ergonomic tasks (autonomously or actively assisting the operator), and at the same time being able to learn new behaviors and applications. In such a way, it will be possible to efficiently assist operators while learning their behaviors, to be reproduced in an autonomous manner. This ambitious scenario, therefore, is strongly interdisciplinary, requiring expertise from different areas, such as control theory, machine learning, artificial intelligence, safety and regulation, mechanical and electrical design, etc. Different applications can be thought of: wearable robotics, collaborative robotics, mobile robotics, sensing and actuation solutions, etc. The aim of this special issue is to identify high quality papers dealing with such arising challenges and direction of industrial robotics, taking into account human-centered solutions capable to actively assist operators while learning their behaviors.
Contributions related (but not limited) to the following topics are welcome:

  • human-robot physical interaction;
  • collaborative controllers;
  • intelligent control;
  • task learning;
  • safety-based control algorithms;
  • collaborative robots and exoskeleton design and control;
  • AI and ML applied to robot control and learning;
  • AR/VR for industrial plants;
  • human’s knowledge transfer to robotic platforms;
  • intelligent motion planning;
  • optimization of industrial robotic solutions;
  • advanced safety;
  • natural human-robot interaction;
  • human’s intention recognition;
  • mobile collaborative robotics.

Dr. Roveda Loris
Dr. Braghin Francesco
Dr. Sotiris Makris
Dr. Petar B. Petrovic
Guest Editors

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. Robotics is an international peer-reviewed open access semimonthly 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 1600 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.

Published Papers (1 paper)

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14 pages, 3957 KiB  
Towards Fully Autonomous Negative Obstacle Traversal via Imitation Learning Based Control
Robotics 2022, 11(4), 67; https://doi.org/10.3390/robotics11040067 - 22 Jun 2022
Cited by 3 | Viewed by 1869
Current research in experimental robotics has had a focus on traditional, cost-based, navigation methods. These methods ascribe a value of utility for occupying certain locations in the environment. A path planning algorithm then uses this cost function to compute an optimal path relative [...] Read more.
Current research in experimental robotics has had a focus on traditional, cost-based, navigation methods. These methods ascribe a value of utility for occupying certain locations in the environment. A path planning algorithm then uses this cost function to compute an optimal path relative to obstacle positions based on proximity, visibility, and work efficiency. However, tuning this function to induce more complex navigation behaviors in the robot is not straightforward. For example, this cost-based scheme tends to be pessimistic when assigning traversal cost to negative obstacles. Its often simpler to ascribe high traversal costs to costmap cells based on elevation. This forces the planning algorithm to plan around uneven terrain rather than exploring techniques that understand if and how to safely traverse through them. In this paper, imitation learning is applied to the task of negative obstacle traversal with Unmanned Ground Vehicles (UGVs). Specifically, this work introduces a novel point cloud-based state representation of the local terrain shape and employs imitation learning to train a reactive motion controller for negative obstacle detection and traversal. This method is compared to a classical motion planner that uses the dynamic window approach (DWA) to assign traversal cost based on the terrain slope local to the robots current pose. Full article
(This article belongs to the Special Issue Human-Robot Physical Interaction)
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