Control of Robots Physically Interacting with Humans and Environment

A special issue of Robotics (ISSN 2218-6581).

Deadline for manuscript submissions: closed (10 September 2022) | Viewed by 11860

Special Issue Editors


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Guest Editor
Dipartimento di Ingegneria, Universitá degli Studi della Campania Luigi Vanvitelli, Aversa, Italy
Interests: Robotic manipulation; force and tactile sensing; physical human–robot interaction

E-Mail Website
Guest Editor
Dipartimento di Ingegneria, Università degli Studi della Campania Luigi Vanvitelli, Via Roma, 29, 81031 Aversa, Italy
Interests: Distributed force/tactile sensing; dexterous manipulation; sensor calibration.

Special Issue Information

Dear Colleagues,

Manipulation and physical interaction with an unstructured environment are key challenges in current robotics. Robots are intelligent machines that are able to physically interact with the environment, objects and humans. When a robot is endowed with suitable perception and control systems, its intelligence is embodied as the robot quickly reacts to external stimuli coming from sensors, e.g., force, proximity and visual data. Manipulation is a very special interaction modality that includes grasping and the in-hand manipulation of objects. If a safe and firm grasp is a difficult task in an unstructured environment, in-hand manipulation is a true challenge.

When robots physically interact with humans by sharing their workspaces, safety is a key aspect. In such a scenario, perception and control need to satisfy additional constraints—performing the task is not enough; therefore, awareness of the human is of paramount importance.

This Special Issue invites papers on modern robot control, e.g., force/tactile control, friction modeling, object detection, obstacle avoidance, machine learning, performance measures, human–robot safe interaction, and grasping of fragile or deformable objects. The Special Issue will cover the scientific and technological advancements in this field, which could improve many social aspects, especially outside strongly structured factories, such as healthcare, domestic robotics, in-store logistics and human–robot collaboration in general.

Topics of interest include (but are not limited to):

  • Deep learning in grasping and manipulation;
  • Mobile manipulation;
  • Object detection, segmentation and categorization;
  • Reactive and sensor-based planning;
  • Human factors and human-in-the-loop;
  • Physical human–robot interaction;
  • Dexterous manipulation;
  • Dual arm manipulation;
  • Grippers and other end effectors;
  • Domestic robots;
  • Force and tactile sensing;
  • Force control;
  • Collision avoidance.

We look forward to receiving your submissions for this Special Issue.

Prof. Dr. Ciro Natale
Dr. Marco Costanzo
Guest Editors

Manuscript Submission Information

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Keywords

  • physical human–robot interaction
  • grasping
  • in-hand manipulation
  • contact modeling
  • perception for grasping and manipulation
  • safety
  • reactive control
  • robot control

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Published Papers (2 papers)

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Research

20 pages, 793 KiB  
Article
Robot Anticipation Learning System for Ball Catching
by Diogo Carneiro, Filipe Silva and Petia Georgieva
Robotics 2021, 10(4), 113; https://doi.org/10.3390/robotics10040113 - 15 Oct 2021
Cited by 5 | Viewed by 5513
Abstract
Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in [...] Read more.
Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase. Full article
(This article belongs to the Special Issue Control of Robots Physically Interacting with Humans and Environment)
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18 pages, 6069 KiB  
Article
Nut Unfastening by Robotic Surface Exploration
by Alireza Rastegarpanah, Rohit Ner, Rustam Stolkin and Naresh Marturi
Robotics 2021, 10(3), 107; https://doi.org/10.3390/robotics10030107 - 14 Sep 2021
Cited by 11 | Viewed by 4851
Abstract
In this paper, we present a novel concept and primary investigations regarding automated unfastening of hexagonal nuts by means of surface exploration with a compliant robot. In contrast to the conventional industrial approaches that rely on custom-designed motorised tools and mechanical tool changers, [...] Read more.
In this paper, we present a novel concept and primary investigations regarding automated unfastening of hexagonal nuts by means of surface exploration with a compliant robot. In contrast to the conventional industrial approaches that rely on custom-designed motorised tools and mechanical tool changers, we propose to use robot fingers to position, grasp and unfasten unknown random-sized hexagonal nuts, which are arbitrarily positioned in the robot’s task space. Inspired by how visually impaired people handle unknown objects, in this work, we use information observed from surface exploration to devise the unfastening strategy. It combines torque monitoring with active compliance for the robot fingers to smoothly explore the object’s surface. We implement a shape estimation technique combining scaled iterative closest point and hypotrochoid approximation to estimate the location as well as contour profile of the hexagonal nut so as to accurately position the gripper fingers. We demonstrate this work in the context of dismantling an electrically driven vehicle battery pack. The experiments are conducted using a seven degrees of freedom (DoF)–compliant robot fitted with a two-finger gripper to unfasten four different sized randomly positioned hexagonal nuts. The obtained results suggest an overall exploration and unfastening success rate of 95% over an average of ten trials for each nut. Full article
(This article belongs to the Special Issue Control of Robots Physically Interacting with Humans and Environment)
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