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Electronics
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Advances in Robotic Mobile Manipulation
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Advances in Robotic Mobile Manipulation

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (1 May 2021) | Viewed by 12668

Special Issue Editors

Prof. Dr. Pablo Gil

E-Mail Website
Guest Editor
Computer Science Research Institute/Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain
Interests: robotic manipulation; robotic grasping; tactile/visual perception; robot vision; object recognition
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Francisco Andrés Candelas

E-Mail Website
Guest Editor
Computer Science Research Institute/Dept. of Physics, Systems Engineering and Signal Theory, University of Alicante, 03690 Alicante, Spain
Interests: mobile robots; intelligent robotics; environment modeling; field robotics; outdoor navigation

Special Issue Information

Humans grasp and manipulate objects of many shapes, sizes, and weights, made of a great variety of materials. It is desirable that robots increase the degree of flexibility to successfully carry out these tasks. The objects can be located in environments with high variability or even unknown, where the scene and the objects located within it can change their pose and location. Therefore, mobile manipulators with high-speed visual sensing are required.

In contrast to a fixed-base robotic manipulator, an autonomous mobile manipulator overcomes the limitation of a constrained workspace. Combining a robotic manipulator with an autonomous vehicle allows combining the grasping skill of the first with the mobility capacity of the second, though increasing degrees of freedom greatly complicates planning and control tasks, especially if both structures work together. Besides, multiple sensors are required to obtain a precise location, such as GNSS receivers, LIDAR, cameras, or IMUs. All these sensing technologies help the robot in the location of objects, in its navigation to reach them, as well as the position control and the planning of manipulation tasks to grasp them. Mobile manipulation still has great challenges, for example in unknown, poorly structured, or dynamic environments in which high flexibility is required to carry out manipulation tasks. This is applicable not only to land vehicles with robotic arms, but also for both underwater and aerial manipulators. In all these cases, apart from the aforementioned sensors, the use of tactile and/or force sensors is recommended, though not necessary. Tactile sensing provides robotic manipulator with new capabilities in order to enable a more robust and precise grasp control, recognize the object in hand and manipulate it properly.

This Special Issue aims to cover advances in robotic mobile manipulation. Novel theoretical approaches or practical applications of all aspects that involve mobile manipulation are welcomed. Reviews, datasets tested in real applications, benchmarks, and surveys of the state-of-the-art methods are also welcomed. Topics of interest to this Special Issue include, but are not limited to, the following topics:

-Location of objectives for grasping.

-Road and trajectory planning to reach the handling areas.

-Sensing and planning in object manipulation.

-Piloting, orientation, and stabilization of the autonomous vehicle during handling.

-Grasping stability assessment using tactile perception, visual perception, or both.

-Control strategies for object manipulation.

-Manipulation of object in-hand with multi-fingered hands.

-Planning of tasks for grasping, transport, and placement of objects.

-Learning of grasping, manipulation, and navigation skills.

Prof. Dr. Pablo Gil
Prof. Dr. Francisco Andrés Candelas
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. Electronics 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 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.

Published Papers (4 papers)

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Research

18 pages, 5993 KiB  
Article
Characterization and Study of the Primitive Dynamic Movements Required to Bi-Manipulate a Box
by Juan Hernandez-Vicen, Santiago Martinez, Raul de Santos-Rico, Elisabeth Menendez and Carlos Balaguer
Electronics 2021, 10(11), 1354; https://doi.org/10.3390/electronics10111354 - 06 Jun 2021
Viewed by 1708
Abstract
Automating the action of finding the opening side of a box is not possible if the robot is not capable of reaching and evaluating all of its sides. To achieve this goal, in this paper, three different movement strategies to bi-manipulate a box [...] Read more.
Automating the action of finding the opening side of a box is not possible if the robot is not capable of reaching and evaluating all of its sides. To achieve this goal, in this paper, three different movement strategies to bi-manipulate a box are studied: overturning, lifting, and spinning it over a surface. First of all, the dynamics involved in each of the three movement strategies are studied using physics equations. Then, a set of experiments are conducted to determine if the real response of the humanoid robot, TEO, to a box is consistent with the expected answer based on theoretical calculus. After the dynamics validation, the information on the forces and the position in the end effectors is used to characterize these movements and create its primitives. These primitive movements will be used in the future to design a hybrid position–force control in order to adapt the movements to different kinds of boxes. The structure of this control is also presented in this paper. Full article
(This article belongs to the Special Issue Advances in Robotic Mobile Manipulation)
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20 pages, 18018 KiB  
Article
A Real Application of an Autonomous Industrial Mobile Manipulator within Industrial Context
by Jose Luis Outón, Ibon Merino, Iván Villaverde, Aitor Ibarguren, Héctor Herrero, Paul Daelman and Basilio Sierra
Electronics 2021, 10(11), 1276; https://doi.org/10.3390/electronics10111276 - 27 May 2021
Cited by 6 | Viewed by 3785
Abstract
In modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative [...] Read more.
In modern industry there are still a large number of low added-value processes that can be automated or semi-automated with safe cooperation between robot and human operators. The European SHERLOCK project aims to integrate an autonomous industrial mobile manipulator (AIMM) to perform cooperative tasks between a robot and a human. To be able to do this, AIMMs need to have a variety of advanced cognitive skills like autonomous navigation, smart perception and task management. In this paper, we report the project’s tackle in a paradigmatic industrial application combining accurate autonomous navigation with deep learning-based 3D perception for pose estimation to locate and manipulate different industrial objects in an unstructured environment. The proposed method presents a combination of different technologies fused in an AIMM that achieve the proposed objective with a success rate of 83.33% in tests carried out in a real environment. Full article
(This article belongs to the Special Issue Advances in Robotic Mobile Manipulation)
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20 pages, 1313 KiB  
Article
The Control Method of Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism to Multi-DOF Manipulator
by Yangyang Hou, Huajie Hong, Zhaomei Sun, Dasheng Xu and Zhe Zeng
Electronics 2021, 10(7), 870; https://doi.org/10.3390/electronics10070870 - 06 Apr 2021
Cited by 9 | Viewed by 2625
Abstract
As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kinematic model. [...] Read more.
As a research hotspot in the field of artificial intelligence, the application of deep reinforcement learning to the learning of the motion ability of a manipulator can help to improve the learning of the motion ability of a manipulator without a kinematic model. To suppress the overestimation bias of values in Deep Deterministic Policy Gradient (DDPG) networks, the Twin Delayed Deep Deterministic Policy Gradient (TD3) was proposed. This paper further suppresses the overestimation bias of values for multi-degree of freedom (DOF) manipulator learning based on deep reinforcement learning. Twin Delayed Deep Deterministic Policy Gradient with Rebirth Mechanism (RTD3) was proposed. The experimental results show that RTD3 applied to multi degree freedom manipulators is in place, with an improved learning ability by 29.15% on the basis of TD3. In this paper, a step-by-step reward function is proposed specifically for the learning and innovation of the multi degree of freedom manipulator’s motion ability. The view of continuous decision-making and process problem is used to guide the learning of the manipulator, and the learning efficiency is improved by optimizing the playback of experience. In order to measure the point-to-point position motion ability of a manipulator, a new evaluation index based on the characteristics of the continuous decision process problem, energy efficiency distance, is presented in this paper, which can evaluate the learning quality of the manipulator motion ability by a more comprehensive and fair evaluation algorithm. Full article
(This article belongs to the Special Issue Advances in Robotic Mobile Manipulation)
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18 pages, 2516 KiB  
Article
Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model
by Izzat Al-Darraji, Dimitrios Piromalis, Ayad A. Kakei, Fazal Qudus Khan, Milos Stojmenovic, Georgios Tsaramirsis and Panagiotis G. Papageorgas
Electronics 2021, 10(7), 831; https://doi.org/10.3390/electronics10070831 - 31 Mar 2021
Cited by 31 | Viewed by 3293
Abstract
Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can [...] Read more.
Aerial Robot Arms (ARAs) enable aerial drones to interact and influence objects in various environments. Traditional ARA controllers need the availability of a high-precision model to avoid high control chattering. Furthermore, in practical applications of aerial object manipulation, the payloads that ARAs can handle vary, depending on the nature of the task. The high uncertainties due to modeling errors and an unknown payload are inversely proportional to the stability of ARAs. To address the issue of stability, a new adaptive robust controller, based on the Radial Basis Function (RBF) neural network, is proposed. A three-tier approach is also followed. Firstly, a detailed new model for the ARA is derived using the Lagrange–d’Alembert principle. Secondly, an adaptive robust controller, based on a sliding mode, is designed to manipulate the problem of uncertainties, including modeling errors. Last, a higher stability controller, based on the RBF neural network, is implemented with the adaptive robust controller to stabilize the ARAs, avoiding modeling errors and unknown payload issues. The novelty of the proposed design is that it takes into account high nonlinearities, coupling control loops, high modeling errors, and disturbances due to payloads and environmental conditions. The model was evaluated by the simulation of a case study that includes the two proposed controllers and ARA trajectory tracking. The simulation results show the validation and notability of the presented control algorithm. Full article
(This article belongs to the Special Issue Advances in Robotic Mobile Manipulation)
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