Journal Description
Robotics
Robotics
is an international, peer-reviewed, open access journal on robotics published monthly online by MDPI. The IFToMM is affiliated with Robotics and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, ESCI (Web of Science), dblp, Inspec, and other databases.
- Journal Rank: JCR - Q2 (Robotics) / CiteScore - Q1 (Mechanical Engineering)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.7 days after submission; acceptance to publication is undertaken in 2.9 days (median values for papers published in this journal in the first half of 2024).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
2.9 (2023);
5-Year Impact Factor:
3.1 (2023)
Latest Articles
Task-Dependent Comfort Zone, a Base Placement Strategy for Mobile Manipulators Based on Manipulability Measures
Robotics 2024, 13(8), 122; https://doi.org/10.3390/robotics13080122 - 16 Aug 2024
Abstract
The present contribution introduces the task-dependent comfort zone as a base placement strategy for mobile manipulators using different manipulability measures. Four different manipulability measures depending on end-effector velocities, forces, stiffness, and accelerations are considered. By evaluating a discrete subspace of the manipulator workspace
[...] Read more.
The present contribution introduces the task-dependent comfort zone as a base placement strategy for mobile manipulators using different manipulability measures. Four different manipulability measures depending on end-effector velocities, forces, stiffness, and accelerations are considered. By evaluating a discrete subspace of the manipulator workspace with these manipulability measures and using image-processing algorithms, a suitable goal position for the autonomous mobile manipulator was defined within the comfort zone. This always ensures a certain manipulator manipulablity value with a lower limit with respect to the maximum possible manipulability in the discrete subspace. Results are shown for three different mobile manipulators using the velocity-dependent manipulability measure in a simulation.
Full article
(This article belongs to the Topic Advances in Mobile Robotics Navigation, 2nd Volume)
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Open AccessArticle
Hybrid Sensor Fusion Mixed with Reinforcement Learning in Autonomous Dual-Arm Lifting Tasks Performed by Humanoid Robots
by
Juan Hernandez-Vicen, Bartek Łukawski, Santiago Martinez, Nikos Tsagarakis and Carlos Balaguer
Robotics 2024, 13(8), 121; https://doi.org/10.3390/robotics13080121 - 11 Aug 2024
Abstract
Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion
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Humanoid robots often struggle with tasks such as lifting objects due to the complexity involved in identifying contact points, applying the correct force, and tracking task progress. We propose an integrated solution that leverages the dual-arm capability of humanoids and utilizes sensor fusion from vision and force sensors. Our system employs a computer vision algorithm to detect and characterize object properties (shape, size, position, orientation) and differentiate between parallel and non-parallel bi-manipulation tasks. The controller then identifies optimal contact points for the end effectors, generating trajectories fed into a closed-loop controller using force feedback. For parallel bi-manipulation, momentum cancellation is achieved through sensor fusion. For non-parallel surfaces, a reinforcement learning algorithm determines the appropriate lifting force to prevent slippage using only two contact points. Experimental validation on a real humanoid platform demonstrates the effectiveness of our approach in autonomously lifting objects, regardless of contact surface configuration. This advancement significantly enhances the reliability and versatility of humanoid robots in performing complex manipulation tasks, contributing to their practical deployment in human-oriented environments.
Full article
(This article belongs to the Section Humanoid and Human Robotics)
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Open AccessArticle
Design and Evaluation of a Novel Passive Shoulder Exoskeleton Based on a Variable Stiffness Mechanism Torque Generator for Industrial Applications
by
Yu Zhu, Felix Balser, Ming Shen and Shaoping Bai
Robotics 2024, 13(8), 120; https://doi.org/10.3390/robotics13080120 - 8 Aug 2024
Abstract
Work-related musculoskeletal disorders (WMSDs) are a common occupational health problem in industries, and they can lead to decreased productivity and a reduced quality of life for workers. Exoskeletons, as an emerging technology, have the potential to solve this challenge by assisting arm movements
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Work-related musculoskeletal disorders (WMSDs) are a common occupational health problem in industries, and they can lead to decreased productivity and a reduced quality of life for workers. Exoskeletons, as an emerging technology, have the potential to solve this challenge by assisting arm movements and reducing muscle effort during load lifting tasks. In this paper, a passive exoskeleton based on a variable stiffness mechanism (VSM) torque generator is proposed and evaluated. This exoskeleton can provide adjustable torque curves and accommodate three degrees of freedom (DOFs) while remaining compact and lightweight. The workspace analysis shows that the workspace of this exoskeleton is sufficient for most industrial manual handling tasks. The experimental results demonstrate that the exoskeleton effectively reduces muscle effort during overhead reaching and load-lifting tasks, highlighting its effectiveness for repetitive tasks in industrial settings.
Full article
(This article belongs to the Section Intelligent Robots and Mechatronics)
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Open AccessArticle
Hand Teleoperation with Combined Kinaesthetic and Tactile Feedback: A Full Upper Limb Exoskeleton Interface Enhanced by Tactile Linear Actuators
by
Daniele Leonardis, Massimiliano Gabardi, Simone Marcheschi, Michele Barsotti, Francesco Porcini, Domenico Chiaradia and Antonio Frisoli
Robotics 2024, 13(8), 119; https://doi.org/10.3390/robotics13080119 - 7 Aug 2024
Abstract
Manipulation involves both fine tactile feedback, with dynamic transients perceived by fingerpad mechanoreceptors, and kinaesthetic force feedback, involving the whole hand musculoskeletal structure. In teleoperation experiments, these fundamental aspects are usually divided between different setups at the operator side: those making use of
[...] Read more.
Manipulation involves both fine tactile feedback, with dynamic transients perceived by fingerpad mechanoreceptors, and kinaesthetic force feedback, involving the whole hand musculoskeletal structure. In teleoperation experiments, these fundamental aspects are usually divided between different setups at the operator side: those making use of lightweight gloves and optical tracking systems, oriented toward tactile-only feedback, and those implementing exoskeletons or grounded manipulators as haptic devices delivering kinaesthetic force feedback. At the level of hand interfaces, exoskeletons providing kinaesthetic force feedback undergo a trade-off between maximum rendered forces and bandpass of the embedded actuators, making these systems unable to properly render tactile feedback. To overcome these limitations, here, we investigate a full upper limb exoskeleton, covering all the upper limb body segments from shoulder to finger phalanxes, coupled with linear voice coil actuators at the fingertips. These are developed to render wide-bandwidth tactile feedback together with the kinaesthetic force feedback provided by the hand exoskeleton. We investigate the system in a pick-and-place teleoperation task, under two different feedback conditions (visual-only and visuo-haptic). The performance based on measured interaction forces and the number of correct trials are evaluated and compared. The study demonstrates the overall feasibility and effectiveness of a complex full upper limb exoskeleton (seven limb-actuated DoFs plus five hand DoFs) capable of combined kinaesthetic and tactile haptic feedback. Quantitative results show significant performance improvements when haptic feedback is provided, in particular for the mean and peak exerted forces, and for the correct rate of the pick-and-place task.
Full article
(This article belongs to the Section Neurorobotics)
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Open AccessArticle
PRF: A Program Reuse Framework for Automated Programming by Learning from Existing Robot Programs
by
Tyler Toner, Dawn M. Tilbury and Kira Barton
Robotics 2024, 13(8), 118; https://doi.org/10.3390/robotics13080118 - 6 Aug 2024
Abstract
This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure
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This paper explores the problem of automated robot program generation from limited historical data when neither accurate geometric environmental models nor online vision feedback are available. The Program Reuse Framework (PRF) is developed, which uses expert-defined motion classes, a novel data structure introduced in this work, to learn affordances, workspaces, and skills from historical data. Historical data comprise raw robot joint trajectories and descriptions of the robot task being completed. Given new tasks, motion classes are then used again to formulate an optimization problem capable of generating new open-loop, skill-based programs to complete the tasks. To cope with a lack of geometric models, a technique to learn safe workspaces from demonstrations is developed, allowing the risk of new programs to be estimated before execution. A new learnable motion primitive for redundant manipulators is introduced, called a redundancy dynamical movement primitive, which enables new end-effector goals to be reached while mimicking the whole-arm behavior of a demonstration. A mobile manipulator part transportation task is used throughout to illustrate each step of the framework.
Full article
(This article belongs to the Section Industrial Robots and Automation)
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Open AccessReview
Holistic Review of UAV-Centric Situational Awareness: Applications, Limitations, and Algorithmic Challenges
by
Somaiyeh MahmoudZadeh, Amirmehdi Yazdani, Yashar Kalantari, Bekir Ciftler, Fathi Aidarus and Mhd Omar Al Kadri
Robotics 2024, 13(8), 117; https://doi.org/10.3390/robotics13080117 - 29 Jul 2024
Abstract
This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper
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This paper presents a comprehensive survey of UAV-centric situational awareness (SA), delineating its applications, limitations, and underlying algorithmic challenges. It highlights the pivotal role of advanced algorithmic and strategic insights, including sensor integration, robust communication frameworks, and sophisticated data processing methodologies. The paper critically analyzes multifaceted challenges such as real-time data processing demands, adaptability in dynamic environments, and complexities introduced by advanced AI and machine learning techniques. Key contributions include a detailed exploration of UAV-centric SA’s transformative potential in industries such as precision agriculture, disaster management, and urban infrastructure monitoring, supported by case studies. In addition, the paper delves into algorithmic approaches for path planning and control, as well as strategies for multi-agent cooperative SA, addressing their respective challenges and future directions. Moreover, this paper discusses forthcoming technological advancements, such as energy-efficient AI solutions, aimed at overcoming current limitations. This holistic review provides valuable insights into the UAV-centric SA, establishing a foundation for future research and practical applications in this domain.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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Open AccessArticle
Enhanced Design of an Adaptive Anthropomorphic Finger through Integration of Modular Soft Actuators and Kinematic Modeling
by
Sheng-Guan Lin and Jen-Yuan (James) Chang
Robotics 2024, 13(8), 116; https://doi.org/10.3390/robotics13080116 - 28 Jul 2024
Abstract
This study introduces a novel modular soft actuator designed for an anthropomorphic robotic finger that addresses the need for adaptive behavior and precise joint-angle control. The key innovation is its modular design, which enables independent pressure regulation in each air chamber, thus achieving
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This study introduces a novel modular soft actuator designed for an anthropomorphic robotic finger that addresses the need for adaptive behavior and precise joint-angle control. The key innovation is its modular design, which enables independent pressure regulation in each air chamber, thus achieving superior precision compared to traditional PneuNets soft actuators. A rigid skeleton is integrated to enhance force transmission and measurement capabilities and thus ensure effective force handling and transmission within each module. The versatility of the actuator is demonstrated through its adaptability in various scenarios, and its features include adaptive positional control achieved by modulating the inflation in each air chamber. This research includes kinematic and kinetostatic analyses to ensure precise control of joint angles and forces at the finger’s endpoint. Experimental results confirm the actuator’s excellent performance and adaptability, providing valuable insights for advancing soft-actuator technology. The findings suggest significant potential for this actuator in diverse applications, emphasizing its role in the future development of precise and adaptable robotic systems.
Full article
(This article belongs to the Special Issue Recent Trends and Advances on Robotics and Mechatronics within the IFToMM Technical Committee on Robotics and Mechatronics)
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Open AccessArticle
Vision-Based Formation Control of Quadrotors Using a Bearing-Only Approach
by
David L. Ramírez-Parada, Héctor M. Becerra, Carlos A. Toro-Arcila and Gustavo Arechavaleta
Robotics 2024, 13(8), 115; https://doi.org/10.3390/robotics13080115 - 28 Jul 2024
Abstract
In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is
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In this paper, we present a vision-based leader–follower strategy for formation control of multiple quadrotors. The leaders use a decoupled visual control scheme based on invariant features. The followers use a control scheme based only on bearing measurements, and a robust control is introduced to deal with perturbations generated by the unknown movement of the leaders. Using this formulation, we study a geometrical pattern formation that can use the distance between the leaders to scale the formation and cross constrained spaces, such as a window. A condition is defined for which a formation has rigidity properties considering the constrained field of view of the cameras, such that invariance to translation and scaling is achieved. This condition allows us to specify a desired formation where the followers do not need to share information between them. Results obtained in a dynamic simulator and real experiments show the effectiveness of the approach.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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Open AccessArticle
Fixed-Wing UAV Pose Estimation Using a Self-Organizing Map and Deep Learning
by
Nuno Pessanha Santos
Robotics 2024, 13(8), 114; https://doi.org/10.3390/robotics13080114 - 27 Jul 2024
Abstract
In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and
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In many Unmanned Aerial Vehicle (UAV) operations, accurately estimating the UAV’s position and orientation over time is crucial for controlling its trajectory. This is especially important when considering the landing maneuver, where a ground-based camera system can estimate the UAV’s 3D position and orientation. A Red, Green, and Blue (RGB) ground-based monocular approach can be used for this purpose, allowing for more complex algorithms and higher processing power. The proposed method uses a hybrid Artificial Neural Network (ANN) model, incorporating a Kohonen Neural Network (KNN) or Self-Organizing Map (SOM) to identify feature points representing a cluster obtained from a binary image containing the UAV. A Deep Neural Network (DNN) architecture is then used to estimate the actual UAV pose based on a single frame, including translation and orientation. Utilizing the UAV Computer-Aided Design (CAD) model, the network structure can be easily trained using a synthetic dataset, and then fine-tuning can be done to perform transfer learning to deal with real data. The experimental results demonstrate that the system achieves high accuracy, characterized by low errors in UAV pose estimation. This implementation paves the way for automating operational tasks like autonomous landing, which is especially hazardous and prone to failure.
Full article
(This article belongs to the Special Issue UAV Systems and Swarm Robotics)
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Open AccessArticle
Learning to Walk with Adaptive Feet
by
Antonello Scaldaferri, Franco Angelini and Manolo Garabini
Robotics 2024, 13(8), 113; https://doi.org/10.3390/robotics13080113 - 24 Jul 2024
Abstract
In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and
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In recent years, tasks regarding autonomous mobility favoredthe use of legged robots rather than wheeled ones thanks to their higher mobility on rough and uneven terrains. This comes at the cost of more complex motion planners and controllers to ensure robot stability and balance. However, in the case of quadrupedal robots, balancing is simpler than it is for bipeds thanks to their larger support polygons. Until a few years ago, most scientists and engineers addressed the quadrupedal locomotion problem with model-based approaches, which require a great deal of modeling expertise. A new trend is the use of data-driven methods, which seem to be quite promising and have shown great results. These methods do not require any modeling effort, but they suffer from computational limitations dictated by the hardware resources used. However, only the design phase of these algorithms requires large computing resources (controller training); their execution in the operational phase (deployment), takes place in real time on common processors. Moreover, adaptive feet capable of sensing terrain profile information have been designed and have shown great performance. Still, no dynamic locomotion control method has been specifically designed to leverage the advantages and supplementary information provided by this type of adaptive feet. In this work, we investigate the use and evaluate the performance of different end-to-end control policies trained via reinforcement learning algorithms specifically designed and trained to work on quadrupedal robots equipped with passive adaptive feet for their dynamic locomotion control over a diverse set of terrains. We examine how the addition of the haptic perception of the terrain affects the locomotion performance.
Full article
(This article belongs to the Special Issue Applications of Neural Networks in Robot Control)
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Open AccessPerspective
The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
by
Souren Pashangpour and Goldie Nejat
Robotics 2024, 13(8), 112; https://doi.org/10.3390/robotics13080112 - 23 Jul 2024
Abstract
The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated
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The potential use of large language models (LLMs) in healthcare robotics can help address the significant demand put on healthcare systems around the world with respect to an aging demographic and a shortage of healthcare professionals. Even though LLMs have already been integrated into medicine to assist both clinicians and patients, the integration of LLMs within healthcare robots has not yet been explored for clinical settings. In this perspective paper, we investigate the groundbreaking developments in robotics and LLMs to uniquely identify the needed system requirements for designing health-specific LLM-based robots in terms of multi-modal communication through human–robot interactions (HRIs), semantic reasoning, and task planning. Furthermore, we discuss the ethical issues, open challenges, and potential future research directions for this emerging innovative field.
Full article
(This article belongs to the Special Issue Robots and Artificial Intelligence for a Better Future of Health Care)
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Open AccessArticle
Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender Stereotypes
by
Sonja Theresa Längle, Stephan Schlögl, Annina Ecker, Willemijn S. M. T. van Kooten and Teresa Spieß
Robotics 2024, 13(8), 111; https://doi.org/10.3390/robotics13080111 - 23 Jul 2024
Abstract
Due to the wide adoption of digital voice assistants (DVAs), interactions with technology have also changed our perceptions, highlighting and reinforcing (mostly) negative gender stereotypes. Regarding the ongoing advancements in the field of human–machine interaction, a developed and improved understanding of and awareness
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Due to the wide adoption of digital voice assistants (DVAs), interactions with technology have also changed our perceptions, highlighting and reinforcing (mostly) negative gender stereotypes. Regarding the ongoing advancements in the field of human–machine interaction, a developed and improved understanding of and awareness of the reciprocity of gender and DVA technology use is thus crucial. Our work in this field expands prior research by including a nonbinary voice option as a means to eschew gender stereotypes. We used a between-subject quasi-experimental questionnaire study (female voice vs. male voice vs. nonbinary voice), in which participants provided feedback on gender stereotypes connected to voice perceptions and personality traits. Our findings show that the overall gender perception of our nonbinary voice leaned towards male on the gender spectrum, whereas the female-gendered and male-gendered voices were clearly identified as such. Furthermore, we found that feminine attributes were clearly tied to our female-gendered voice, whereas the connection of masculine attributes to the male voice was less pronounced. Most notably, however, we did not find gender-stereotypical trait attributions with our nonbinary voice. Results also show that the likability of our female-gendered and nonbinary voices was lower than it was with our male-gendered voice, and that, particularly with the nonbinary voice, this likability was affected by people’s personality traits. Thus, overall, our findings contribute (1) additional theoretical grounding for gender-studies in human–machine interaction, and (2) insights concerning peoples’ perceptions of nonbinary voices, providing additional guidance for researchers, technology designers, and DVA providers.
Full article
(This article belongs to the Special Issue Chatbots and Talking Robots)
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Open AccessArticle
A Reconfigurable UGV for Modular and Flexible Inspection Tasks in Nuclear Sites
by
Ivan Villaverde, Arkaitz Urquiza and Jose Luis Outón
Robotics 2024, 13(7), 110; https://doi.org/10.3390/robotics13070110 - 22 Jul 2024
Abstract
Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide
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Current operations involving Dismantling and Decommissioning (D&D) in nuclear and other harsh environments rely on manual inspection and assessment of the sites, exposing human operators to potentially dangerous situations. This work presents a reconfigurable Autonomous Mobile Robot (AMR) able to mount a wide range of nuclear sensors for flexible and modular inspection tasks in these operations. This AMR is part of the CLEANDEM solution, which uses Unmanned Ground Vehicles (UGVs), nuclear sensors, and a Digital Twin to facilitate a tool for improving D&D operations in nuclear sites. Both the AMR used as a UGV and the system have been successfully tested in real nuclear sites, showing that these tools can greatly aid in operations management and hazard reduction.
Full article
(This article belongs to the Section Aerospace Robotics and Autonomous Systems)
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Open AccessArticle
Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns
by
Theodore Tranos, Christos Spatharis, Konstantinos Blekas and Andreas-Giorgios Stafylopatis
Robotics 2024, 13(7), 109; https://doi.org/10.3390/robotics13070109 - 19 Jul 2024
Abstract
The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal
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The automatic control of vehicle traffic in large urban networks constitutes one of the most serious challenges to modern societies, with an impact on improving the quality of human life and saving energy and time. Intersections are a special traffic structure of pivotal importance as they accumulate a large number of vehicles that should be served in an optimal manner. Constructing intelligent models that manage to automatically coordinate and steer vehicles through intersections is a key point in the fragmentation of traffic control, offering active solutions through the flexibility of automatically adapting to a variety of traffic conditions. Responding to this call, this work aims to propose an integrated active solution of automatic traffic management. We introduce a multi-agent reinforcement learning framework that effectively models traffic flow at individual unsignalized intersections. It relies on a compact agent definition, a rich information state space, and a learning process characterized not only by depth and quality, but also by substantial degrees of freedom and variability. The resulting driving profiles are further transferred to larger road networks to integrate their individual elements and compose an effective automatic traffic control platform. Experiments are conducted on simulated road networks of variable complexity, demonstrating the potential of the proposed method.
Full article
(This article belongs to the Special Issue Active Methods in Autonomous Navigation)
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Open AccessArticle
Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments
by
Quentin Serdel, Julien Marzat and Julien Moras
Robotics 2024, 13(7), 108; https://doi.org/10.3390/robotics13070108 - 18 Jul 2024
Abstract
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions.
[...] Read more.
While mobile ground robots have now the physical capacity of travelling in unstructured challenging environments such as extraterrestrial surfaces or devastated terrains, their safe and efficient autonomous navigation has yet to be improved before entrusting them with complex unsupervised missions in such conditions. Recent advances in machine learning applied to semantic scene understanding and environment representations, coupled with modern embedded computational means and sensors hold promising potential in this matter. This paper therefore introduces the combination of semantic understanding, continuous implicit environment representation and smooth informed path-planning in a new method named COSMAu-Nav. It is specifically dedicated to autonomous ground robot navigation in unstructured environments and adaptable for embedded, real-time usage without requiring any form of telecommunication. Data clustering and Gaussian processes are employed to perform online regression of the environment topography, occupancy and terrain traversability from 3D semantic point clouds while providing an uncertainty modeling. The continuous and differentiable properties of Gaussian processes allow gradient based optimisation to be used for smooth local path-planning with respect to the terrain properties. The proposed pipeline has been evaluated and compared with two reference 3D semantic mapping methods in terms of quality of representation under localisation and semantic segmentation uncertainty using a Gazebo simulation, derived from the 3DRMS dataset. Its computational requirements have been evaluated using the Rellis-3D real world dataset. It has been implemented on a real ground robot and successfully employed for its autonomous navigation in a previously unknown outdoor environment.
Full article
(This article belongs to the Special Issue Decision-Making and Control under Uncertainties for Robotic and Autonomous Systems)
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Open AccessArticle
Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities
by
Joel Baptista, Afonso Castro, Manuel Gomes, Pedro Amaral, Vítor Santos, Filipe Silva and Miguel Oliveira
Robotics 2024, 13(7), 107; https://doi.org/10.3390/robotics13070107 - 17 Jul 2024
Abstract
This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures
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This paper presents a collaborative manufacturing cell implemented in a laboratory setting, focusing on developing learning-based interaction abilities to enhance versatility and ease of use. The key components of the system include 3D real-time volumetric monitoring for safety, visual recognition of hand gestures for human-to-robot communication, classification of physical-contact-based interaction primitives during handover operations, and detection of hand–object interactions to anticipate human intentions. Due to the nature and complexity of perception, deep-learning-based techniques were used to enhance robustness and adaptability. The main components are integrated in a system containing multiple functionalities, coordinated through a dedicated state machine. This ensures appropriate actions and reactions based on events, enabling the execution of specific modules to complete a given multi-step task. An ROS-based architecture supports the software infrastructure among sensor interfacing, data processing, and robot and gripper controllers nodes. The result is demonstrated by a functional use case that involves multiple tasks and behaviors, paving the way for the deployment of more advanced collaborative cells in manufacturing contexts.
Full article
(This article belongs to the Section Industrial Robots and Automation)
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Open AccessArticle
Vision-Based Situational Graphs Exploiting Fiducial Markers for the Integration of Semantic Entities
by
Ali Tourani, Hriday Bavle, Deniz Işınsu Avşar, Jose Luis Sanchez-Lopez, Rafael Munoz-Salinas and Holger Voos
Robotics 2024, 13(7), 106; https://doi.org/10.3390/robotics13070106 - 16 Jul 2024
Abstract
Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining
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Situational Graphs (S-Graphs) merge geometric models of the environment generated by Simultaneous Localization and Mapping (SLAM) approaches with 3D scene graphs into a multi-layered jointly optimizable factor graph. As an advantage, S-Graphs not only offer a more comprehensive robotic situational awareness by combining geometric maps with diverse, hierarchically organized semantic entities and their topological relationships within one graph, but they also lead to improved performance of localization and mapping on the SLAM level by exploiting semantic information. In this paper, we introduce a vision-based version of S-Graphs where a conventional Visual SLAM (VSLAM) system is used for low-level feature tracking and mapping. In addition, the framework exploits the potential of fiducial markers (both visible and our recently introduced transparent or fully invisible markers) to encode comprehensive information about environments and the objects within them. The markers aid in identifying and mapping structural-level semantic entities, including walls and doors in the environment, with reliable poses in the global reference, subsequently establishing meaningful associations with higher-level entities, including corridors and rooms. However, in addition to including semantic entities, the semantic and geometric constraints imposed by the fiducial markers are also utilized to improve the reconstructed map’s quality and reduce localization errors. Experimental results on a real-world dataset collected using legged robots show that our framework excels in crafting a richer, multi-layered hierarchical map and enhances robot pose accuracy at the same time.
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(This article belongs to the Special Issue Localization and 3D Mapping of Intelligent Robotics)
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Open AccessArticle
ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement
by
José Francisco Villa-Tiburcio, José Antonio Estrada-Torres, Rodrigo Hernández-Alvarado, Josue Rafael Montes-Martínez, Darío Bringas-Posadas and Edgar Adrián Franco-Urquiza
Robotics 2024, 13(7), 105; https://doi.org/10.3390/robotics13070105 - 13 Jul 2024
Abstract
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In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without
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In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).
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Open AccessArticle
A Framework for Enhanced Human–Robot Collaboration during Disassembly Using Digital Twin and Virtual Reality
by
Timon Hoebert, Stephan Seibel, Manuel Amersdorfer, Markus Vincze, Wilfried Lepuschitz and Munir Merdan
Robotics 2024, 13(7), 104; https://doi.org/10.3390/robotics13070104 - 12 Jul 2024
Abstract
This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related
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This paper presents a framework that integrates digital twin and virtual reality (VR) technologies to improve the efficiency and safety of human–robot collaborative systems in the disassembly domain. With the increasing complexity of the handling of end-of-life electronic products and as the related disassembly tasks are characterized by variabilities such as rust, deformation, and diverse part geometries, traditional industrial robots face significant challenges in this domain. These challenges require adaptable and flexible automation solutions that can work safely alongside human workers. We developed an architecture to address these challenges and support system configuration, training, and operational monitoring. Our framework incorporates a digital twin to provide a real-time virtual representation of the physical disassembly process, allowing for immediate feedback and dynamic adjustment of operations. In addition, VR is used to simulate and optimize the workspace layout, improve human–robot interaction, and facilitate safe and effective training scenarios without the need for physical prototypes. A unique case study is presented, where the collaborative system is specifically applied to the disassembly of antenna amplifiers, illustrating the potential of our comprehensive approach to facilitate engineering processes and enhance collaborative safety.
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(This article belongs to the Special Issue Digital Twin-Based Human–Robot Collaborative Systems)
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Open AccessArticle
Development, Experimental, and Numerical Characterisation of Novel Flexible Strain Sensors for Soft Robotics Applications
by
Sylvester Ndidiamaka Nnadi, Ivor Ajadalu, Amir Rahmani, Aliyu Aliyu, Khaled Elgeneidy, Allahyar Montazeri and Behnaz Sohani
Robotics 2024, 13(7), 103; https://doi.org/10.3390/robotics13070103 - 11 Jul 2024
Abstract
Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to
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Medical and agricultural robots that interact with living tissue or pick fruit require tactile and flexible sensors to minimise or eliminate damage. Until recently, research has focused on the development of robots made of rigid materials, such as metal or plastic. Due to their complex configuration, poor spatial adaptability and low flexibility, rigid robots are not fully applicable in some special environments such as limb rehabilitation, fragile objects gripping, human–machine interaction, and locomotion. All these should be done in an accurate and safe manner for them to be useful. However, the design and manufacture of soft robot parts that interact with living tissue or fragile objects is not as straightforward. Given that hyper-elasticity and conductivity are involved, conventional (subtractive) manufacturing can result in wasted materials (which are expensive), incompatible parts due to different physical properties, and high costs. In this work, additive manufacturing (3D printing) is used to produce a conductive, composite flexible sensor. Its electrical response was tested based on various physical conditions. Finite element analysis (FEA) was used to characterise its deformation and stress behaviour for optimisation to achieve functionality and durability. Also, a nonlinear regression model was developed for the sensor’s performance.
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(This article belongs to the Special Issue Soft Robotics: Fusing Function with Structure)
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