Pub Date : 2024-07-24DOI: 10.3390/robotics13080113
Antonello Scaldaferri, Franco Angelini, M. Garabini
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.
{"title":"Learning to Walk with Adaptive Feet","authors":"Antonello Scaldaferri, Franco Angelini, M. Garabini","doi":"10.3390/robotics13080113","DOIUrl":"https://doi.org/10.3390/robotics13080113","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141809478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.3390/robotics13080112
Souren Pashangpour, G. Nejat
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.
{"title":"The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare","authors":"Souren Pashangpour, G. Nejat","doi":"10.3390/robotics13080112","DOIUrl":"https://doi.org/10.3390/robotics13080112","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141811912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-23DOI: 10.3390/robotics13080111
Sonja Theresa Längle, Stephan Schlögl, Annina Ecker, Willemijn S. M. T. van Kooten, Teresa Spiess
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 n=318 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.
由于数字语音助手（DVA）的广泛应用，与技术的互动也改变了我们的观念，凸显并强化了（大部分）负面的性别刻板印象。随着人机交互领域的不断进步，对性别和数字语音助理技术使用的互惠性的理解和认识的发展和提高至关重要。我们在这一领域的工作扩展了之前的研究，加入了非二元语音选项，以此来摒弃性别刻板印象。我们使用了一项主体间准实验问卷研究（女声 vs. 男声 vs. 非二元语音），共有 318 名参与者提供了与语音感知和个性特征相关的性别刻板印象反馈。我们的研究结果表明，非二元声音的整体性别认知偏向于性别光谱中的男性，而女性性别和男性性别的声音则被明确识别为男性声音。此外，我们还发现，女性特质与我们的女性性别声音有着明显的联系，而男性特质与男性声音的联系则不那么明显。然而，最值得注意的是，我们没有发现我们的非二元声音具有性别陈规定型特征。研究结果还显示，女性性别声音和非二元声音的好感度低于男性性别声音，尤其是非二元声音，这种好感度受到人们人格特质的影响。因此，总的来说，我们的研究结果有助于：（1）为人机交互中的性别研究提供更多理论依据；（2）深入了解人们对非二元声音的看法，为研究人员、技术设计人员和 DVA 提供者提供更多指导。
{"title":"Nonbinary Voices for Digital Assistants—An Investigation of User Perceptions and Gender Stereotypes","authors":"Sonja Theresa Längle, Stephan Schlögl, Annina Ecker, Willemijn S. M. T. van Kooten, Teresa Spiess","doi":"10.3390/robotics13080111","DOIUrl":"https://doi.org/10.3390/robotics13080111","url":null,"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 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 n=318 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-22DOI: 10.3390/robotics13070110
Ivan Villaverde, Arkaitz Urquiza, Jose Luis Outón
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.
{"title":"A Reconfigurable UGV for Modular and Flexible Inspection Tasks in Nuclear Sites","authors":"Ivan Villaverde, Arkaitz Urquiza, Jose Luis Outón","doi":"10.3390/robotics13070110","DOIUrl":"https://doi.org/10.3390/robotics13070110","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141816544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns","authors":"Theodore Tranos, Christos Spatharis, Konstantinos Blekas, Andreas-Giorgios Stafylopatis","doi":"10.3390/robotics13070109","DOIUrl":"https://doi.org/10.3390/robotics13070109","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141822538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-18DOI: 10.3390/robotics13070108
Quentin Serdel, J. Marzat, Julien Moras
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.
{"title":"Continuous Online Semantic Implicit Representation for Autonomous Ground Robot Navigation in Unstructured Environments","authors":"Quentin Serdel, J. Marzat, Julien Moras","doi":"10.3390/robotics13070108","DOIUrl":"https://doi.org/10.3390/robotics13070108","url":null,"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. 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141824996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-17DOI: 10.3390/robotics13070107
Joel Baptista, Afonso Castro, Manuel Gomes, Pedro Amaral, Vítor M. F. Santos, Filipe Silva, Miguel Oliveira
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.
{"title":"Human–Robot Collaborative Manufacturing Cell with Learning-Based Interaction Abilities","authors":"Joel Baptista, Afonso Castro, Manuel Gomes, Pedro Amaral, Vítor M. F. Santos, Filipe Silva, Miguel Oliveira","doi":"10.3390/robotics13070107","DOIUrl":"https://doi.org/10.3390/robotics13070107","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141829717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-13DOI: 10.3390/robotics13070105
José Francisco Villa-Tiburcio, José Antonio Estrada-Torres, R. Hernández-Alvarado, Josue-Rafael Montes-Martínez, Darío Bringas-Posadas, E. Franco-Urquiza
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).
{"title":"ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement","authors":"José Francisco Villa-Tiburcio, José Antonio Estrada-Torres, R. Hernández-Alvarado, Josue-Rafael Montes-Martínez, Darío Bringas-Posadas, E. Franco-Urquiza","doi":"10.3390/robotics13070105","DOIUrl":"https://doi.org/10.3390/robotics13070105","url":null,"abstract":"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).","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141651472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-12DOI: 10.3390/robotics13070104
Timon Hoebert, Stephan Seibel, Manuel Amersdorfer, Markus Vincze, Wilfried Lepuschitz, M. Merdan
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.
{"title":"A Framework for Enhanced Human–Robot Collaboration during Disassembly Using Digital Twin and Virtual Reality","authors":"Timon Hoebert, Stephan Seibel, Manuel Amersdorfer, Markus Vincze, Wilfried Lepuschitz, M. Merdan","doi":"10.3390/robotics13070104","DOIUrl":"https://doi.org/10.3390/robotics13070104","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141653497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-07-11DOI: 10.3390/robotics13070103
Sylvester Ndidiamaka Nnadi, Ivor Ajadalu, Amir Rahmani, A. Aliyu, Khaled Elgeneidy, Allahyar Montazeri, B. Sohani
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.
{"title":"Development, Experimental, and Numerical Characterisation of Novel Flexible Strain Sensors for Soft Robotics Applications","authors":"Sylvester Ndidiamaka Nnadi, Ivor Ajadalu, Amir Rahmani, A. Aliyu, Khaled Elgeneidy, Allahyar Montazeri, B. Sohani","doi":"10.3390/robotics13070103","DOIUrl":"https://doi.org/10.3390/robotics13070103","url":null,"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 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.","PeriodicalId":506759,"journal":{"name":"Robotics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141656283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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