Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.
{"title":"DQN as an alternative to Market-based approaches for Multi-Robot processing Task Allocation (MRpTA)","authors":"Paul Gautier, J. Laurent, J. Diguet","doi":"10.35708/rc1870-126266","DOIUrl":"https://doi.org/10.35708/rc1870-126266","url":null,"abstract":"Multi-robot task allocation (MRTA) problems require that robots make complex choices based on their understanding of a dynamic and uncertain environment. As a distributed computing system, the Multi-Robot System (MRS) must handle and distribute processing tasks (MRpTA). Each robot must contribute to the overall efficiency of the system based solely on a limited knowledge of its environment. Market-based methods are a natural candidate to deal processing tasks over a MRS but recent and numerous developments in reinforcement learning and especially Deep Q-Networks (DQN) provide new opportunities to solve the problem. In this paper we propose a new DQN-based method so that robots can learn directly from experience, and compare it with Market-based approaches as well with centralized and purely local solutions. \u0000Our study shows the relevancy of learning-based methods and also highlight research challenges to solve the processing load-balancing problem in MRS.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123948523","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}
This article presents an approach for determining suitable camera view poses for inspection of surface tolerances based on visual tracking of the tool movements performed by a skilled worker. Automated surface inspection of a workpiece adjusted by manual operations depends on manual programming of the inspecting robot, or a timeconsuming exhaustive search over the entire surface. The proposed approach is based on the assumption that the tool movements of the skilled worker coincide with the most relevant regions of the underlying surface of the workpiece, namely the parts where a manual process has been performed. The affected region is detected with a visual tracking system, which measures the motion of the tool using a low-cost RGBD-camera, a particle filter, and a CAD model of the tool. The main contribution is a scheme for selecting relevant camera view poses for inspecting the affected region using a robot equipped with a high-accuracy RGBDcamera. A principal component analysis of the tracked tool paths allows for evaluating the view poses by the Hotelling’s T-squared distribution test in order to sort and select suitable camera view poses. The approach is implemented and tested for the case where a large ship propeller blade cast in NiAl bronze is to be inspected by a robot after manual adjustments of its surface.
{"title":"View Planning for Robotic Inspection of Tolerances Through Visual Tracking of Manual Surface Finishing Operations","authors":"E. B. Njaastad","doi":"10.35708/rc1869-126261","DOIUrl":"https://doi.org/10.35708/rc1869-126261","url":null,"abstract":"This article presents an approach for determining suitable\u0000camera view poses for inspection of surface tolerances based on visual\u0000tracking of the tool movements performed by a skilled worker. Automated surface inspection of a workpiece adjusted by manual operations\u0000depends on manual programming of the inspecting robot, or a timeconsuming exhaustive search over the entire surface. The proposed approach is based on the assumption that the tool movements of the skilled\u0000worker coincide with the most relevant regions of the underlying surface\u0000of the workpiece, namely the parts where a manual process has been\u0000performed. The affected region is detected with a visual tracking system,\u0000which measures the motion of the tool using a low-cost RGBD-camera,\u0000a particle filter, and a CAD model of the tool. The main contribution\u0000is a scheme for selecting relevant camera view poses for inspecting the\u0000affected region using a robot equipped with a high-accuracy RGBDcamera. A principal component analysis of the tracked tool paths allows\u0000for evaluating the view poses by the Hotelling’s T-squared distribution\u0000test in order to sort and select suitable camera view poses. The approach\u0000is implemented and tested for the case where a large ship propeller blade\u0000cast in NiAl bronze is to be inspected by a robot after manual adjustments of its surface.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132489003","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 estimation of the intrinsic properties of an unknown ob- ject is a very challenging problem, mainly due the limitations on the tactile technology. In this article we present a method to estimate an ob- ject's weight during a precision grip made by a humanoid robot. Tactile sensors on the ngertips provide information on the 3D force vector dur- ing a movement of grasping and lifting a cup lled with dierent masses (30-100g). Using the force measurements across time, we were able to successfully calculate the object weight for 8 dierent masses in two sce- narios: (i) Manually segmented force measurments and (ii) automatically segmented force measurments. Regarding the manually segmented data, we are able to have repeatable measurement and low deviations from the real value, especially for higher object masses. Regarding the automat- ically segmented data, we are able to identify the various phases of the grasping experiment and use the segmented phases to compute the mass automatically.
{"title":"Towards Autonomous Estimation of Lightweight Object's Mass by a Humanoid Robot during a Precision Grip with Soft Tactile Sensors","authors":"A. Silva","doi":"10.35708/rc1869-126257","DOIUrl":"https://doi.org/10.35708/rc1869-126257","url":null,"abstract":"The estimation of the intrinsic properties of an unknown ob-\u0000ject is a very challenging problem, mainly due the limitations on the \u0000tactile technology. In this article we present a method to estimate an ob-\u0000ject's weight during a precision grip made by a humanoid robot. Tactile \u0000sensors on the ngertips provide information on the 3D force vector dur-\u0000ing a movement of grasping and lifting a cup lled with dierent masses \u0000(30-100g). Using the force measurements across time, we were able to \u0000successfully calculate the object weight for 8 dierent masses in two sce-\u0000narios: (i) Manually segmented force measurments and (ii) automatically \u0000segmented force measurments. Regarding the manually segmented data,\u0000we are able to have repeatable measurement and low deviations from the\u0000real value, especially for higher object masses. Regarding the automat-\u0000ically segmented data, we are able to identify the various phases of the\u0000grasping experiment and use the segmented phases to compute the mass\u0000automatically.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130000248","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}
Ten years after its rst release, the Robot Operating System (ROS) is arguably the most popular software framework used to pro- gram robots. It achieved such status despite its shortcomings compared to alternatives similarly centered on manual programming and, perhaps surprisingly, to model-driven engineering (MDE) approaches. Based on our experience, we identied possible ways to leverage the accessibility of ROS and its large software ecosystem, while providing quality assurance measures through selected MDE techniques. After describing our vision on how to combine MDE and manually written code, we present the rst technical contribution in this pursuit: a family of three metamodels to respectively model ROS nodes, communication interfaces, and sys- tems. Such metamodels can be used, through the accompanying Eclipse- based tooling made publicly available, to model ROS systems of arbitrary complexity and generate with correctness guarantees the software arti- facts for their composition and deployment. Furthermore, they account for specications on these aspects by the Object Management Group (OMG), in order to be amenable to hybrid systems coupling ROS and other frameworks. We also report on our experience with a large and complex corpus of ROS software including the shortcomings of standard ROS tools and of previous eorts on ROS modeling.
{"title":"Bootstrapping MDE Development from ROS Manual Code","authors":"N. Garcia","doi":"10.35708/rc1869-126256","DOIUrl":"https://doi.org/10.35708/rc1869-126256","url":null,"abstract":"Ten years after its rst release, the Robot Operating System \u0000(ROS) is arguably the most popular software framework used to pro-\u0000gram robots. It achieved such status despite its shortcomings compared \u0000to alternatives similarly centered on manual programming and, perhaps\u0000surprisingly, to model-driven engineering (MDE) approaches. Based on\u0000our experience, we identied possible ways to leverage the accessibility of\u0000ROS and its large software ecosystem, while providing quality assurance\u0000measures through selected MDE techniques. After describing our vision\u0000on how to combine MDE and manually written code, we present the\u0000rst technical contribution in this pursuit: a family of three metamodels \u0000to respectively model ROS nodes, communication interfaces, and sys-\u0000tems. Such metamodels can be used, through the accompanying Eclipse-\u0000based tooling made publicly available, to model ROS systems of arbitrary \u0000complexity and generate with correctness guarantees the software arti-\u0000facts for their composition and deployment. Furthermore, they account \u0000for specications on these aspects by the Object Management Group\u0000(OMG), in order to be amenable to hybrid systems coupling ROS and\u0000other frameworks. We also report on our experience with a large and\u0000complex corpus of ROS software including the shortcomings of standard\u0000ROS tools and of previous eorts on ROS modeling.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125719321","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}
Safe interactions between humans and robots are needed in several industrial processes and service tasks. Compliance design and control of mechanisms is a way to increase safety. This article presents a compliant revolute joint mechanism using a biphasic media variable stiffness actuator. The actuator has a member configured to transmit motion that is connected to a fluidic circuit, into which a biphasic control fluid circulates. Stiffness is controlled by changing pressure of control fluid into distribution lines. A mathematical model of the actuator is presented, a model-based control method is implemented to track the desired position and stiffness, and equations relating to the dynamics of the mechanism are provided. Results from force loaded and unloaded simulations and experiments with a physical prototype are discussed. The additional information covers a detailed description of the system and its physical implementation.
{"title":"Simultaneous position and stiffness control of a revolute\u0000joint using a biphasic media variable stiffness actuator","authors":"Jesus H Lugo","doi":"10.35708/rc1868-126252","DOIUrl":"https://doi.org/10.35708/rc1868-126252","url":null,"abstract":"Safe interactions between humans and robots are needed in several industrial processes and service tasks. \u0000Compliance design and control of mechanisms is a way to increase safety.\u0000This article presents a compliant revolute joint mechanism using a biphasic media variable stiffness actuator. The actuator has a member configured to transmit motion that is connected to a fluidic circuit, into which a biphasic control fluid circulates. Stiffness is controlled by changing pressure of control fluid into distribution lines. A mathematical model of the actuator is presented, a model-based control method is implemented to track the desired position and stiffness, and equations relating to the dynamics of the mechanism are provided. Results from force loaded and unloaded simulations and experiments with a physical prototype are discussed. The additional information covers a detailed description of the system and its physical implementation.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668997","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}
In this paper, the well known recursive Newton-Euler inverse dynamics algorithm for serial manipulators is reformulated into the context of the algebra of Dual Quaternions. Here we structure the forward kinematic description with screws and line displacements rather than the well established Denavit-Hartemberg parameters, thus accounting better efficiency, compactness and simpler dynamical models. We also present here the closed solution for the dqRNEA, and to do so we formalize some of the algebra for dual quaternion-vectors and dual quaternion-matrices. With a closed formulation of the dqRNEA we also create a dual quaternion based formulation for the computed torque control, a feedback linearization method for controlling a serial manipulator's torques in the joint space. Finally, a cost analysis of the main Dual Quaternions operations and of the Newton-Euler inverse dynamics algorithm as a whole is made and compared with other results in the literature.
{"title":"A Novel Dual Quaternion Based Cost Effcient Recursive Newton-Euler Inverse Dynamics Algorithm","authors":"Cristiana Miranda de Farias","doi":"10.35708/rc1868-126255","DOIUrl":"https://doi.org/10.35708/rc1868-126255","url":null,"abstract":"In this paper, the well known recursive Newton-Euler\u0000inverse dynamics algorithm for serial manipulators is reformulated into\u0000the context of the algebra of Dual Quaternions. Here we structure\u0000the forward kinematic description with screws and line displacements\u0000rather than the well established Denavit-Hartemberg parameters, thus\u0000accounting better efficiency, compactness and simpler dynamical models.\u0000We also present here the closed solution for the dqRNEA, and to do\u0000so we formalize some of the algebra for dual quaternion-vectors and\u0000dual quaternion-matrices. With a closed formulation of the dqRNEA\u0000we also create a dual quaternion based formulation for the computed\u0000torque control, a feedback linearization method for controlling a serial\u0000manipulator's torques in the joint space. Finally, a cost analysis of the\u0000main Dual Quaternions operations and of the Newton-Euler inverse\u0000dynamics algorithm as a whole is made and compared with other results\u0000in the literature.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131535386","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}
Robotic prosthetic hands are commonly controlled using electromyography (EMG) signals as a means of inferring user intention. However, relying on EMG signals alone, although provides very good results in lab settings, is not sufficiently robust to real-life conditions. For this reason, taking advantage of other contextual clues are proposed in previous works. In this work, we propose a method for intention inference based on particle filtering (PF) based on user hand's trajectory information. Our methodology, also provides an estimate of time-to-arrive, i.e. time left until reaching to the object, which is an essential variable in successful grasping of objects. The proposed probabilistic framework can incorporate available sources of information to improve the inference process. We also provide a data-driven method based on hidden Markov model (HMM) as a baseline for intention inference. HMM is widely used for human gesture classification. The algorithms were tested (and trained) with regards to 160 reaching trajectories collected from 10 subjects reaching to one of four objects at a time.
{"title":"Particle Filters vs Hidden Markov Models for Prosthetic Robot Hand Grasp Selection","authors":"M. Sharif","doi":"10.35708/rc1868-126253","DOIUrl":"https://doi.org/10.35708/rc1868-126253","url":null,"abstract":"Robotic prosthetic hands are commonly controlled using electromyography (EMG) signals as a means of inferring user intention. However, relying on EMG signals alone, although provides very good results in lab settings, is not sufficiently robust to real-life conditions. For this reason, taking advantage of other contextual clues are proposed in previous works. In this work, we propose a method for intention inference based on particle filtering (PF) based on user hand's trajectory information. Our methodology, also provides an estimate of time-to-arrive, i.e. time left until reaching to the object, which is an essential variable in successful grasping of objects. The proposed probabilistic framework can incorporate available sources of information to improve the inference process. We also provide a data-driven method based on hidden Markov model (HMM) as a baseline for intention inference. HMM is widely used for human gesture classification. The algorithms were tested (and trained) with regards to 160 reaching trajectories collected from 10 subjects reaching to one of four objects at a time.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124317361","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}
Arabic poetry generation is a very challenging task since the linguistic structure of the Arabic language is considered a severe challenge for many researchers and developers in the Natural Language Processing (NLP) field. In this paper, we propose a poetry generation model with extended phonetic and semantic embeddings (Phonetic CNNsubword embeddings). We show that Phonetic CNNsubword embeddings have an effective contribution to the overall model performance compared to FastTextsubword embeddings. Our poetry generation model consists of a two-stage approach: (1.) generating the first verse which explicitly incorporates the theme related phrase, (2.) other verses generation with the proposed Hierarchy-Attention Sequence-to-Sequence model (HAS2S), which adequately capture word, phrase, and verse information between contexts. A comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria such as Meaning, Coherence, Fluency, and Poeticness. Extensive quantitative experiments using Bi-Lingual Evaluation Understudy (BLEU) scores also demonstrate significant improvements over strong baselines.
{"title":"Arabic Poem Generation Incorporating Deep Learning and Phonetic CNNsubword Embedding Models","authors":"Sameerah Talafha, Banafsheh Rekabdar","doi":"10.35708/tai1868-126246","DOIUrl":"https://doi.org/10.35708/tai1868-126246","url":null,"abstract":"Arabic poetry generation is a very challenging task since the linguistic structure of the Arabic language is considered a severe challenge for many researchers and developers in the Natural Language Processing (NLP) field. In this paper, we propose a poetry generation model with extended phonetic and semantic embeddings (Phonetic CNNsubword embeddings). We show that Phonetic CNNsubword embeddings have an\u0000effective contribution to the overall model performance compared to FastTextsubword embeddings. Our poetry generation model consists of a two-stage approach: (1.) generating the first verse which explicitly incorporates the theme related phrase, (2.) other verses generation with the proposed Hierarchy-Attention Sequence-to-Sequence model (HAS2S), which adequately capture word, phrase, and verse information between contexts. A comprehensive human evaluation confirms that the poems generated by our model outperform the base models in criteria such as Meaning, Coherence, Fluency, and Poeticness. Extensive quantitative experiments using Bi-Lingual Evaluation Understudy (BLEU) scores also demonstrate significant improvements over strong baselines.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126875897","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}
Caroline Sabty, A. Sherif, M. Elmahdy, Slim Abdennadher
As a result of globalization and better quality of education, a signifcant percentage of the population in Arab countries have become bilingual/multilingual. This has raised the frequency of code-switching and code-mixing among Arabs in daily communication. Consequently, huge amount of Code-Mixed (CM) content can be found on different social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges emerging due to this multilingual phenomenon. Named-Entity Recognition (NER) is one of the major tasks for several NLP systems. It is the process of identifying named entities in text. However, there is a lack of annotated CM data and resources for such task. This work aims at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embeddings for Arabic-English CM text. Moreover, we investigated the usage of different types of classical and contextual pre-trained word embeddings on our system. The highest NER system achieved an F1-score of 77.69% by combining classical and contextual word embeddings.
{"title":"Techniques for Named Entity Recognition on Arabic-English Code-Mixed Data","authors":"Caroline Sabty, A. Sherif, M. Elmahdy, Slim Abdennadher","doi":"10.35708/tai1868-126245","DOIUrl":"https://doi.org/10.35708/tai1868-126245","url":null,"abstract":"As a result of globalization and better quality of education,\u0000a signifcant percentage of the population in Arab countries have become\u0000bilingual/multilingual. This has raised the frequency of code-switching\u0000and code-mixing among Arabs in daily communication. Consequently,\u0000huge amount of Code-Mixed (CM) content can be found on different\u0000social media platforms. Such data could be analyzed and used in different Natural Language Processing (NLP) tasks to tackle the challenges\u0000emerging due to this multilingual phenomenon. Named-Entity Recognition (NER) is one of the major tasks for several NLP systems. It is\u0000the process of identifying named entities in text. However, there is a\u0000lack of annotated CM data and resources for such task. This work aims\u0000at collecting and building the first annotated CM Arabic-English corpus for NER. Furthermore, we constructed a baseline NER system using deep neural networks and word embeddings for Arabic-English CM\u0000text. Moreover, we investigated the usage of different types of classical\u0000and contextual pre-trained word embeddings on our system. The highest\u0000NER system achieved an F1-score of 77.69% by combining classical and\u0000contextual word embeddings.","PeriodicalId":292418,"journal":{"name":"International Journal of Robotic Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130726381","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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