Pub Date : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197263
Jeffrey A. Caley, Geoffrey A. Hollinger
Robots often require a model of their environment to make informed decisions. In unknown environments, the ability to infer the value of a data field from a limited number of samples is essential to many robotics applications. In this work, we propose a neural network architecture to model these spatially correlated data fields based on a limited number of spatially continuous samples. Additionally, we provide a method based on biased loss functions to suggest future areas of exploration to minimize reconstruction error. We run simulated robotic information gathering trials on both the MNIST hand written digits dataset and a Regional Ocean Modeling System (ROMS) ocean dataset for ocean monitoring. Our method outperforms Gaussian process regression in both environments for modeling the data field and action selection.
{"title":"Environment Prediction from Sparse Samples for Robotic Information Gathering","authors":"Jeffrey A. Caley, Geoffrey A. Hollinger","doi":"10.1109/ICRA40945.2020.9197263","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197263","url":null,"abstract":"Robots often require a model of their environment to make informed decisions. In unknown environments, the ability to infer the value of a data field from a limited number of samples is essential to many robotics applications. In this work, we propose a neural network architecture to model these spatially correlated data fields based on a limited number of spatially continuous samples. Additionally, we provide a method based on biased loss functions to suggest future areas of exploration to minimize reconstruction error. We run simulated robotic information gathering trials on both the MNIST hand written digits dataset and a Regional Ocean Modeling System (ROMS) ocean dataset for ocean monitoring. Our method outperforms Gaussian process regression in both environments for modeling the data field and action selection.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79241131","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197038
Nadia Figueroa, Salman Faraji, M. Koptev, A. Billard
In this paper, we present an integrated approach that provides compliant control of an iCub humanoid robot and adaptive reaching, grasping, navigating and co-manipulating capabilities. We use state-dependent dynamical systems (DS) to (i) coordinate and drive the robots hands (in both position and orientation) to grasp an object using an intermediate virtual object, and (ii) drive the robot's base while walking/navigating. The use of DS as motion generators allows us to adapt smoothly as the object moves and to re-plan on-line motion of the arms and body to reach the object's new location. The desired motion generated by the DS are used in combination with a whole-body compliant control strategy that absorbs perturbations while walking and offers compliant behaviors for grasping and manipulation tasks. Further, the desired dynamics for the arm and body can be learned from demonstrations. By integrating these components, we achieve unprecedented adaptive behaviors for whole body manipulation. We showcase this in simulations and real-world experiments where iCub robots (i) walk-to-grasp objects, (ii) follow a human (or another iCub) through interaction and (iii) learn to navigate or comanipulate an object from human guided demonstrations; whilst being robust to changing targets and perturbations.
{"title":"A Dynamical System Approach for Adaptive Grasping, Navigation and Co-Manipulation with Humanoid Robots","authors":"Nadia Figueroa, Salman Faraji, M. Koptev, A. Billard","doi":"10.1109/ICRA40945.2020.9197038","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197038","url":null,"abstract":"In this paper, we present an integrated approach that provides compliant control of an iCub humanoid robot and adaptive reaching, grasping, navigating and co-manipulating capabilities. We use state-dependent dynamical systems (DS) to (i) coordinate and drive the robots hands (in both position and orientation) to grasp an object using an intermediate virtual object, and (ii) drive the robot's base while walking/navigating. The use of DS as motion generators allows us to adapt smoothly as the object moves and to re-plan on-line motion of the arms and body to reach the object's new location. The desired motion generated by the DS are used in combination with a whole-body compliant control strategy that absorbs perturbations while walking and offers compliant behaviors for grasping and manipulation tasks. Further, the desired dynamics for the arm and body can be learned from demonstrations. By integrating these components, we achieve unprecedented adaptive behaviors for whole body manipulation. We showcase this in simulations and real-world experiments where iCub robots (i) walk-to-grasp objects, (ii) follow a human (or another iCub) through interaction and (iii) learn to navigate or comanipulate an object from human guided demonstrations; whilst being robust to changing targets and perturbations.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84787223","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197209
Ruihua Han, Shengduo Chen, Qi Hao
The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.
{"title":"Cooperative Multi-Robot Navigation in Dynamic Environment with Deep Reinforcement Learning","authors":"Ruihua Han, Shengduo Chen, Qi Hao","doi":"10.1109/ICRA40945.2020.9197209","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197209","url":null,"abstract":"The challenges of multi-robot navigation in dynamic environments lie in uncertainties in obstacle complexities, partially observation of robots, and policy implementation from simulations to the real world. This paper presents a cooperative approach to address the multi-robot navigation problem (MRNP) under dynamic environments using a deep reinforcement learning (DRL) framework, which can help multiple robots jointly achieve optimal paths despite a certain degree of obstacle complexities. The novelty of this work includes threefold: (1) developing a cooperative architecture that robots can exchange information with each other to select the optimal target locations; (2) developing a DRL based framework which can learn a navigation policy to generate the optimal paths for multiple robots; (3) developing a training mechanism based on dynamics randomization which can make the policy generalized and achieve the maximum performance in the real world. The method is tested with Gazebo simulations and 4 differential drive robots. Both simulation and experiment results validate the superior performance of the proposed method in terms of success rate and travel time when compared with the other state-of-art technologies.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84804878","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196545
Alec Orlofsky, Chang Liu, S. Kamrava, A. Vaziri, Samuel M. Felton
This paper presents a robotic gripper design that can perform customizable grasping tasks at the millimeter scale. The design is based on the origami string, a mechanism with a single degree of freedom that can be mechanically programmed to approximate arbitrary paths in space. By using this concept, we create miniature fingers that bend at multiple joints with a single actuator input. The shape and stiffness of these fingers can be varied to fit different grasping tasks by changing the crease pattern of the string. We show that the experimental behavior of these strings follows their analytical models and that they can perform a variety of tasks including pinching, wrapping, and twisting common objects such as pencils, bottle caps, and blueberries.
{"title":"Mechanically Programmed Miniature Origami Grippers","authors":"Alec Orlofsky, Chang Liu, S. Kamrava, A. Vaziri, Samuel M. Felton","doi":"10.1109/ICRA40945.2020.9196545","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196545","url":null,"abstract":"This paper presents a robotic gripper design that can perform customizable grasping tasks at the millimeter scale. The design is based on the origami string, a mechanism with a single degree of freedom that can be mechanically programmed to approximate arbitrary paths in space. By using this concept, we create miniature fingers that bend at multiple joints with a single actuator input. The shape and stiffness of these fingers can be varied to fit different grasping tasks by changing the crease pattern of the string. We show that the experimental behavior of these strings follows their analytical models and that they can perform a variety of tasks including pinching, wrapping, and twisting common objects such as pencils, bottle caps, and blueberries.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85445203","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197498
Raphaela Kreiser, Gabriel Waibel, Nuria Armengol, Alpha Renner, Yulia Sandamirskaya
Neuromorphic hardware offers computing platforms for the efficient implementation of spiking neural networks (SNNs) that can be used for robot control. Here, we present such an SNN on a neuromorphic chip that solves a number of tasks related to simultaneous localization and mapping (SLAM): forming a map of an unknown environment and, at the same time, estimating the robot's pose. In particular, we present an SNN mechanism to detect and estimate errors when the robot revisits a known landmark and updates both the map and the path integration speed to reduce the error. The whole system is fully realized in a neuromorphic device, showing the feasibility of a purely SNN-based SLAM, which could be efficiently implemented in a small form-factor neuromorphic chip.
{"title":"Error estimation and correction in a spiking neural network for map formation in neuromorphic hardware","authors":"Raphaela Kreiser, Gabriel Waibel, Nuria Armengol, Alpha Renner, Yulia Sandamirskaya","doi":"10.1109/ICRA40945.2020.9197498","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197498","url":null,"abstract":"Neuromorphic hardware offers computing platforms for the efficient implementation of spiking neural networks (SNNs) that can be used for robot control. Here, we present such an SNN on a neuromorphic chip that solves a number of tasks related to simultaneous localization and mapping (SLAM): forming a map of an unknown environment and, at the same time, estimating the robot's pose. In particular, we present an SNN mechanism to detect and estimate errors when the robot revisits a known landmark and updates both the map and the path integration speed to reduce the error. The whole system is fully realized in a neuromorphic device, showing the feasibility of a purely SNN-based SLAM, which could be efficiently implemented in a small form-factor neuromorphic chip.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85448966","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197203
Martin Fevre, J. Schmiedeler
This paper employs velocity decomposition of underactuated mechanical systems to determine the degree of dynamic coupling in the gaits of a two-link biped model. The degree of coupling between controlled and uncontrolled directions quantifies the control authority the system has over its unactuated degree of freedom. This paper shows that the amount of coupling is directly correlated to gait robustness, as seen through the size of the gait’s region of attraction. The analytical measure of coupling is applied in the context of trajectory optimization to generate two-link gaits that maximize or minimize coupling. Simulation studies show that gaits maximizing coupling exhibit significantly superior robustness, as measured by 1) stochastic performance on uneven terrain, 2) ability to maintain desired walking speed under non-vanishing disturbances, 3) size of the region of attraction, and 4) robustness to model uncertainties.
{"title":"Dynamic Coupling as an Indicator of Gait Robustness for Underactuated Biped Robots","authors":"Martin Fevre, J. Schmiedeler","doi":"10.1109/ICRA40945.2020.9197203","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197203","url":null,"abstract":"This paper employs velocity decomposition of underactuated mechanical systems to determine the degree of dynamic coupling in the gaits of a two-link biped model. The degree of coupling between controlled and uncontrolled directions quantifies the control authority the system has over its unactuated degree of freedom. This paper shows that the amount of coupling is directly correlated to gait robustness, as seen through the size of the gait’s region of attraction. The analytical measure of coupling is applied in the context of trajectory optimization to generate two-link gaits that maximize or minimize coupling. Simulation studies show that gaits maximizing coupling exhibit significantly superior robustness, as measured by 1) stochastic performance on uneven terrain, 2) ability to maintain desired walking speed under non-vanishing disturbances, 3) size of the region of attraction, and 4) robustness to model uncertainties.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85516652","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196522
Y. Latif, Anh-Dzung Doan, Tat-Jun Chin, I. Reid
Visual place recognition is an important problem in mobile robotics which aims to localize a robot using image information alone. Recent methods have shown promising results for place recognition under varying environmental conditions by exploiting the sequential nature of the image acquision process. We show that by using k nearest neighbours based image retrieval as the backend, and exploiting the structure of the image acquisition process which introduces temporal relations between images in the database, the location of possible matches can be restricted to a subset of all the images seen so far. In effect, the original problem space can thus be restricted to a significantly smaller subspace, reducing the inference time significantly. This is particularly important for scalable place recognition over databases containing millions of images. We present large scale experiments using publicly sourced data that show the computational performance of the proposed method under varying environmental conditions.
{"title":"SPRINT: Subgraph Place Recognition for INtelligent Transportation","authors":"Y. Latif, Anh-Dzung Doan, Tat-Jun Chin, I. Reid","doi":"10.1109/ICRA40945.2020.9196522","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196522","url":null,"abstract":"Visual place recognition is an important problem in mobile robotics which aims to localize a robot using image information alone. Recent methods have shown promising results for place recognition under varying environmental conditions by exploiting the sequential nature of the image acquision process. We show that by using k nearest neighbours based image retrieval as the backend, and exploiting the structure of the image acquisition process which introduces temporal relations between images in the database, the location of possible matches can be restricted to a subset of all the images seen so far. In effect, the original problem space can thus be restricted to a significantly smaller subspace, reducing the inference time significantly. This is particularly important for scalable place recognition over databases containing millions of images. We present large scale experiments using publicly sourced data that show the computational performance of the proposed method under varying environmental conditions.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79627511","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197158
T. Westenbroek, David Fridovich-Keil, Eric V. Mazumdar, Shreyas Arora, Valmik Prabhu, S. Sastry, C. Tomlin
We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dynamics of a nonlinear plant linear under application of an appropriate feedback controller. Once a linearizing controller has been constructed, desired output trajectories for the nonlinear plant can be tracked using a variety of linear control techniques. However, the calculation of a linearizing controller requires a precise dynamics model for the system. As a result, model-based approaches for learning exact linearizing controllers generally require a simple, highly structured model of the system with easily identifiable parameters. In contrast, the model-free approach presented in this paper is able to approximate the linearizing controller for the plant using general function approximation architectures. Specifically, we formulate a continuous-time optimization problem over the parameters of a learned linearizing controller whose optima are the set of parameters which best linearize the plant. We derive conditions under which the learning problem is (strongly) convex and provide guarantees which ensure the true linearizing controller for the plant is recovered. We then discuss how model-free policy optimization algorithms can be used to solve a discrete-time approximation to the problem using data collected from the real-world plant. The utility of the framework is demonstrated in simulation and on a real-world robotic platform.
{"title":"Feedback Linearization for Uncertain Systems via Reinforcement Learning","authors":"T. Westenbroek, David Fridovich-Keil, Eric V. Mazumdar, Shreyas Arora, Valmik Prabhu, S. Sastry, C. Tomlin","doi":"10.1109/ICRA40945.2020.9197158","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197158","url":null,"abstract":"We present a novel approach to control design for nonlinear systems which leverages model-free policy optimization techniques to learn a linearizing controller for a physical plant with unknown dynamics. Feedback linearization is a technique from nonlinear control which renders the input-output dynamics of a nonlinear plant linear under application of an appropriate feedback controller. Once a linearizing controller has been constructed, desired output trajectories for the nonlinear plant can be tracked using a variety of linear control techniques. However, the calculation of a linearizing controller requires a precise dynamics model for the system. As a result, model-based approaches for learning exact linearizing controllers generally require a simple, highly structured model of the system with easily identifiable parameters. In contrast, the model-free approach presented in this paper is able to approximate the linearizing controller for the plant using general function approximation architectures. Specifically, we formulate a continuous-time optimization problem over the parameters of a learned linearizing controller whose optima are the set of parameters which best linearize the plant. We derive conditions under which the learning problem is (strongly) convex and provide guarantees which ensure the true linearizing controller for the plant is recovered. We then discuss how model-free policy optimization algorithms can be used to solve a discrete-time approximation to the problem using data collected from the real-world plant. The utility of the framework is demonstrated in simulation and on a real-world robotic platform.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85143310","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9197387
Garrison L. H. Johnston, A. Orekhov, N. Simaan
Collaborative robots should ideally use low torque actuators for passive safety reasons. However, some applications require these collaborative robots to reach deep into confined spaces while assisting a human operator in physically demanding tasks. In this paper, we consider the use of in-situ collaborative robots (ISCRs) that balance the conflicting demands of passive safety dictating low torque actuation and the need to reach into deep confined spaces. We consider the judicious use of bracing as a possible solution to these conflicting demands and present a modeling framework that takes into account the constrained kinematics and the effect of bracing on the endeffector compliance. We then define a redundancy resolution framework that minimizes the directional compliance of the end-effector while maximizing end-effector dexterity. Kinematic simulation results show that the redundancy resolution strategy successfully decreases compliance and improves kinematic conditioning while satisfying the constraints imposed by the bracing task. Applications of this modeling framework can support future research on the choice of bracing locations and support the formation of an admittance control framework for collaborative control of ISCRs under bracing constraints. Such robots can benefit workers in the future by reducing the physiological burdens that contribute to musculoskeletal injury.
{"title":"Kinematic Modeling and Compliance Modulation of Redundant Manipulators Under Bracing Constraints","authors":"Garrison L. H. Johnston, A. Orekhov, N. Simaan","doi":"10.1109/ICRA40945.2020.9197387","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9197387","url":null,"abstract":"Collaborative robots should ideally use low torque actuators for passive safety reasons. However, some applications require these collaborative robots to reach deep into confined spaces while assisting a human operator in physically demanding tasks. In this paper, we consider the use of in-situ collaborative robots (ISCRs) that balance the conflicting demands of passive safety dictating low torque actuation and the need to reach into deep confined spaces. We consider the judicious use of bracing as a possible solution to these conflicting demands and present a modeling framework that takes into account the constrained kinematics and the effect of bracing on the endeffector compliance. We then define a redundancy resolution framework that minimizes the directional compliance of the end-effector while maximizing end-effector dexterity. Kinematic simulation results show that the redundancy resolution strategy successfully decreases compliance and improves kinematic conditioning while satisfying the constraints imposed by the bracing task. Applications of this modeling framework can support future research on the choice of bracing locations and support the formation of an admittance control framework for collaborative control of ISCRs under bracing constraints. Such robots can benefit workers in the future by reducing the physiological burdens that contribute to musculoskeletal injury.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84011206","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 : 2020-05-01DOI: 10.1109/ICRA40945.2020.9196972
M. Görner, Fabian Benedikt, Ferdinand Grimmel, T. Hulin
SwarmRail represents a novel solution to overhead manipulation from a mobile unit that drives in an aboveground rail-structure. The concept is based on the combination of omnidirectional mobile platform and L-shaped rail profiles that form a through-going central gap. This gap makes possible mounting a robotic manipulator arm overhead at the underside of the mobile platform. Compared to existing solutions, SwarmRail enables continuous overhead manipulation while traversing rail crossings. It also can be operated in a robot swarm, as it allows for concurrent operation of a group of mobile SwarmRail units inside a single rail network. Experiments on a first functional demonstrator confirm the functional capability of the concept. Potential fields of applications reach from industry over logistics to vertical farming.
{"title":"SwarmRail: A Novel Overhead Robot System for Indoor Transport and Mobile Manipulation","authors":"M. Görner, Fabian Benedikt, Ferdinand Grimmel, T. Hulin","doi":"10.1109/ICRA40945.2020.9196972","DOIUrl":"https://doi.org/10.1109/ICRA40945.2020.9196972","url":null,"abstract":"SwarmRail represents a novel solution to overhead manipulation from a mobile unit that drives in an aboveground rail-structure. The concept is based on the combination of omnidirectional mobile platform and L-shaped rail profiles that form a through-going central gap. This gap makes possible mounting a robotic manipulator arm overhead at the underside of the mobile platform. Compared to existing solutions, SwarmRail enables continuous overhead manipulation while traversing rail crossings. It also can be operated in a robot swarm, as it allows for concurrent operation of a group of mobile SwarmRail units inside a single rail network. Experiments on a first functional demonstrator confirm the functional capability of the concept. Potential fields of applications reach from industry over logistics to vertical farming.","PeriodicalId":6859,"journal":{"name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80231793","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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