Pub Date : 2021-08-23DOI: 10.1109/CASE49439.2021.9551394
Merrill Edmonds, Tarik Yigit, J. Yi
Precision agriculture relies on large-scale visual inspections for accurate crop monitoring and yield maximization. For many farms, the scales of production preclude manual inspections, and it is therefore desirable for larger producers to employ unmanned ground and aerial vehicles (UGV/UAV) to automate the necessary proximal and remote sensing tasks, respectively. This paper presents a new problem formulation for cooperative crop inspection missions under fuel and pathing constraints. We propose an a priori optimization method that leverages knowledge of the energy constraints and plot topology to determine resolution-optimal walks on a graph representing the union of reachable sets for each robot. We show that approximating the reachable sets guarantees energy efficiency. We further show that UGV-UAV interactions such as sethopping can increase the effective continuous monitoring range. Simulation studies show that our method accounts for charge-recharge cycles that are typical of long inspection missions, while also optimizing capture time and sensing resolution.
{"title":"Resolution-Optimal, Energy-Constrained Mission Planning for Unmanned Aerial/Ground Crop Inspections","authors":"Merrill Edmonds, Tarik Yigit, J. Yi","doi":"10.1109/CASE49439.2021.9551394","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551394","url":null,"abstract":"Precision agriculture relies on large-scale visual inspections for accurate crop monitoring and yield maximization. For many farms, the scales of production preclude manual inspections, and it is therefore desirable for larger producers to employ unmanned ground and aerial vehicles (UGV/UAV) to automate the necessary proximal and remote sensing tasks, respectively. This paper presents a new problem formulation for cooperative crop inspection missions under fuel and pathing constraints. We propose an a priori optimization method that leverages knowledge of the energy constraints and plot topology to determine resolution-optimal walks on a graph representing the union of reachable sets for each robot. We show that approximating the reachable sets guarantees energy efficiency. We further show that UGV-UAV interactions such as sethopping can increase the effective continuous monitoring range. Simulation studies show that our method accounts for charge-recharge cycles that are typical of long inspection missions, while also optimizing capture time and sensing resolution.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114349884","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551666
Oussama Hayane, D. Lefebvre
This paper aims to develop a new method to determine a robust scheduling control for systems evolving in uncertain environments. Time Petri Nets with controllable and uncontrollable transitions are used to model the system. The controllable transitions represent the operations and the uncontrollable transitions represent unexpected events that correspond either to interruption of operations or to unavailability of resources. The developed method computes reconfigurable control sequences based on the determination of series of timed extended reachability graphs (R-TERG). Once an unexpected event is detected, a reconfiguration point is created and the R-TERG is updated. Successive applications of the Dijkstra algorithm allow reconfiguring the control sequence in order to preserve optimality with respect to the faults that affect the system.
{"title":"Reconfigurable Timed Extended Reachability Graphs for scheduling problems in uncertain environments*","authors":"Oussama Hayane, D. Lefebvre","doi":"10.1109/CASE49439.2021.9551666","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551666","url":null,"abstract":"This paper aims to develop a new method to determine a robust scheduling control for systems evolving in uncertain environments. Time Petri Nets with controllable and uncontrollable transitions are used to model the system. The controllable transitions represent the operations and the uncontrollable transitions represent unexpected events that correspond either to interruption of operations or to unavailability of resources. The developed method computes reconfigurable control sequences based on the determination of series of timed extended reachability graphs (R-TERG). Once an unexpected event is detected, a reconfiguration point is created and the R-TERG is updated. Successive applications of the Dijkstra algorithm allow reconfiguring the control sequence in order to preserve optimality with respect to the faults that affect the system.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114719390","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551407
Jakub Sláma, Petr Váňa, J. Faigl
In-flight aircraft failures are never avoidable entirely, inducing a significant risk to people and properties on the ground in an urban environment. Existing risk-aware trajectory planning approaches minimize the risk by determining trajectories that might result in less damage in the case of failure. However, the risk of the loss of thrust can be eliminated by executing a safe emergency landing if a landing site is reachable. Therefore, we propose a novel risk-aware trajectory planning that minimizes the risk to people on the ground while an option of a safe emergency landing in the case of loss of thrust is guaranteed. The proposed method has been empirically evaluated on a realistic urban scenario. Based on the reported results, an improvement in the risk reduction is achieved compared to the shortest and risk-aware only trajectory. The proposed risk-aware planning with safe emergency landing seems to be suitable trajectory planning for urban air mobility.
{"title":"Risk-aware Trajectory Planning in Urban Environments with Safe Emergency Landing Guarantee","authors":"Jakub Sláma, Petr Váňa, J. Faigl","doi":"10.1109/CASE49439.2021.9551407","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551407","url":null,"abstract":"In-flight aircraft failures are never avoidable entirely, inducing a significant risk to people and properties on the ground in an urban environment. Existing risk-aware trajectory planning approaches minimize the risk by determining trajectories that might result in less damage in the case of failure. However, the risk of the loss of thrust can be eliminated by executing a safe emergency landing if a landing site is reachable. Therefore, we propose a novel risk-aware trajectory planning that minimizes the risk to people on the ground while an option of a safe emergency landing in the case of loss of thrust is guaranteed. The proposed method has been empirically evaluated on a realistic urban scenario. Based on the reported results, an improvement in the risk reduction is achieved compared to the shortest and risk-aware only trajectory. The proposed risk-aware planning with safe emergency landing seems to be suitable trajectory planning for urban air mobility.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126351446","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551568
H. Nehme, Clément Aubry, R. Rossi, R. Boutteau
Local perception navigation methods allow agricultural robots to accurately track crop row structures while performing automated farming tasks. The integration of these methods as a part of a fully autonomous navigation solution requires continuous assessment of their reliability since they rely solely on sensor data in a changing and unpredictable environment. This paper presents a data-driven monitoring approach for the task of structure-based navigation in agriculture. The proposed method applies semi-supervised anomaly detection, aiming to learn a model of normal scene geometry that characterizes a domain of reliable execution of the considered task. To this end, a convolutional neural network was trained in one-class classification fashion on Hough representations of LiDAR point clouds. In experimentation, the learned normal model was used to derive a confidence measure for a LiDAR-based tracking algorithm allowing its integration as a part of a hybrid navigation solution in vineyards for a commercial robotic platform.
{"title":"An Anomaly Detection Approach to Monitor the Structured-Based Navigation in Agricultural Robotics","authors":"H. Nehme, Clément Aubry, R. Rossi, R. Boutteau","doi":"10.1109/CASE49439.2021.9551568","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551568","url":null,"abstract":"Local perception navigation methods allow agricultural robots to accurately track crop row structures while performing automated farming tasks. The integration of these methods as a part of a fully autonomous navigation solution requires continuous assessment of their reliability since they rely solely on sensor data in a changing and unpredictable environment. This paper presents a data-driven monitoring approach for the task of structure-based navigation in agriculture. The proposed method applies semi-supervised anomaly detection, aiming to learn a model of normal scene geometry that characterizes a domain of reliable execution of the considered task. To this end, a convolutional neural network was trained in one-class classification fashion on Hough representations of LiDAR point clouds. In experimentation, the learned normal model was used to derive a confidence measure for a LiDAR-based tracking algorithm allowing its integration as a part of a hybrid navigation solution in vineyards for a commercial robotic platform.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"85 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126428343","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551523
Chengyuan Liu, Mingfeng Wang, Xuefang Li, S. Ratchev
This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78° to 1.09°, and 21.09% to 3.99%, respectively, within three iterations.
{"title":"Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator","authors":"Chengyuan Liu, Mingfeng Wang, Xuefang Li, S. Ratchev","doi":"10.1109/CASE49439.2021.9551523","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551523","url":null,"abstract":"This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunov-like composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78° to 1.09°, and 21.09% to 3.99%, respectively, within three iterations.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"2012 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128005367","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551455
Takayuki Murooka, Masashi Hamaya, Felix von Drigalski, Kazutoshi Tanaka, Yoshihisa Ijiri
Disturbance Observer (DOB) has been widely used for robotic applications to eliminate various kinds of disturbances. Recently, learning-based DOB has attracted significant attention as it can deal with complex robotic systems. In this study, we propose the Iterative Backpropagation Disturbance Observer (IB-DOB) method. IB-DOB learns the forward model with a neural network, and calculates disturbances via iterative backpropagations, which behaves like the inverse model. Our method can not only improve estimation performances owing to the iterative calculation but also be applied to both model-free and -based learning control. We conducted experiments for two manipulation tasks: the cart pole with Deep Deterministic Policy Gradient (DDPG) and the pushing object task with Deep Model Predictive Control (DeepMPC). Our method demonstrated better task performances than the baselines without DOB and with DOB using a learned inverse model even though disturbances of external forces and model errors were provided.
{"title":"Iterative Backpropagation Disturbance Observer with Forward Dynamics Model","authors":"Takayuki Murooka, Masashi Hamaya, Felix von Drigalski, Kazutoshi Tanaka, Yoshihisa Ijiri","doi":"10.1109/CASE49439.2021.9551455","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551455","url":null,"abstract":"Disturbance Observer (DOB) has been widely used for robotic applications to eliminate various kinds of disturbances. Recently, learning-based DOB has attracted significant attention as it can deal with complex robotic systems. In this study, we propose the Iterative Backpropagation Disturbance Observer (IB-DOB) method. IB-DOB learns the forward model with a neural network, and calculates disturbances via iterative backpropagations, which behaves like the inverse model. Our method can not only improve estimation performances owing to the iterative calculation but also be applied to both model-free and -based learning control. We conducted experiments for two manipulation tasks: the cart pole with Deep Deterministic Policy Gradient (DDPG) and the pushing object task with Deep Model Predictive Control (DeepMPC). Our method demonstrated better task performances than the baselines without DOB and with DOB using a learned inverse model even though disturbances of external forces and model errors were provided.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125697407","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551645
James Akl, Fadi M. Alladkani, B. Çalli
We propose a novel framework for robotic metal scrap cutting in unstructured scrap yards. In this framework the robots and workers collaborate: the worker marks the cutting locations on the scrap metal with spray paint and the robot then generates the cutting trajectories. This leverages worker expertise, while deferring the dull, dirty, dangerous aspects to the robot. For the robot, this requires a 3-D exploration and curve reconstruction stage for path generation. We use a non-uniform rational basis spline (NURBS) model and a topological skeletonization method for path generation, and implement and compare these methods via simulations. These simulations employ a realistic sensor noise model and highly-detailed 3-D scans of complex, real-life scrap pieces. Real-robot experiments with three different shapes are also provided.
{"title":"Towards Robotic Metal Scrap Cutting: A Novel Workflow and Pipeline for Cutting Path Generation","authors":"James Akl, Fadi M. Alladkani, B. Çalli","doi":"10.1109/CASE49439.2021.9551645","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551645","url":null,"abstract":"We propose a novel framework for robotic metal scrap cutting in unstructured scrap yards. In this framework the robots and workers collaborate: the worker marks the cutting locations on the scrap metal with spray paint and the robot then generates the cutting trajectories. This leverages worker expertise, while deferring the dull, dirty, dangerous aspects to the robot. For the robot, this requires a 3-D exploration and curve reconstruction stage for path generation. We use a non-uniform rational basis spline (NURBS) model and a topological skeletonization method for path generation, and implement and compare these methods via simulations. These simulations employ a realistic sensor noise model and highly-detailed 3-D scans of complex, real-life scrap pieces. Real-robot experiments with three different shapes are also provided.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131558183","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551448
Marisa M. Gioioso, Akshay Kurmi
The operation of a mass spectrometry instrument, used in analytical chemistry, for custom applications requires the careful tuning of several instrument settings by an expert. In this work, we developed a model that allows the instrument to tune itself. The approach employs a fast, adaptive evolutionary algorithm, the Covariance Matrix Adaptation evolutionary strategy, to tune a mass spectrometry instrument. By developing a scheme for normalizing the values of the outcome variables (resolution, intensity and peak shape of a calibrant peak signal) based on their experimental probability distributions, we combined the outcomes into a single score that was used as the fitness score for the search algorithm. This approach resulted in a more thorough examination of the search space, and in an economical amount of time by being adaptive, resulting in a more stable tuning, no matter the initial state of the settings involved.
{"title":"Covariance matrix adaptation based tuning of mass spectrometry parameters using experimental probability distributions","authors":"Marisa M. Gioioso, Akshay Kurmi","doi":"10.1109/CASE49439.2021.9551448","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551448","url":null,"abstract":"The operation of a mass spectrometry instrument, used in analytical chemistry, for custom applications requires the careful tuning of several instrument settings by an expert. In this work, we developed a model that allows the instrument to tune itself. The approach employs a fast, adaptive evolutionary algorithm, the Covariance Matrix Adaptation evolutionary strategy, to tune a mass spectrometry instrument. By developing a scheme for normalizing the values of the outcome variables (resolution, intensity and peak shape of a calibrant peak signal) based on their experimental probability distributions, we combined the outcomes into a single score that was used as the fitness score for the search algorithm. This approach resulted in a more thorough examination of the search space, and in an economical amount of time by being adaptive, resulting in a more stable tuning, no matter the initial state of the settings involved.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130847848","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551481
Yandong Ji, Bike Zhang, K. Sreenath
Motivated towards performing missions in unstructured environments using a group of robots, this paper presents a reinforcement learning-based strategy for multiple quadrupedal robots executing collaborative manipulation tasks. By taking target position, velocity tracking, and height adjustment into account, we demonstrate that the proposed strategy enables four quadrupedal robots manipulating a payload to walk at desired linear and angular velocities, as well as over challenging terrain. The learned policy is robust to variations of payload mass and can be parameterized by different commanded velocities. (Video11https://youtu.be/i8kZSYdi9Nk)
{"title":"Reinforcement Learning for Collaborative Quadrupedal Manipulation of a Payload over Challenging Terrain","authors":"Yandong Ji, Bike Zhang, K. Sreenath","doi":"10.1109/CASE49439.2021.9551481","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551481","url":null,"abstract":"Motivated towards performing missions in unstructured environments using a group of robots, this paper presents a reinforcement learning-based strategy for multiple quadrupedal robots executing collaborative manipulation tasks. By taking target position, velocity tracking, and height adjustment into account, we demonstrate that the proposed strategy enables four quadrupedal robots manipulating a payload to walk at desired linear and angular velocities, as well as over challenging terrain. The learned policy is robust to variations of payload mass and can be parameterized by different commanded velocities. (Video11https://youtu.be/i8kZSYdi9Nk)","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133643448","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 : 2021-08-23DOI: 10.1109/CASE49439.2021.9551534
Amit Prigozin, A. Degani
As part of automation processes, robotic manipulators are occasionally required to assemble deformable objects, e.g., installing an O-ring into a groove. However, deformable objects are characterized by high uncertainty due to shape and length change under external forces. These uncertainties make the assembly process complex and slow and may lead to errors between the actual and desired gripping location. In this paper, we present a localization technique to estimate the actual gripping point by using the grid localization algorithm based on tactile sensing. To reduce the dependency on complex and relatively slow vision sensors, the pose estimation process is based only on tactile feedback, by recognizing features, e.g., corners, along the deformable object. In simulations and experiments, the proposed algorithm converged to the correct gripping point after three detected features with an accuracy of less than 1 mm.
{"title":"Tactile-Based Gripper Localization on 1-D Deformable Objects","authors":"Amit Prigozin, A. Degani","doi":"10.1109/CASE49439.2021.9551534","DOIUrl":"https://doi.org/10.1109/CASE49439.2021.9551534","url":null,"abstract":"As part of automation processes, robotic manipulators are occasionally required to assemble deformable objects, e.g., installing an O-ring into a groove. However, deformable objects are characterized by high uncertainty due to shape and length change under external forces. These uncertainties make the assembly process complex and slow and may lead to errors between the actual and desired gripping location. In this paper, we present a localization technique to estimate the actual gripping point by using the grid localization algorithm based on tactile sensing. To reduce the dependency on complex and relatively slow vision sensors, the pose estimation process is based only on tactile feedback, by recognizing features, e.g., corners, along the deformable object. In simulations and experiments, the proposed algorithm converged to the correct gripping point after three detected features with an accuracy of less than 1 mm.","PeriodicalId":232083,"journal":{"name":"2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132045007","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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