Pub Date : 2019-08-01DOI: 10.1109/COASE.2019.8842863
Amir Zakerimanesh, Ali Reza Torabi, F. Hashemzadeh, M. Tavakoli
This note presents a novel approach for task-space tracking control of redundant manipulators with bounded actuation. Inspired by the leader-follower containment problem in multi-agent systems, the proposed controller is utilized to address the containment control of a single follower manipulator led by multiple manipulators. In the controller design, the redundancy of the robots is exploited for achieving sub-task control such as singularity avoidance, and joint limit avoidance. The asymptotic stability condition for the closed-loop dynamics is obtained using Lyapunov functional. For the containment, the proposed controller makes sure that the leaders track their desired positions and the follower robot’s end-effector asymptotically converges to the convex hull formed by the leaders’ traversed trajectories. The efficiency of the proposed control algorithm is verified through numerical simulations and experimental results.
{"title":"Task-Space Position and Containment Control of Redundant Manipulators with Bounded Inputs","authors":"Amir Zakerimanesh, Ali Reza Torabi, F. Hashemzadeh, M. Tavakoli","doi":"10.1109/COASE.2019.8842863","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842863","url":null,"abstract":"This note presents a novel approach for task-space tracking control of redundant manipulators with bounded actuation. Inspired by the leader-follower containment problem in multi-agent systems, the proposed controller is utilized to address the containment control of a single follower manipulator led by multiple manipulators. In the controller design, the redundancy of the robots is exploited for achieving sub-task control such as singularity avoidance, and joint limit avoidance. The asymptotic stability condition for the closed-loop dynamics is obtained using Lyapunov functional. For the containment, the proposed controller makes sure that the leaders track their desired positions and the follower robot’s end-effector asymptotically converges to the convex hull formed by the leaders’ traversed trajectories. The efficiency of the proposed control algorithm is verified through numerical simulations and experimental results.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"4 1","pages":"431-436"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73236961","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842864
Félicien Barhebwa-Mushamuka, S. Dauzére-Pérés, C. Yugma
This paper presents a multi-objective optimization approach for global fab scheduling, based on a mathematical model that determines production targets, i.e. product quantities to complete in each operation and each period on a scheduling horizon. The multi-objective approach balances product mix variability minimization and throughput maximization using an $epsilon$-constraint approach. For evaluation purposes, the global fab scheduling model is coupled with a generic multi-method simulation model. Numerical experiments conducted on industrial data illustrate the effectiveness of the approach.
{"title":"Multi-objective optimization for Work-In-Process balancing and throughput maximization in global fab scheduling","authors":"Félicien Barhebwa-Mushamuka, S. Dauzére-Pérés, C. Yugma","doi":"10.1109/COASE.2019.8842864","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842864","url":null,"abstract":"This paper presents a multi-objective optimization approach for global fab scheduling, based on a mathematical model that determines production targets, i.e. product quantities to complete in each operation and each period on a scheduling horizon. The multi-objective approach balances product mix variability minimization and throughput maximization using an $epsilon$-constraint approach. For evaluation purposes, the global fab scheduling model is coupled with a generic multi-method simulation model. Numerical experiments conducted on industrial data illustrate the effectiveness of the approach.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"301 1","pages":"697-702"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74963274","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843092
S. Das, M. Saadatzi, Shamsudeen Abubakar, D. Popa
To detect forces during physical Human-Robot Interaction (pHRI), a force-torque sensor (FTS) is generally attached at the wrist of a robot manipulator. Alternatively, collaborative robots can measure interaction forces via torque sensing at their joints. Yet another direction toward safe and interactive robots is to cover them in smart skins with embedded tactile sensors. In this paper, we explore another idea to facilitate pHRI using an FTS placed at the base of a robot arm. The resulting base force-torque sensor (BFTS) is able to sense external forces and torques applied anywhere along the robot body. We formulate a model-free, on-line learning controller to estimate the interaction forces on the robot from the BFTS data. The controller does not require a robot dynamic model to operate, and has Lyapunov stability guarantees. We conduct experiments to validate the mean-square estimation error of our scheme using a custom 6-DOF robotic arm under real-time control. Results show that the measured torques at individual joints closely follow the estimated values. In the future, this controller can be used for adaptive pHRI with non-collaborative robots or robot manipulators.
{"title":"Joint Torque Estimation using Base Force-Torque Sensor to Facilitate Physical Human-Robot Interaction (pHRI)","authors":"S. Das, M. Saadatzi, Shamsudeen Abubakar, D. Popa","doi":"10.1109/COASE.2019.8843092","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843092","url":null,"abstract":"To detect forces during physical Human-Robot Interaction (pHRI), a force-torque sensor (FTS) is generally attached at the wrist of a robot manipulator. Alternatively, collaborative robots can measure interaction forces via torque sensing at their joints. Yet another direction toward safe and interactive robots is to cover them in smart skins with embedded tactile sensors. In this paper, we explore another idea to facilitate pHRI using an FTS placed at the base of a robot arm. The resulting base force-torque sensor (BFTS) is able to sense external forces and torques applied anywhere along the robot body. We formulate a model-free, on-line learning controller to estimate the interaction forces on the robot from the BFTS data. The controller does not require a robot dynamic model to operate, and has Lyapunov stability guarantees. We conduct experiments to validate the mean-square estimation error of our scheme using a custom 6-DOF robotic arm under real-time control. Results show that the measured torques at individual joints closely follow the estimated values. In the future, this controller can be used for adaptive pHRI with non-collaborative robots or robot manipulators.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"75 5 1","pages":"1367-1372"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77311281","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842871
V. Ortenzi, Naresh Marturi, Vijaykumar Rajasekaran, Maxime Adjigble, R. Stolkin
This paper investigates the effect of inverse kinematics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot’s singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.
{"title":"Singularity-Robust Inverse Kinematics Solver for Tele-manipulation","authors":"V. Ortenzi, Naresh Marturi, Vijaykumar Rajasekaran, Maxime Adjigble, R. Stolkin","doi":"10.1109/COASE.2019.8842871","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842871","url":null,"abstract":"This paper investigates the effect of inverse kinematics (IK) on operator performance during the telemanipulation of an industrial robot. Robotic teleoperation is often preferred when manipulating objects in extreme conditions. In many applications, e.g., hazardous and high-consequence environments, operators cannot directly perceive the robot motions and have to rely only on CCTV views of the scene for situational awareness while teleoperating the heavy-duty industrial robots. Making best guesses for the IK plays a significant role on the task success rate and increases the operator cognitive load significantly. In this context, we develop a new optimisation-based IK solver that is robust with respect to the robot’s singularities and assists the operator in generating smooth trajectories. Inspired by a successful algorithm used in computer graphics to solve the IK problem and devise smooth movements (FABRIK), our algorithm takes advantage also of the kinematic structure of the robot in order to decouple the notoriously difficult IK problem of orientation and position. To evaluate the effectiveness of the proposed method, we have compared its performance to that of the commonly used Jacobian pseudo inverse-based method in terms of positional accuracy and task-space reachability. We also report the results of telemanipulation experiments with human test-subjects. Our proposed IK algorithm outperforms classical IK methods on both objective metrics of task success, and subjective metrics of operator preference.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"88 1","pages":"1821-1828"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76389738","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843274
A. Rashidinejad, M. Reniers, Martin Fabian
In conventional supervisory control theory, a plant and supervisor are supposed to work synchronously such that enabling an event by the supervisor, execution of it in the plant, and observation of the executed event by the supervisor all occur at once. Therefore, these occurrences are all captured by means of a single event. However, when a supervisor synthesized from conventional supervisory control theory is implemented in real life, it will face problems since exact synchronization can hardly happen in practice due to delayed communications. In this paper, we propose a synthesis technique to achieve a supervisor that does not face the problems caused by inexact synchronization. For this purpose, we first introduce an asynchronous setting in which enablement, execution, and observation of an event do not occur simultaneously but with some delay. We present a model representing the behavior of the plant in the asynchronous setting which we call the asynchronous plant. For the asynchronous plant, we present an algorithm synthesizing an asynchronous supervisor which satisfies (asynchronous) controllability and nonblockingness.
{"title":"Supervisory Control of Discrete-Event Systems in an Asynchronous Setting","authors":"A. Rashidinejad, M. Reniers, Martin Fabian","doi":"10.1109/COASE.2019.8843274","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843274","url":null,"abstract":"In conventional supervisory control theory, a plant and supervisor are supposed to work synchronously such that enabling an event by the supervisor, execution of it in the plant, and observation of the executed event by the supervisor all occur at once. Therefore, these occurrences are all captured by means of a single event. However, when a supervisor synthesized from conventional supervisory control theory is implemented in real life, it will face problems since exact synchronization can hardly happen in practice due to delayed communications. In this paper, we propose a synthesis technique to achieve a supervisor that does not face the problems caused by inexact synchronization. For this purpose, we first introduce an asynchronous setting in which enablement, execution, and observation of an event do not occur simultaneously but with some delay. We present a model representing the behavior of the plant in the asynchronous setting which we call the asynchronous plant. For the asynchronous plant, we present an algorithm synthesizing an asynchronous supervisor which satisfies (asynchronous) controllability and nonblockingness.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"34 1","pages":"494-501"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76274890","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842852
M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri, S. Digiesi
This paper deals with the electric vehicle fleet re-location management in a sharing system. The mobility sharing systems efficiency depends on the vehicles relocation task that strongly affect the company operating cost, and consequently the service price for users. The proposed approach aims at minimizing the cost of vehicles relocation for a sharing company by involving users through an innovative incentive scheme. The idea is to request users of the sharing service to relocate the EVs, e.g. through an IT application, incentivizing them by free-of-charge travels and rewards. The proposed incentive mechanism is based on the application of different levels of incentive proposal. In addition, in case of user unavailability, the vehicle relocation is guaranteed by the company staff. To this aim, a first ILP is formalized to manage the relocation task by the company staff. Moreover, a second ILP allows the company to involve users in the relocation process by the proposed incentive mechanism. Finally, a case study is presented to show the application of the proposed methodology on the relocation of electric cars and light electric vehicles.
{"title":"Electric Vehicle Fleet Relocation Management for Sharing Systems based on Incentive Mechanism","authors":"M. P. Fanti, A. M. Mangini, M. Roccotelli, B. Silvestri, S. Digiesi","doi":"10.1109/COASE.2019.8842852","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842852","url":null,"abstract":"This paper deals with the electric vehicle fleet re-location management in a sharing system. The mobility sharing systems efficiency depends on the vehicles relocation task that strongly affect the company operating cost, and consequently the service price for users. The proposed approach aims at minimizing the cost of vehicles relocation for a sharing company by involving users through an innovative incentive scheme. The idea is to request users of the sharing service to relocate the EVs, e.g. through an IT application, incentivizing them by free-of-charge travels and rewards. The proposed incentive mechanism is based on the application of different levels of incentive proposal. In addition, in case of user unavailability, the vehicle relocation is guaranteed by the company staff. To this aim, a first ILP is formalized to manage the relocation task by the company staff. Moreover, a second ILP allows the company to involve users in the relocation process by the proposed incentive mechanism. Finally, a case study is presented to show the application of the proposed methodology on the relocation of electric cars and light electric vehicles.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"114 1","pages":"1048-1053"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77718626","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 : 2019-08-01DOI: 10.1109/COASE.2019.8842982
Z. Zabinsky, Ting-Yu Ho, Hao Huang
Algorithms for solving large-scale optimization problems often use heuristics and approximations to achieve a solution quickly, however there is often little or no information as to the quality of the solution. We integrate heuristics and approximations into a branch and bound framework to take advantage of obtaining a solution quickly, while using the framework to prune regions that do not contain an optimal solution, and provide an optimality gap. Three examples are cast into this framework. First, we describe a Rollout Algorithm with Branch-and-Bound (RA-BnB) that embeds an approximate dynamic program into a branch and bound framework to address a challenging resource allocation problem in population disease management. Second, we describe a Vehicle Routing and Scheduling Algorithm (VeRSA) that embeds an easily calculated index, as is commonly used in scheduling, to dynamically search and prune a branch and bound tree. Third, we describe a Probabilistic Branch and Bound algorithm (PBnB) that uses a statistical sampling method to obtain confidence interval bounds that are embedded into a tree to probabilistically prune regions of the tree. These three, apparently different, methods share commonalities that make use of heuristics and approximations to generate a “near-optimal” solution quickly, and also provide information on the quality of the solution by providing an optimality gap. Lessons learned on implementation decisions and how to balance computation in the context of these three problems are discussed.
{"title":"Integrating Heuristics and Approximations into a Branch and Bound Framework*","authors":"Z. Zabinsky, Ting-Yu Ho, Hao Huang","doi":"10.1109/COASE.2019.8842982","DOIUrl":"https://doi.org/10.1109/COASE.2019.8842982","url":null,"abstract":"Algorithms for solving large-scale optimization problems often use heuristics and approximations to achieve a solution quickly, however there is often little or no information as to the quality of the solution. We integrate heuristics and approximations into a branch and bound framework to take advantage of obtaining a solution quickly, while using the framework to prune regions that do not contain an optimal solution, and provide an optimality gap. Three examples are cast into this framework. First, we describe a Rollout Algorithm with Branch-and-Bound (RA-BnB) that embeds an approximate dynamic program into a branch and bound framework to address a challenging resource allocation problem in population disease management. Second, we describe a Vehicle Routing and Scheduling Algorithm (VeRSA) that embeds an easily calculated index, as is commonly used in scheduling, to dynamically search and prune a branch and bound tree. Third, we describe a Probabilistic Branch and Bound algorithm (PBnB) that uses a statistical sampling method to obtain confidence interval bounds that are embedded into a tree to probabilistically prune regions of the tree. These three, apparently different, methods share commonalities that make use of heuristics and approximations to generate a “near-optimal” solution quickly, and also provide information on the quality of the solution by providing an optimality gap. Lessons learned on implementation decisions and how to balance computation in the context of these three problems are discussed.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"8 1","pages":"774-779"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75895179","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843291
Zhuo Yang, Yan Lu, H. Yeung, S. Krishnamurty
Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF) additive manufacturing process. Melt pool size and shape reflect the impact of process parameters and scanning path on the interaction between the laser and the powder material, the phase change and the flow dynamics of the material during the process. Current L-PBF processes are operated based on predetermined toolpaths and processing parameters and consequently lack the ability to make reactions to unexpected melt pool changes. This paper investigated how melt pool can be characterized in real-time for feedback control. A deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately. The classifier, based on a convolutional neural network, was trained with 2763 melt pool images captured from a laser melting powder fusion build using a serpentine scan strategy. The model is validated through 2926 new images collected from a different part in the same build using ‘island’ serpentine strategy with predictive accuracy of 91%. Compared to a traditional image analysis method, the processing time of the validation images is reduced by 90 %, from 9.72 s to 0.99 s, which gives the feedback control a reaction time window of 0.34 ms/image. Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process.
{"title":"Investigation of Deep Learning for Real-Time Melt Pool Classification in Additive Manufacturing","authors":"Zhuo Yang, Yan Lu, H. Yeung, S. Krishnamurty","doi":"10.1109/COASE.2019.8843291","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843291","url":null,"abstract":"Consistent melt pool geometry is an indicator of a stable laser powder bed fusion (L-PBF) additive manufacturing process. Melt pool size and shape reflect the impact of process parameters and scanning path on the interaction between the laser and the powder material, the phase change and the flow dynamics of the material during the process. Current L-PBF processes are operated based on predetermined toolpaths and processing parameters and consequently lack the ability to make reactions to unexpected melt pool changes. This paper investigated how melt pool can be characterized in real-time for feedback control. A deep learning-based melt pool classification method is developed to analyze melt pool size both fast and accurately. The classifier, based on a convolutional neural network, was trained with 2763 melt pool images captured from a laser melting powder fusion build using a serpentine scan strategy. The model is validated through 2926 new images collected from a different part in the same build using ‘island’ serpentine strategy with predictive accuracy of 91%. Compared to a traditional image analysis method, the processing time of the validation images is reduced by 90 %, from 9.72 s to 0.99 s, which gives the feedback control a reaction time window of 0.34 ms/image. Results show the feasibility of the proposed method for a real-time closed loop control of L-PBF process.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"97 1","pages":"640-647"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76736842","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843128
Sven Tittel, M. Malekzadeh, Jochen J. Steil
Human robot interaction (HRI) is a major research field in robotics with significant progress over the last decades. While most HRI is focused on novel light weight robots, we here present an admittance control implementation for the 6-DOF industrial robot Stäubli TX60. We use only standard and commercially available interfaces, without adding external force sensing, and present a method to estimate joint friction to improve the robot model. In contrast to most previous works, all six joints are controlled simultaneously to realize a handguided motion of the whole robot. To this aim, we present a modular control framework that allows for seamlessly switching between simulated and real hardware.
{"title":"Full 6-DOF Admittance Control for the Industrial Robot Stäubli TX60","authors":"Sven Tittel, M. Malekzadeh, Jochen J. Steil","doi":"10.1109/COASE.2019.8843128","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843128","url":null,"abstract":"Human robot interaction (HRI) is a major research field in robotics with significant progress over the last decades. While most HRI is focused on novel light weight robots, we here present an admittance control implementation for the 6-DOF industrial robot Stäubli TX60. We use only standard and commercially available interfaces, without adding external force sensing, and present a method to estimate joint friction to improve the robot model. In contrast to most previous works, all six joints are controlled simultaneously to realize a handguided motion of the whole robot. To this aim, we present a modular control framework that allows for seamlessly switching between simulated and real hardware.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"102 1","pages":"1450-1455"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80583246","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 : 2019-08-01DOI: 10.1109/COASE.2019.8843299
Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam
Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.
{"title":"Data-Driven Surgical Duration Prediction Model for Surgery Scheduling: A Case-Study for a Practice-Feasible Model in a Public Hospital","authors":"Kar Way Tan, Francis Ngoc Hoang Long Nguyen, Boon Yew Ang, Jerald Gan, Sean Shao Wei Lam","doi":"10.1109/COASE.2019.8843299","DOIUrl":"https://doi.org/10.1109/COASE.2019.8843299","url":null,"abstract":"Hospitals have been trying to improve the utilization of operating rooms as it affects patient satisfaction, surgery throughput, revenues and costs. Surgical prediction model which uses post-surgery data often requires high-dimensional data and contains key predictors such as surgical team factors which may not be available during the surgical listing process. Our study considers a two-step data-mining model which provides a practical, feasible and parsimonious surgical duration prediction. Our model first leverages on domain knowledge to provide estimate of the first surgeon rank (a key predicting attribute) which is unavailable during the listing process, then uses this predicted attribute and other predictors such as surgical team, patient, temporal and operational factors in a tree-based model for predicting surgical durations. Experimental results show that the proposed two-step model is more parsimonious and outperforms existing moving averages method used by the hospital. Our model bridges the research-to-practice gap by combining data analytics with expert’s inputs to develop a deployable surgical duration prediction model for a real-world public hospital.","PeriodicalId":6695,"journal":{"name":"2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)","volume":"15 1","pages":"275-280"},"PeriodicalIF":0.0,"publicationDate":"2019-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81545791","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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