2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)最新文献_第2页

Integrated Inventory Placement and Transportation Vehicle Selection using Neural Network 基于神经网络的综合库存配置与运输车辆选择

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926536

Junyan Qiu, Jun Xia, Jun Luo, Y. Liu, Yuxin Liu

{"title":"Integrated Inventory Placement and Transportation Vehicle Selection using Neural Network","authors":"Junyan Qiu, Jun Xia, Jun Luo, Y. Liu, Yuxin Liu","doi":"10.1109/CASE49997.2022.9926536","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926536","url":null,"abstract":"In this work, we investigate an integrated optimization problem of inventory placement and transportation vehicle selection in a logistics system with multiple central distribution centers and multiple regional distribution centers. The main decision in our problem refers to the selection of transportation vehicles, concerning the trade-offs among different types of costs in the system, such as the vehicle selection cost, commodity transportation cost and inventory holding cost. We formulate the problem as a nonconvex mixed-integer quadratically constrained program. Due to the nonconvexity of the objective function which makes the model difficult to solve, we establish a convex approximation on the proposed formulation using Cauthy inequalities. An efficient two-phase solution framework, combining neural network prediction and branch-and-bound search, is developed to solve the approximate model. Computational results demonstrate that using a neural network is effective in predicting values of a subset of integer variables in solution, which can be subsequently extended to form a high-quality solution to the integrated optimization. Moreover, the two-phase method has a significant advantage in solving speed over the pure implementation of branch-and-bound method, which suggests its strength in solving larger mixed-integer programs.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"28 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114464754","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}

引用次数: 0

MultiROS: ROS-Based Robot Simulation Environment for Concurrent Deep Reinforcement Learning MultiROS:基于ros的并发深度强化学习机器人仿真环境

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926475

Jayasekara Kapukotuwa, Brian Lee, D. Devine, Yuansong Qiao

{"title":"MultiROS: ROS-Based Robot Simulation Environment for Concurrent Deep Reinforcement Learning","authors":"Jayasekara Kapukotuwa, Brian Lee, D. Devine, Yuansong Qiao","doi":"10.1109/CASE49997.2022.9926475","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926475","url":null,"abstract":"On the journey of true autonomous robotics, applying deep reinforcement learning (DRL) techniques to solve complex robotics tasks has been a growing interest in academics and the industry. Currently, numerous simulation frameworks exist for evaluating DRL algorithms with robots, and they usually come with prebuilt tasks or provide tools to create custom environments. Among these, one of the highly sought approaches is using Robot Operating System (ROS) based DRL frameworks for simulation and deployment in the real world. The current ROS-based DRL simulation frameworks like openai_ros or Gym-gazebo provide a framework for creating environments; however, they do not support training with vectorised environments for speeding up the training process and parallel simulations for testing and evaluating meta-learning, multi-task learning and transfer learning approaches. Therefore, we present MultiROS, a 3D robotic simulation framework with a collection of prebuilt environments for deep reinforcement learning (DRL) research to overcome these limitations. This package interfaces with the Gazebo robotic simulator using ROS and provides a modular structure to create ROS-based RL environments. Unlike the others, MultiROS provides support for training with multiple environments in parallel and simultaneously accessing data from each simulation. Furthermore, since MultiROS uses the popular OpenAI Gym interface, it is compatible with most OpenAI Gym based reinforcement learning algorithms that use third-party python frameworks.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128622330","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}

引用次数: 0

Rendezvous Scheduling for Charging Coordination Between Aerial Robot - Mobile Ground Robot 空中机器人与地面移动机器人充电协调的交会调度

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926705

A. Eker, Ahmet Öncü, H. I. Bozma

{"title":"Rendezvous Scheduling for Charging Coordination Between Aerial Robot - Mobile Ground Robot","authors":"A. Eker, Ahmet Öncü, H. I. Bozma","doi":"10.1109/CASE49997.2022.9926705","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926705","url":null,"abstract":"This paper studies the problem of rendezvous scheduling between an energy-limited aerial robot (AR) and a mobile ground robot (MGR). If the AR cannot complete its mission in a single sortie, it has to recharge at scheduled locations and times with the MGR acting as a mobile charging station. Differing from previous work, the visit order of the waypoints is assumed to be determined a priori using one of the available algorithms for pathfinding or area coverage. We consider two alternative cases depending on whether the AR can land prior to the arrival of the MGR or it has to hover in the air and wait for its arrival. Our approach to each is motivated by the principle of optimality - namely the corresponding constrained optimization problem is decomposed into smaller problems whose solutions are then integrated together. Their solutions are found from a binary search tree of charge selections that evolves iteratively using a tree sort algorithm. The advantage of this approach is that as the focus is on the scheduling aspect, the algorithm becomes relatively more tractable and realistically applicable. Our simulation results demonstrate that the optimal rendezvous schedule can be determined in realizable time even for large-scale missions.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130549177","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}

引用次数: 0

Selecting Objects on Conveyor Belts Using Pointing Gestures Sensed by a Wrist-worn Inertial Measurement Unit 使用腕带惯性测量单元感知的指向手势选择传送带上的对象

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926448

Gabriele Abbate, A. Giusti, Antonio Paolillo, L. Gambardella, A. Rizzoli, Jérôme Guzzi

引用次数: 0

A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning 基于mip的多机器人几何任务运动规划方法

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926661

Hejia Zhang, Shao-Hung Chan, Jie Zhong, Jiaoyang Li, Sven Koenig, S. Nikolaidis

{"title":"A MIP-Based Approach for Multi-Robot Geometric Task-and-Motion Planning","authors":"Hejia Zhang, Shao-Hung Chan, Jie Zhong, Jiaoyang Li, Sven Koenig, S. Nikolaidis","doi":"10.1109/CASE49997.2022.9926661","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926661","url":null,"abstract":"We address multi-robot geometric task-and-motion planning (MR-GTAMP) problems in synchronous, monotone setups. The goal of the MR-GTAMP problem is to move objects with multiple robots to goal regions in the presence of other movable objects. To perform the tasks successfully and effectively, the robots have to adopt intelligent collaboration strategies, i.e., decide which robot should move which objects to which positions, and perform collaborative actions, such as handovers. To endow robots with these collaboration capabilities, we propose to first collect occlusion and reachability information for each robot as well as information about whether two robots can perform a handover action by calling motion-planning algorithms. We then propose a method that uses the collected information to build a graph structure which captures the precedence of the manipulations of different objects and supports the implementation of a mixed-integer program to guide the search for highly effective collaborative task-and-motion plans. The search process for collaborative task-and-motion plans is based on a Monte-Carlo Tree Search (MCTS) exploration strategy to achieve exploration-exploitation balance. We evaluate our framework in two challenging GTAMP domains and show that it can generate high-quality task-and-motion plans with respect to the planning time, the resulting plan length and the number of objects moved compared to two state-of-the-art baselines.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114230023","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}

引用次数: 4

Robotic Fabric Fusing using a Novel Electroadhesion Gripper 一种新型电粘接夹具用于机器人织物融合

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926477

Honglu He, Glenn Saunders, J. Wen

{"title":"Robotic Fabric Fusing using a Novel Electroadhesion Gripper","authors":"Honglu He, Glenn Saunders, J. Wen","doi":"10.1109/CASE49997.2022.9926477","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926477","url":null,"abstract":"Automation has been playing a major role in manufacturing such as in automotive, electronics, and pharmaceutical industries. While robots are able to perform repeatable tasks with speed and accuracy, its use in garment industry has been limited by challenges in grasping and handling soft fabrics using commercially available robot end effectors. This paper considers a common garment manufacturing process, fabric fusing, which combines a piece of woven or knitted fabric with an interface material (interlining) to provide additional firmness and support. Current practice uses human operators to perform fabric pick-up, alignment, and manipulation tasks to feed the combined materials into a fusing machine. We have developed a robotic system with an electroadhesion robot gripper containing actuated pins to alleviate human operators from performing these repeated and laborious tasks in an uncomfortable manufacturing environment. This robotic system can reliably pick-up fabric pieces, place them without wrinkles, align them using machine vision, and feed the combined bundle through the conveyor belt into the fusing machine. The actuated pins interlaced with the electrodes on the gripper can detach the fabric from the gripper with residual charges and to slide the bundle onto the conveyor belt without affecting the fabric alignment. The electroadhesion force depends on the applied voltage, fabric material property, and humidity. The environmental condition needs to be controlled and the applied voltage adjusted based on the type of fabric materials and humidity to achieve reliable performance. The prototype system has been demonstrated in both the laboratory setting and actual garment manufacturing shop floor.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125739243","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}

引用次数: 4

Causality-based Prediction Method for the Diesel Engine Assembly Line System* 柴油机装配线系统的因果关系预测方法*

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926702

Jingjing Hu, Yanning Sun, Hongwei Xu, Zhanhong Zhang, Wei Qin, Xinyu Li

{"title":"Causality-based Prediction Method for the Diesel Engine Assembly Line System*","authors":"Jingjing Hu, Yanning Sun, Hongwei Xu, Zhanhong Zhang, Wei Qin, Xinyu Li","doi":"10.1109/CASE49997.2022.9926702","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926702","url":null,"abstract":"The prediction of diesel engine power is a vital prerequisite for diesel engine quality promotion. A key issue of diesel engine power prediction is the selection of representative features for forecasting. However, current feature selection methods mainly rely on correlation analysis which cannot distinguish between direct correlation and indirect correlation. This paper presents a causal feature selection method for diesel engine power forecasting. Causalities distinguish direct influences from indirect ones. Therefore, this paper proposes a diesel engine power prediction framework based on using Markov Blanket-based feature selection approach and Gradient Boosting Decision Tree (GBDT) forecasting model. The proposed framework first applies Markov Blanket to identify causalities between manufacturing variables and diesel engine power and generates a causal feature set. Then, the quantitative relationship between causal features and the diesel engine power is established through GBDT. Finally, the proposed framework is tested by the experiment on a real diesel engine dataset. And the results show that the proposed framework delivers a satisfactory performance advantage for the validation condition in actual applications, the root mean squared error and the coefficient of variation of the root mean squared error of the GBDT model under the validation condition are 2.94kW and 1.17%, respectively.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"158 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125778674","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}

引用次数: 0

Digital Twin-based Collision Avoidance System for Autonomous Excavator with Automatic 3D LiDAR Sensor Calibration 基于数字双模的3D激光雷达自动标定挖掘机避碰系统

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926653

Mineto Satoh

{"title":"Digital Twin-based Collision Avoidance System for Autonomous Excavator with Automatic 3D LiDAR Sensor Calibration","authors":"Mineto Satoh","doi":"10.1109/CASE49997.2022.9926653","DOIUrl":"https://doi.org/10.1109/CASE49997.2022.9926653","url":null,"abstract":"This paper proposes a real-time collision avoidance system with automatic three-dimensional (3D) Light Detection and Ranging (LiDAR) sensor calibration as a means of meeting the increasing demand for safety in construction automation. Although a typical system requires object detection to prevent collisions with obstacles in the workspace, practical safety performance relies heavily on detection accuracy and processing time delays. To achieve both robustness and operational efficiency while increasing safety, we propose a system that determines the possibility of a collision from the observed point cloud and the posture of an excavator without detecting objects. This is achieved by introducing an excavator model synchronized with a real one as a digital twin and evaluating the overlap between the volume occupied by the model and the point cloud observed by the 3D LiDAR sensor. Moreover, the algorithm to estimate the position and orientation of the 3D LiDAR was developed utilizing a digital twin and the probabilistic sequential estimation technique. The proposed system was successfully demonstrated through experiments using a real excavator, making us confident that deploying the system, from the installation of LiDAR to normal operation, could be fully automated.","PeriodicalId":325778,"journal":{"name":"2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115917676","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}

引用次数: 2

A Keypoint-based Object Representation for Generating Task-specific Grasps 用于生成特定任务抓取的基于关键点的物体表示法

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926438

Mark Robson, M. Sridharan

引用次数: 1

Towards Distributed Real-Time capable Robotic Control using ROS2 利用ROS2实现分布式实时机器人控制

2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) Pub Date : 2022-08-20 DOI: 10.1109/CASE49997.2022.9926569

C. Plasberg, H. Nessau, David Timmermann, Christian Eichmann, A. Roennau, R. Dillmann

引用次数: 1