2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)最新文献_第4页

Developmentally Synthesizing Earthworm-Like Locomotion Gaits with Bayesian-Augmented Deep Deterministic Policy Gradients (DDPG) 基于贝叶斯增强深度确定性策略梯度(DDPG)的类蚯蚓运动步态发育合成

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216782

Sayyed Jaffar Ali Raza, Apan Dastider, Mingjie Lin

{"title":"Developmentally Synthesizing Earthworm-Like Locomotion Gaits with Bayesian-Augmented Deep Deterministic Policy Gradients (DDPG)","authors":"Sayyed Jaffar Ali Raza, Apan Dastider, Mingjie Lin","doi":"10.1109/CASE48305.2020.9216782","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216782","url":null,"abstract":"In this paper, a reinforcement learning method is presented to generate earthworm-like gaits for a hyperredundant earthworm-like manipulator robot. Partially inspired by human brain’s learning mechanism, the proposed learning framework builds its preliminary belief by first starting with adapting rudimentary gaits governed by a generic kinematic knowledge of undulatory, sidewinding and circular patterns. The preliminary belief is then represented as a prior ensemble to learn new gaits by leveraging apriori knowledge and learning a policy by inferring posterior over prior distribution. While the fundamental idea of incorporating Bayesian learning with reinforcement learning is not new, this paper extends Bayesian actor-critic approach by introducing augmented prior-based directed bias in policy search, aiding in faster parameter learning and reduced sampling requirements. We show results on an in-house built 10-DoF earthworm-like robot that exhibits adaptive development, qualitatively learning different locomotion modes, while given with only rudimentary generic gait behaviors. The results are compared against deterministic policy gradient method (DDPG) for continuous control as the baseline. We show that our proposed method can characterize effective performance over DDPG, and it also achieves faster kinematic indexes in various gaits.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132374107","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

Robust Task-Based Grasping as a Service 健壮的基于任务的抓取即服务

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216952

Jingyi Song, A. Tanwani, Jeffrey Ichnowski, Michael Danielczuk, Kate Sanders, Jackson Chui, J. A. Ojea, Ken Goldberg

引用次数: 4

Redundant Robot Control Using Multi Agent Reinforcement Learning 基于多智能体强化学习的冗余机器人控制

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216774

Adolfo Perrusquía, Wen Yu, Xiaoou Li

引用次数: 7

A Self-Adaptive Cuckoo Search Algorithm for Energy Consumption Minimization Problem with Deadline Constraint 最后期限约束下能耗最小化问题的自适应布谷鸟搜索算法

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216895

Biao Hu, Hao Chen, Zhengcai Cao, Chengran Lin

引用次数: 0

Mura Defect Detection on Compact Camera Module (CCM) Using Metric Learning 基于度量学习的紧凑型相机模块(CCM)缺陷检测

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216886

Y. Kim, T. Park

{"title":"Mura Defect Detection on Compact Camera Module (CCM) Using Metric Learning","authors":"Y. Kim, T. Park","doi":"10.1109/CASE48305.2020.9216886","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216886","url":null,"abstract":"The Compact Camera Module (CCM) is a device used for various compact electronic devices such as notebooks, smartphones, etc. Various defects occur in the manufacturing process, such as scratches, stamps, and mura. Most notably, mura defect detection is the most challenging issue because of how normal it appears. With this, various methods based on deep learning have been developed to detect mura defects. However, previous research assumes that there is a substantial amount of training data. Therefore, classification accuracy decreases in an environment wherein it is difficult to obtain a sample of an actual defect. This study proposes a metric learning-based mura defect detection method with higher classification accuracy than the previous semantic segmentation method in an environment with little training data. In the training phase, we obtained the center of the normal metric vector of the input image through the metric embedding model and metric loss, while in the test stage, we detected the mura defect based on the center of the normal metric vector. The experimental results show that the proposed method has higher detection accuracy than the previous method in an environment with few training data.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126712342","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

Multi-robot Path Planning and Trajectory Smoothing 多机器人路径规划与轨迹平滑

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216972

Hub Ali, Gang Xiong, Huaiyu Wu, Bin Hu, Zhen Shen, Hongxing Bai

{"title":"Multi-robot Path Planning and Trajectory Smoothing","authors":"Hub Ali, Gang Xiong, Huaiyu Wu, Bin Hu, Zhen Shen, Hongxing Bai","doi":"10.1109/CASE48305.2020.9216972","DOIUrl":"https://doi.org/10.1109/CASE48305.2020.9216972","url":null,"abstract":"In this paper we consider a problem in task execution for multi-robot trajectory planning with collision avoidance in a shared working environment. Consider two or more robots generating trajectories towards their respective goal positions. The collision may occur if their trajectory coordinates are intersecting at a point or follow the same path segment simultaneously. The central planner is introduced to control robot motion in the collision state and to reduce the complexity of the multi-robot path planning system. The global path for every robot is generated by the $mathrm{A}^{*}$ algorithm in a grid-based environment. The path has presented a sequence of optimal grid numbers and later transformed into Cartesian coordinates for smooth trajectory generation. The central planner takes an optimal grid sequence for every robot to analyze the collision state according to its cost value. It regenerates the trajectories to minimize the complexity cost value and replaces the previous trajectory based on minimum cost value. In the collision state, the central planner allows one robot at a time to pass along the conflict path segment and hold others in queue at a safety offset distance until the previous robot passes safely. The algorithm has been applied to robots working in a shared environment in complex maps and the simulations is performed with MATLAB to calculate the efficiency of this approach for handling collision states in a multi-robot path planning system.","PeriodicalId":212181,"journal":{"name":"2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126323832","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: 6

Turn and orientation Sensitive A* for Autonomous Vehicles in Intelligent Material Handling Systems 智能物料搬运系统中自动驾驶车辆的转向和方向敏感A*

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216869

Rashmi Ballamajalu, M. Li, F. Sahin, C. Hochgraf, R. Ptucha, M. Kuhl

引用次数: 3

Coverage Criteria based Testing of Industrial Robots 基于工业机器人测试的覆盖标准

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9217031

Ameena K. Ashraf, Meenakshi D'Souza, R. Jetley

引用次数: 1

Accessibility Map for Assisting Cutter Posture Determination in Five-Axis Mold Machining * 辅助五轴模具加工中刀具姿态确定的可达性图*

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216975

M. Inui, Kouhei Nishimiya, Nobuyuki Umezu

引用次数: 1

Formulation and Methods for a Class of Two-stage Flow-shop Scheduling Problem with the Batch Processor 一类带批处理的两阶段流水车间调度问题的表述与方法

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE) Pub Date : 2020-08-01 DOI: 10.1109/CASE48305.2020.9216748

Runsen Wang, Yilan Shen, Weihao Wang, Leyuan Shi

引用次数: 2