On 16 November 2011, Japan Embedded Systems Technology Association (JASA) announced that Platform Research Group of Engineering division has started work on the specification of OpenEL (Embedded Libraries) for Robot.
OpenEL for Robot is an open platform to standardize the specifications of the software implementation of robotics and control systems.
This is the Japanese version of the presentation materials that were presented at Embedded Technology 2011 in Japan. The English version is under construction.
This presentation shows how to do frontloading in software development in fundamental level, also called shift-left technique or W model. The main topic is software trap analysis / software failure mode analysis, a method of root cause analysis of (part of) software FMEA, which extracts patterns of bugs.
On 16 November 2011, Japan Embedded Systems Technology Association (JASA) announced that Platform Research Group of Engineering division has started work on the specification of OpenEL (Embedded Libraries) for Robot.
OpenEL for Robot is an open platform to standardize the specifications of the software implementation of robotics and control systems.
This is the Japanese version of the presentation materials that were presented at Embedded Technology 2011 in Japan. The English version is under construction.
This presentation shows how to do frontloading in software development in fundamental level, also called shift-left technique or W model. The main topic is software trap analysis / software failure mode analysis, a method of root cause analysis of (part of) software FMEA, which extracts patterns of bugs.
オープンコミュニティ「要求開発アライアンス」(http://www.openthology.org)の2012年12月定例会発表資料です。
Open Community "Requirement Development Alliance" 2012/12 regular meeting of the presentation materials.
On Sept. 4, 2010 at XP Matsuri, Kenji Hiranabe talked about the current situation of Agile and XP. Covers history of Patterns and Agile, Lean and recent Kanban movements, and goes back to XP. Explores what was the thing called "XP" with love.
Math in Machine Learning / PCA and SVD with ApplicationsKenji Hiranabe
Math in Machine Learning / PCA and SVD with Applications
機会学習の数学とPCA/SVD
Colab での練習コードつきです.コードはこちら.
https://colab.research.google.com/drive/1YZgZWX5a7_MGA__HV2bybSuJsqkd4XxD?usp=sharing
Graphic Notes on Introduction to Linear AlgebraKenji Hiranabe
Graphic Notes on Introduction to Linear Algebra authored by Prof. Gilbert Strang.
This is an idea for visualization to better understand linear algebra.
If you want a PowerPoint version, feel free to let me know, I'll share it with you.
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。