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Ev-Conv: Fast CNN Inference on Event Camera Inputs for High-Speed Robot Perception

IEEE roboticS AND automation LETTERS 2023年第6期8卷 3174-3181页

作者： Durvasula, Sankeerth Guan, Yushi Vijaykumar, Nandita Univ Toronto Toronto ON M5R 0A3 Canada

Event cameras capture visual information with a high temporal resolution and a wide dynamic range. This enables capturing visual information at fine time granularities (e.g., microseconds) in rapidly changing environments. This makes event cameras highly useful for high-speed robotics tasks involving rapid motion, such as high-speed perception, object tracking, and control. However, convolutional neural network inference on event camera streams cannot currently perform real-time inference at the high speeds at which event cameras operate-current CNN inference times are typically closer in order of magnitude to the frame rates of regular frame-based cameras. Real-time inference at event camera rates is necessary to fully leverage the high frequency and high temporal resolution that event cameras offer. This letter presents Ev-Conv, a new approach to enable fast inference on CNNs for inputs from event cameras. We observe that consecutive inputs to the CNN from an event camera have only small differences between them. Thus, we propose to perform inference on the difference between consecutive input tensors, or the increment. This enables a significant reduction in the number of floating-point operations required (and thus the inference latency) because increments are very sparse. We design Ev-Conv to leverage the irregular sparsity in increments from event cameras and to retain the sparsity of these increments across all layers of the network. We demonstrate a reduction in the number of floating operations required in the forward pass by up to 98%. We also demonstrate a speedup of up to 1.6xfor inference using CNNs for tasks such as depth estimation, object recognition, and optical flow estimation, with almost no loss in accuracy.

关键词： software architecture for robotic and automation computer architecture for robotic and automation software middleware and programming environments

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A Distributed Framework for Knowledge-Driven Root-Cause Analysis on Evolving Alarm Data-An Industrial Case Study

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IEEE roboticS AND automation LETTERS 2023年第6期8卷 3732-3739页

作者： Wilch, Jan Vogel-Heuser, Birgit Mager, Jens Cendelin, Rostislav Fett, Thomas Hsieh, Yu-Ming Cheng, Fan-Tien Tech Univ Munich Inst Automat & Informat Syst D-85748 Garching Germany Reifenhauser Reicofil GmbH Co KG D-53844 Troisdorf Germany Natl Cheng Kung Univ Acad Innovat Semicond & Sustainable Mfg Tainan 701401 Taiwan Natl Cheng Kung Univ Inst Mfg Informat & Syst Tainan 701401 Taiwan

Root-cause Analysis (RCA) of alarms is a well-established research area in automated Production Systems (aPS). Many RCA algorithms have been proposed and successfully evaluated and new ones are being developed. Recently, researchers focus on the incorporation of formalized information about the technical process in the analysis to gather further evidence for common root causes. In industrial applications, alarm data are usually preprocessed to accommodate for use case-specific properties and prepare subsequent analysis steps. Consequently, this letter proposes a generalized RCA framework, for which an arbitrary number of preprocessing, data-driven RCA, and postprocessing algorithms can be selected, to support varying use cases. The framework was successfully evaluated in an industrial case study, using 1.8 million alarms recorded over 450 days from an industrial nonwoven production plant and analyzed using formalized information from process documentation and expert interviews. Seven preprocessing algorithms, one data-driven RCA algorithm, and nine postprocessing algorithms typical for continuous and hybrid technical processes were realized in an otherwise entirely use case-agnostic implementation.

关键词： Manufacturing Computer architecture Cloud computing Reliability Production systems Process control Manuals software architecture for robotic and automation Big Data in robotics and automation

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CNS Flight Stack for Reproducible, Customizable, and Fully Autonomous Applications

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IEEE roboticS AND automation LETTERS 2022年第4期7卷 11283-11290页

作者： Scheiber, Martin Fornasier, Alessandro Jung, Roland Bohm, Christoph Dhakate, Rohit Stewart, Christian Steinbrener, Jan Weiss, Stephan Brommer, Christian Univ Klagenfurt Inst Smart Syst Technol Grp Control Networked Syst A-9020 Klagenfurt Austria Aperture Robot LLC Santa Barbara CA 93103 USA

While low-level auto pilot stacks for aerial vehicles focus on robust control, sensing, and estimation, the continuous advancement of higher-level autonomy for aerial vehicles requires much more complex higher-level flight stacks in order to enable safe, fully autonomous long-duration missions. Rather than focusing on the low-level control, high-level flight stacks are required to monitor the system's integrity continuously, initiate contingency plans, execute mission plans and adapt them in non-nominal situations, allow for proper data logging, and provide standardized interfaces and integrity verification for external mission planners and localization modules. To that end, we present our freely available, high-level flight stack dubbed CNS Flight Stack that meets the above requirements and at the same time a) is platform-agnostic through a generalized (embedded) hardware abstraction layer, b) uses low compute complexity for online use on embedded hardware, and c) can he extended with other sensor modalities, integrity checks, and mission modules. These additional properties make it reproducible on a variety of different platforms for safe and fully autonomous applications. We tested the proposed flight stack in over 450 real-world flights and report the failure modes our framework detected and also mitigated to avoid crashes of the aerial system.

关键词： software architecture for robotic and automation autonomous vehicle navigation aerial systems: perception and autonomy

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FRobs_RL: A Flexible robotics Reinforcement Learning Library 18

FRobs_RL: A Flexible Robotics Reinforcement Learning Library

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IEEE 18th International Conference on automation Science and Engineering (IEEE CASE)

作者： Manuel Fajardo, Jose Gonzalez Roldan, Felipe Realpe, Sebastian Hernandez, Juan D. Ji, Ze Cardenas, Pedro-F Univ Nacl Colombia Mech & Mechatron Dept Bogota Colombia Cardiff Univ Sch Comp Sci & Informat Cardiff Wales Cardiff Univ Sch Engn Cardiff Wales

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

Reinforcement learning (RL) has become an interesting topic in robotics applications as it can solve complex problems in specific scenarios. The small amount of RL-tools focused on robotics, plus the lack of features such as easy transfer of simulated environments to real hardware, are obstacles to the widespread use of RL in robotic applications. FRobs _RL is a Python library that aims to facilitate the implementation, testing, and deployment of RL algorithms in intelligent robotic applications using robot operating system (ROS), Gazebo, and OpenAI Gym. FRobs _RL provides an Application Programming Interface (API) to simplify the creation of RL environments, where users can import a wide variety of robot models as well as different simulated environments. With the FRobs _RL library, users do not need to be experts in ROS, Gym, or Gazebo to create a realistic RL application. Using the library, we created and tested two environments containing common robotic tasks;one is a reacher task using a robotic manipulator, and the other is a mapless navigation task using a mobile robot. The library is available in GitHub.

关键词： Deep Learning Methods Reinforcement Learning software architecture for robotic and automation

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robotic Framework for Autonomous Assembly: a Report from the Robothon 2021 Grand Challenge

Robotic Framework for Autonomous Assembly: a Report from the...

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Wittmann, Jonas Pachler, Florian Ruhkamp, Patrick Jung, Hyunjun Sygulla, Felix Rixen, Daniel Tech Univ Munich TUM Sch Engn & Design Munich Inst Robot & Machine Intelligence Chair Appl Mech Munich Germany Tech Univ Munich Dept Informat Comp Aided Med Procedures & AR Munich Germany

ISBN: (纸本)9781665482172

Up to now, many repetitive industrial processes, like welding in automotive, have been automated. However, robust solutions for assembly operations that, besides precise robot positioning, also require precise tactile interaction are not available yet. The current research efforts on sensitive collaborative robots and their control are a promising step towards assembly automation. In our contribution we present an autonomous robotic framework to automate assembly tasks. It implements state-ofthe-art algorithms for object localization, motion planning, force control, error handling and task scheduling. We present the implementation details of our localization and insertion skills on a FRANKA EMIKA robot and an Intel RealSense, the two main components of the framework. Further, we introduce the Robothon Grand Challenge, a new biennial competition series that deals with automated electronic waste decomposition. Within that competition, our framework made the fifth place out of nine teams.

关键词： Compliant Assembly Intelligent and Flexible Manufacturing software architecture for robotic and automation

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Oops! It's Too Late. Your Autonomous Driving System Needs a Faster Middleware

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IEEE roboticS AND automation LETTERS 2021年第4期6卷 7301-7308页

作者： Wu, Tianze Wu, Baofu Wang, Sa Liu, Liangkai Liu, Shaoshan Bao, Yungang Shi, Weisong Chinese Acedemy Sci State Key Lab Comp Architecture Inst Comp Technol Beijing 100089 Peoples R China Univ Chinese Acad Sci Beijing 100089 Peoples R China Hangzhou Dianzi Univ Sch Comp Sci & Technol Hangzhou 310018 Peoples R China Wayne State Univ Detroit MI 48202 USA Univ Chinese Acad Sci State Key Lab Comp Architecture Inst Comp Technol Nanjing Peoples R China Univ Chinese Acad Sci Inst Comp Technol Nanjing Nanjing 100190 Peoples R China PerceptIn Santa Clara CA 95054 USA Univ Chinese Acad Sci State Key Lab Comp Architecture Inst Comp Technol Beijing 100190 Peoples R China Peng Cheng Lab Beijing 100190 Peoples R China

Autonomous Driving (AD) has entered a period of rapid development in recent years. With the amount of sensors and control logics installed increasing tremendously to guarantee robustness, a big challenge is posed for AD middleware. Both the academia and the industry are eager for an investigation of the performance of middlewares in Autonomous Driving Vehicles (AVs). To fill this gap, we summarize typical communication scenarios of AVs and evaluate different communication mechanisms of three popular open-source middlewares comprehensively. Besides, we construct a benchmark pack named ComP which consists of a perception communication scenario and a group of real AD applications for researchers to assess middleware performance. Our findings provide useful guidelines for researchers and insightful optimization advice for designing middlewares.

关键词： Embedded systems for robotic and automation software architecture for robotic and automation distributed robot systems

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Improving the Reliability of Pick-and-Place With Aerial Vehicles Through Fault-Tolerant software and a Custom Magnetic End-Effector

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IEEE roboticS AND automation LETTERS 2021年第4期6卷 7501-7508页

作者： Garimella, Gowtham Sheckells, Matthew Kim, Soowon Baraban, Gabriel Kobilarov, Marin Johns Hopkins Univ Dept Mech Engn Baltimore MD 21218 USA Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA SpaceX Hawthorne CA 90250 USA Virginia Polytech Inst & State Univ Blacksburg VA 24061 USA

Aerial manipulation is an emerging field in robotics with various potential applications such as transport and delivery, agriculture, and, infrastructure inspection. To deploy aerial vehicles in the real world, the safety and reliability of these systems is paramount. Motivated by the need for safety and reliability, this work proposes a software framework that has built-in robustness to algorithmic failures and hardware faults. The framework allows users to build complex applications while reasoning about faults that can happen at different stages of an aerial manipulation task and specifying fallback actions to return to normal operating mode. The aerial manipulator is further endowed with a magnetic gripper that can handle positional errors arising from perception and control uncertainties. We also introduce a bias estimator for measuring the contact forces and sensor bias. We demonstrate how the estimator can be used to detect either completion or failures across several tasks. We demonstrate the reliability of the proposed framework on two tasks: package sorting task (e.g. as might be used in a distribution center) and sensor placement task (for infrastructure inspection). We show different failure modes that can occur and how our aerial manipulation system recovers from them.

关键词： Planning scheduling and coordination software architecture for robotic and automation factory automation

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ChoiRbot: A ROS 2 Toolbox for Cooperative robotics

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IEEE roboticS AND automation LETTERS 2021年第2期6卷 2714-2720页

作者： Testa, Andrea Camisa, Andrea Notarstefano, Giuseppe Univ Bologna Dept Elect Elect & Informat Engn I-40136 Bologna Italy

In this letter, we introduce ChoiRbot, a toolbox for distributed cooperative robotics based on the novel Robot Operating System (ROS) 2. ChoiRbot provides a fully-functional toolset to execute complex distributed multi-robot tasks, either in simulation or experimentally, with a particular focus on networks of heterogeneous robots without a central coordinator. Thanks to its modular structure, ChoiRbot allows for a highly straight implementation of optimization-based distributed control schemes, such as distributed optimal control, model predictive control, task assignment, in which local computation and communication with neighboring robots are alternated. To this end, the toolbox provides functionalities for the solution of distributed optimization problems. The package can be also used to implement distributed feedback laws that do not need optimization features but do require the exchange of information among robots. The potential of the toolbox is illustrated with simulations and experiments on distributed robotics scenarios with mobile ground robots. The ChoiRbot toolbox is available at https://***/OPT4SMART/choirbot.

关键词： Distributed robot systems optimization and optimal control software architecture for robotic and automation

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ROS-NetSim: A Framework for the Integration of robotic and Network Simulators

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IEEE roboticS AND automation LETTERS 2021年第2期6卷 1120-1127页

作者： Calvo-Fullana, Miguel Mox, Daniel Pyattaev, Alexander Fink, Jonathan Kumar, Vijay Ribeiro, Alejandro Univ Penn Dept Elect & Syst Engn Philadelphia PA 19104 USA MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA Univ Penn Dept Mech Engn & Appl Mech Philadelphia PA 19104 USA Tampere Univ Unit Elect Engn Tampere 33720 Finland US Army Res Lab Dept Computat & Informat Sci Adelphi MD 20783 USA

Multi-agent systems play an important role in modern robotics. Due to the nature of these systems, coordination among agents via communication is frequently necessary. Indeed, Perception-Action-Communication (PAC) loops, or Perception-Action loops closed over a communication channel, are a critical component of multi-robot systems. However, we lack appropriate tools for simulating PAC loops. To that end, in this letter, we introduce ROS-NetSim, a ROS package that acts as an interface between robotic and network simulators. With ROS-NetSim, we can attain high-fidelity representations of both robotic and network interactions by accurately simulating the PAC loop. Our proposed approach is lightweight, modular and adaptive. Furthermore, it can be used with many available network and physics simulators by making use of our proposed interface. In summary, ROS-NetSim is (i) Transparent to the ROS target application, (ii) Agnostic to the specific network and physics simulator being used, and (iii) Tunable in fidelity and complexity. As part of our contribution, we have made available an open-source implementation of ROS-NetSim to the community.

关键词： Physics Robot kinematics Picture archiving and communication systems Synchronization Robot sensing systems Communication channels Wireless sensor networks Multi-robot systems networked robots methods and tools for robot system design software architecture for robotic and automation

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An Expressiveness Hierarchy of Behavior Trees and Related architectures

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IEEE roboticS AND automation LETTERS 2021年第3期6卷 5397-5404页

作者： Biggar, Oliver Zamani, Mohammad Shames, Iman Def Sci & Technol Grp Land Div Fishermans Bend 3207 Australia Australian Natl Univ Sch Engn Canberra ACT 2601 Australia

In this letter, we provide a formal framework for comparing the expressive power of Behavior Trees (BTs) to other action selection architectures. Taking inspiration from the analogous comparisons of structural programming methodologies, we formalise the concept of 'expressiveness'. This leads us to an expressiveness hierarchy of control architectures, which includes BTs, Decision Trees (DTs), Teleo-reactive Programs (TRs) and Finite State Machines (FSMs). By distinguishing between BTs with auxiliary variables and those without, we demonstrate the existence of a trade-off in BT design between readability and expressiveness. We discuss what this means for BTs in practice.

关键词： Control architectures and programming methods and tools for robot system design software architecture for robotic and automation

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