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Overworld: Assessing the Geometry of the World for Human-Robot Interaction

IEEE roboticS AND automation LETTERS 2023年第3期8卷 1874-1880页

作者： Sarthou, Guillaume Univ Toulouse LAAS CNRS CNRS Toulouse France

For a robot to interact with humans in a given environment, a key need is to understand its environment in terms of the objects composing it, the other agents acting in it, and the relations between all of them. This capability is often called the geometrical situation assessment and is mainly related to spatial reasoning in time. In this letter, we present Overworld, a novel lightweight and open-source framework, merging the key features of a decade of research in the domain. It permanently maintains a geometric state of the world from the point of view of the robot by aggregating perceptual information from several sources and reasoning on them to create a coherent world. Furthermore, Overworld implements perspective-taking by emulating the humans' ability to perceive to estimate the state of the world from their perspective. Finally, thanks to a strong link with an ontology framework, it ensures knowledge coherence in the whole robotic architecture. This work is part of a broader effort to develop a complete, stable, and shareable decisional robotic architecture for Human-Robot Interaction.

关键词： Multi-modal perception for HRI human-robot collaboration software architecture for robotic and automation

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Plug'n Play Task-Level Autonomy for robotics Using POMDPs and Probabilistic Programs

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IEEE roboticS AND automation LETTERS 2024年第1期9卷 587-594页

作者： Wertheim, Or Suissa, Dan R. Brafman, Ronen I. Ben Gurion Univ Negev Dept Comp Sci IL-84105 Beer Sheva Israel

We describe AOS, the first general-purpose system for model-based control of autonomous robots using AI planning that fully supports partial observability and noisy sensing. The AOS provides a code-based language for specifying a generative model of the system, making model specification easier and model sampling efficient. It provides a language for specifying the relation between the model and the code, using which it auto-generates all required integration code. This allows Plug'n Play behavior, which facilitates incremental and modular system design. Extensive experiments on real and simulated robotic platforms demonstrate these advantages.

关键词： AI-enabled robotics autonomous agents integrated planning and control planning under uncertainty software architecture for robotic and automation

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Autonomous System for Tumor Resection (ASTR) - Dual-Arm robotic Midline Partial Glossectomy

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IEEE roboticS AND automation LETTERS 2024年第2期9卷 1166-1173页

作者： Ge, Jiawei Kam, Michael Opfermann, Justin D. Saeidi, Hamed Leonard, Simon Mady, Leila J. Schnermann, Martin J. Krieger, Axel Johns Hopkins Univ Dept Mech Engn Baltimore MD 21211 USA Univ N Carolina Dept Comp Sci Wilmington NC 28403 USA Johns Hopkins Univ Dept Elect & Comp Engn Baltimore MD 21211 USA Johns Hopkins Univ Johns Hopkins Sch Med Dept Otolaryngol Head & Neck Surg Baltimore MD 21287 USA NCI Chem Biol Lab Ctr Canc Res NIH Frederick MD 21702 USA

Head and neck cancers are the seventh most common cancers worldwide, with squamous cell carcinoma being the most prevalent histologic subtype. Surgical resection is a primary treatment modality for many patients with head and neck squamous cell carcinoma, and accurately identifying tumor boundaries and ensuring sufficient resection margins are critical for optimizing oncologic outcomes. This letter presents an innovative autonomous system for tumor resection (ASTR) and conducts a feasibility study by performing supervised autonomous midline partial glossectomy for pseudotumor with millimeter accuracy. The proposed ASTR system consists of a dual-camera vision system, an electrosurgical instrument, a newly developed vacuum grasping instrument, two 6-DOF manipulators, and a novel autonomous control system. The letter introduces an ontology-based research framework for creating and implementing a complex autonomous surgical workflow, using the glossectomy as a case study. Porcine tongue tissues are used in this study, and marked using color inks and near-infrared fluorescent (NIRF) markers to indicate the pseudotumor. ASTR actively monitors the NIRF markers and gathers spatial and color data from the samples, enabling planning and execution of robot trajectories in accordance with the proposed glossectomy workflow. The system successfully performs six consecutive supervised autonomous pseudotumor resections on porcine specimens. The average surface and depth resection errors measure 0.73 +/- 0.60 mm and 1.89 +/- 0.54 mm, respectively, with no positive tumor margins detected in any of the six resections. The resection accuracy is demonstrated to be on par with manual pseudotumor glossectomy performed by an experienced otolaryngologist.

关键词： Index Terms-Medical robots and systems software architecture for robotic and automation control architectures and programming

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Impact of ROS 2 Node Composition in robotic Systems

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IEEE roboticS AND automation LETTERS 2023年第7期8卷 3996-4003页

作者： Macenski, Steve Soragna, Alberto Carroll, Michael Ge, Zhenpeng Samsung Res Amer Mountain View CA 94043 USA iRobot Corp Bedford MA 01730 USA Open Robot Mountain View CA 94043 USA Univ Elect Sci & Technol China Chengdu 610056 Peoples R China

The Robot Operating System 2 (ROS 2) is the second generation of ROS representing a step forward in the robotic framework. Several new types of nodes and executor models are integral to control where, how, and when information is processed in the computational graph. This letter explores and benchmarks one of these new node types - the Component node - which allows nodes to be composed manually or dynamically into processes while retaining separation of concerns in a codebase for distributed development. Composition is shown to achieve a high degree of performance optimization, particularly valuable for resource-constrained systems and sensor processing pipelines, enabling distributed tasks that would not be otherwise possible in ROS 2. In this work, we briefly introduce the significance and design of node composition, then our contribution of benchmarking is provided to analyze its impact on robotic systems. Its compelling influence on performance is shown through several experiments on the latest Long Term Support (LTS) ROS 2 distribution, Humble Hawksbill.

关键词： Robots Task analysis Robot sensing systems Optimization Operating systems Middleware Benchmark testing Engineering for robotic systems software architecture for robotic and automation software tools for robot programming

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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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A Tree-Based World Model for Reducing System Complexity in Autonomous Mobile Manipulation

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IEEE roboticS AND automation LETTERS 2024年第7期9卷 6680-6687页

作者： Sakagami, Ryo Doemel, Andreas Lehner, Peter Riedel, Sebastian Brunner, Sebastian G. Albu-Schaeffer, Alin Stulp, Freek German Aerosp Ctr DLR Inst Robot & Mechatron D-82234 Wessling Germany German Aerosp Ctr DLR D-82234 Wessling Germany

Mobile manipulation tasks in unstructured environments remain challenging for autonomous robots. The need to employ a diverse set of software and hardware components to solve the various subtasks inevitably increases system complexity. Knowledge exchange among such diverse components renders them highly coupled, reduces communication efficiency, and makes the knowledge less accessible. To overcome these challenges, we propose AIMM-WM, a central world model as a single source of truth having an abstracted geometric tree structure. Despite its concise, efficient state representation, AIMM-WM is able to provide a wide range of information from low-level geometries to highly abstracted symbols and is interfaced with diverse components for navigation, motion planning, perception, decision-making, and mission control. We evaluate the performance of AIMM-WM from the real use case of our Lightweight Rover Unit during the four-week Moon-analogue demo mission on Mt. Etna, Italy.

关键词： Robots software Navigation End effectors Complexity theory Decision making Collision avoidance Mobile manipulation software architecture for robotic and automation

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

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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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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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