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Active perception network for non-myopic online exploration and visual surface coverage

INTERNATIONAL JOURNAL OF robotICS RESEARCH 2025年第2期44卷 247-272页

作者： Vutetakis, David Xiao, Jing Univ North Carolina Dept Comp Sci Charlotte NC USA Worcester Polytech Inst Dept Robot Engn Worcester MA USA

This work addresses the problem of online exploration and visual sensor coverage of unknown environments. We introduce a novel perception roadmap we refer to as the Active Perception Network (APN) that serves as a hierarchical topological graph describing how to traverse and perceive an incrementally built spatial map of the environment. The APN state is incrementally updated to expand a connected configuration space that extends throughout as much of the known space as possible, using efficient difference-awareness techniques that track the discrete changes of the spatial map to inform the updates. A frontier-guided approach is presented for efficient evaluation of information gain and covisible information, which guides view sampling and refinement to ensure maximum coverage of the unmapped space is maintained within the APN. The updated roadmap is hierarchically decomposed into subgraph regions which we use to facilitate a non-myopic global view sequence planner. A comparative analysis to several state-of-the-art approaches was conducted, showing significant performance improvements in terms of total exploration time and surface coverage, and demonstrating high computational efficiency that is scalable to large and complex environments.

关键词： Aerial systems: perception and autonomy reactive and sensor-based planning software tools for robot programming software architecture for robotic and automation

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An Image-Guided robotic System for Transcranial Magnetic Stimulation: System Development and Experimental Evaluation

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IEEE robotICS AND AUTOMATION LETTERS 2025年第2期10卷 1936-1943页

作者： Liu, Yihao Zhang, Jiaming Ai, Letian Tian, Jing Sefati, Shahriar Liu, Huan Martin-Gomez, Alejandro Kheradmand, Amir Armand, Mehran Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Med Dept Neurol Baltimore 21287 MD USA Johns Hopkins Univ Dept Mech Engn Baltimore 21218 MD USA China Univ Geosci Sch Automat Wuhan 430079 Hubei Peoples R China Univ Arkansas I3R Fayetteville AR 72701 USA Johns Hopkins Med Dept Neurosci Baltimore MD 21287 USA

Transcranial magnetic stimulation is a noninvasive medical procedure that can modulate brain activity, and it is widely used in neuroscience, neurology research, and clinical practice. Compared to manual operators, robots may improve the outcome due to their superior accuracy and repeatability. However, there has not been a widely accepted standard protocol for performing robotic TMS using fine-segmented brain images, resulting in arbitrary planned angles with respect to the true boundaries of the modulated cortex. Given that the recent study in TMS simulation suggests a noticeable difference in outcomes when using different anatomical details, cortical shape should play a more significant role in deciding the optimal TMS coil pose. In this work, we introduce an image-guided robotic system for TMS that focuses on (1) establishing standardized planning methods to define a reference (true zero) for the coil poses and (2) solving the issue that the manual coil placement requires expert hand-eye coordination which often leading to low repeatability of the experiments. To validate the design of our robotic system, a phantom study and a preliminary human subject study were performed. Our results show that the robotic method can half the positional error and improve the rotational accuracy by up to two orders of magnitude. The accuracy is proven to be repeatable because the standard deviation of multiple trials is lowered by an order of magnitude. The improved actuation accuracy successfully translates to the TMS application, with a higher and more stable induced voltage in magnetic field sensors and a higher electromyography (EMG) reading in the preliminary human subject study.

关键词： robots Coils robot kinematics Planning Accuracy robot sensing systems Brain Electric fields Standards Motors Medical robots and systems robotics and automation in life sciences software tools for robot programming

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RadaRays: Real-Time Simulation of Rotating FMCW Radar for Mobile robotics via Hardware-Accelerated Ray Tracing

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IEEE robotICS AND AUTOMATION LETTERS 2025年第3期10卷 2470-2477页

作者： Mock, Alexander Magnusson, Martin Hertzberg, Joachim Osnabruck Univ KBS Grp D-49074 Osnabruck Germany Orebro Univ RNP Lab AASS Res Ctr S-70182 Orebro Sweden DFKI Niedersachsen Plan based Robot Control Grp D-49084 Osnabruck Germany

RadaRays allows for the accurate modeling and simulation of rotating FMCW radar sensors in complex environments, including the simulation of reflection, refraction, and scattering of radar waves. Our software is able to handle large numbers of objects and materials in real-time, making it suitable for use in a variety of mobile robotics applications. We demonstrate the effectiveness of RadaRays through a series of experiments and show that it can more accurately reproduce the behavior of FMCW radar sensors in a variety of environments, compared to the ray casting-based lidar-like simulations that are commonly used in simulators for autonomous driving such as CARLA. Our experiments additionally serve as a valuable reference point for researchers to evaluate their own radar simulations. By using RadaRays, developers can significantly reduce the time and cost associated with prototyping and testing FMCW radar-based algorithms. We also provide a Gazebo plugin that makes our work accessible to the mobile robotics community.

关键词： Radar Radar imaging robots Spaceborne radar Ray tracing Meteorological radar Real-time systems Radar scattering Radar antennas Radar cross-sections Simulation and animation range sensing software tools for robot programming SLAM collision avoidance

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CusADi: A GPU Parallelization Framework for Symbolic Expressions and Optimal Control

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IEEE robotICS AND AUTOMATION LETTERS 2025年第2期10卷 899-906页

作者： Jeon, Se Hwan Hong, Seungwoo Lee, Ho Jae Khazoom, Charles Kim, Sangbae MIT Dept Mech Engn Cambridge MA 02139 USA

The parallelism afforded by GPUs presents significant advantages in training controllers through reinforcement learning (RL). However, integrating model-based optimization into this process remains challenging due to the complexity of formulating and solving optimization problems across thousands of instances. In this work, we present CusADi, an extension of the casadi symbolic framework to support the parallelization of arbitrary closed-form expressions on GPUs with CUDA. We also formulate a closed-form approximation for solving general optimal control problems, enabling large-scale parallelization and evaluation of MPC controllers. Our results show a ten-fold speedup relative to similar MPC implementation on the CPU, and we demonstrate the use of CusADi for various applications, including parallel simulation, parameter sweeps, and policy training.

关键词： Graphics processing units Optimal control Training Optimization Libraries Kernel Reinforcement learning Indexes Codes Trajectory Optimization and optimal control reinforcement learning software tools for robot programming

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OHM: GPU Based Occupancy Map Generation

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IEEE robotICS AND AUTOMATION LETTERS 2022年第4期7卷 11078-11085页

作者： Stepanas, Kazys Williams, Jason Hernandez, Emili Ruetz, Fabio Hines, Thomas CSIRO Robot & Autonomous Syst Grp RASG Pullenvale Qld 4069 Australia CSIRO RASG Pullenvale Qld 4069 Australia Emesent Milton Qld 4064 Australia QUT Ctr Robot Brisbane Qld 4000 Australia

Occupancy grid maps (OGMs) are fundamental to most systems for autonomous robotic navigation. However, CPU-based implementations struggle to keep up with data rates from modern 3D lidar sensors, and provide little capacity for modern extensions which maintain richer voxel representations. This article presents Occupancy Homogenous Mapping (OHM), our open source, GPU-based OGM framework. We show how the algorithms can be mapped to GPU resources, resolving difficulties with contention to obtain a successful implementation. The implementation supports many modern OGM algorithms including Normal Distributions Transform-Occupancy Maps (NDT-OM), Normal Distributions Transform-Traversability Maps (NDT-TM), decay-rate and Truncated Sign Distance Function (TSDF). A thorough performance evaluation is presented based on tracked and quadruped Uncrewed Ground Vehicle (UGV) platforms and UAVs, and data sets from both outdoor and subterranean environments. The results demonstrate excellent performance improvements both offline, and for online processing in embedded platforms. Finally, we describe how OHM was a key enabler for the UGV navigation solution for our entry in the Defense Advanced Research Projects Agency (DARPA) Subterranean Challenge, which placed second at the Final Event.

关键词： Autonomous vehicle navigation sensor fusion software tools for robot programming

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Motion Comparator: Visual Comparison of robot Motions

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IEEE robotICS AND AUTOMATION LETTERS 2024年第9期9卷 7699-7706页

作者： Wang, Yeping Peseckis, Alexander Jiang, Zelong Gleicher, Michael Univ Wisconsin Dept Comp Sci Madison WI 53706 USA

roboticists compare robot motions for tasks such as parameter tuning, troubleshooting, and deciding between possible motions. However, most existing visualization tools are designed for individual motions and lack the features necessary to facilitate robot motion comparison. In this letter, we utilize a rigorous design framework to develop Motion Comparator, a web-based tool that facilitates the comprehension, comparison, and communication of robot motions. Our design process identified roboticists' needs, articulated design challenges, and provided corresponding strategies. Motion Comparator includes several key features such as multi-view coordination, quaternion visualization, time warping, and comparative designs. To demonstrate the applications of Motion Comparator, we discuss four case studies in which our tool is used for motion selection, troubleshooting, parameter tuning, and motion review.

关键词： robot motion Visualization robots Data visualization robot kinematics Quaternions Three-dimensional displays software tools for robot programming software tools for benchmarking and reproducibility

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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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ros2_tracing: Multipurpose Low-Overhead Framework for Real-Time Tracing of ROS 2

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IEEE robotICS AND AUTOMATION LETTERS 2022年第3期7卷 6511-6518页

作者： Bedard, Christophe Lutkebohle, Ingo Dagenais, Michel Polytech Montreal Dept Comp Engn & Software Engn Montreal PQ H3T 1J4 Canada Robert Bosch GmbH Corp Res D-71272 Renningen Germany

Testing and debugging have become major obstacles for robot software development, because of high system complexity and dynamic environments. Standard, middleware-based data recording does not provide sufficient information on internal computation and performance bottlenecks. Other existing methods also target very specific problems and thus cannot be used for multipurpose analysis. Moreover, they are not suitable for real-time applications. In this letter, we present ros2_tracing, a collection of flexible tracing tools and multipurpose instrumentation for ROS 2. It allows collecting runtime execution information on real-time distributed systems, using the low-overhead LTTng tracer. tools also integrate tracing into the invaluable ROS 2 orchestration system and other usability tools. A message latency experiment shows that the end-to-end message latency overhead, when enabling all ROS 2 instrumentation, is on average 0.0033 ms, which we believe is suitable for production real-time systems. ROS 2 execution information obtained using ros2_tracing can be combined with trace data from the operating system, enabling a wider range of precise analyses, that help understand an application execution, to find the cause of performance bottlenecks and other issues.

关键词： software tools for robot programming distributed robot systems robot Operating System (ROS) performance analysis tracing

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A High-Fidelity Simulation Platform for Industrial Manufacturing by Incorporating robotic Dynamics Into an Industrial Simulation Tool

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IEEE robotICS AND AUTOMATION LETTERS 2022年第4期7卷 9123-9128页

作者： Zhang, Zengjie Dershan, Ram Enayati, Amir M. Soufi Yaghoubi, Marjan Richert, Dean Najjaran, Homayoun Univ British Columbia Sch Engn Kelowna BC V1V 1V7 Canada Univ Victoria Fac Engn & Comp Sci Victoria BC V8P 5C2 Canada

Simulation provides an efficient and safe evaluation solution for industrial automation to pretest software before deploying it in real systems. However, only high-fidelity simulation environments that precisely reconstruct the behavioral patterns of real systems can guarantee a successful transfer from simulation to reality (sim-to-real). Many existing industrial simulation tools provide libraries for various industrial devices, which simplify the development efforts significantly, but they generally lack the ability to model the system dynamics and often fail to generate a realistic representation when the system performance is sensitive to the modeling deviation. For example, robots equipped with intelligent algorithms potentially lead to task failure if the software is sensitive to the variation of the system dynamics. In this paper, we design a novel simulation platform for industrial manufacturing use cases consisting of a cooperative robot and a modular manufacturing device. With the dynamic model of the robot integrated into a manufacturing digital-twining software, the platform achieves high simulation fidelity by incorporating the effect of the robot dynamics to the control logic of the industrial tasks. Also, the simulation can exchange data with the real robot via an open protocol, which enables the simultaneous test of the real and simulated systems. Two experiments are conducted on the simulation platform to validate its fidelity in terms of the consistent control logic with the real system. Also, a workpiece distribution use case is studied to show how the simulation platform is used to develop a task-planning algorithm for a manufacturing application.

关键词： Simulation and animation software tools for robot programming software-hardware integration for robot systems robot simulation industrial software

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Deploying and Evaluating LLMs to Program Service Mobile robots

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IEEE robotICS AND AUTOMATION LETTERS 2024年第3期9卷 2853-2860页

作者： Hu, Zichao Lucchetti, Francesca Schlesinger, Claire Saxena, Yash Freeman, Anders Modak, Sadanand Guha, Arjun Biswas, Joydeep Univ Texas Austin Dept Comp Sci Austin TX 78712 USA Northeastern Univ Khoury Coll Comp Sci Boston MA 02115 USA Wellesley Coll Dept Comp Sci Wellesley MA 02481 USA

Recent advancements in large language models (LLMs) have spurred interest in using them for generating robot programs from natural language, with promising initial results. We investigate the use of LLMs to generate programs for service mobile robots leveraging mobility, perception, and human interaction skills, and where accurate sequencing and ordering of actions is crucial for success. We contribute CodeBotler, an open-source robot-agnostic tool to program service mobile robots from natural language, and RoboEval, a benchmark for evaluating LLMs' capabilities of generating programs to complete service robot tasks. CodeBotler performs program generation via few-shot prompting of LLMs with an embedded domain-specific language (eDSL) in Python, and leverages skill abstractions to deploy generated programs on any general-purpose mobile robot. RoboEval evaluates the correctness of generated programs by checking execution traces starting with multiple initial states, and checking whether the traces satisfy temporal logic properties that encode correctness for each task. RoboEval also includes multiple prompts per task to test for the robustness of program generation. We evaluate several popular state-of-the-art LLMs with the RoboEval benchmark, and perform a thorough analysis of the modes of failures, resulting in a taxonomy that highlights common pitfalls of LLMs at generating robot programs.

关键词： software tools for benchmarking and reproducibility software tools for robot programming social HRI human-centered robotics service robotics

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