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2812 | An Interaction-Aware Two-Level Robotic Planning and Control System for Vegetation Override 21

2812 | An Interaction-Aware Two-Level Robotic Planning and C...

21st International and 12th Asia-Pacific Regional Conference of the International Society for Terrain-Vehicle Systems, ISTVS 2024

作者： Noren, Charles Shirose, Burhanuddin Vundurthy, Bhaskar Scherer, Sebastian Travers, Matthew The Robotics Institute School of Computer Science Carnegie Mellon University PA United States

ISBN: (纸本)9781942112570

During off-road operations, mobile robotic platforms often encounter objects that influence the platform's route. As determining the outcome of an interaction with an object (e.g., overriding) is difficult, many robotic planners are designed to avoid interactions with all environmental objects. Yet, this object-adverse planning behavior is not reflected in the actions of expert human operators, who may interact with objects in order to find a viable path towards their goal. This work intends to emulate that human operator intuition. Our objective is to improve the performance of robotic traversals in off-road terrains through the development of a planning paradigm that allows certain safe contact with environmental objects. Specifically, we design a two-level hierarchical planning and control system which couples a contact-informed regional motion planner with contact-constrained local nonlinear trajectory optimization techniques. The approach is demonstrated for classes of vegetative objects which are characterized by existing parameterized collision models from the terramechanics community. The top level of the hierarchy combines sampling-based planning techniques with a set of override (velocity) constraints derived from these collision models during the search for a minimum-time trajectory towards the platform's goal. This minimum-time trajectory is then passed to the lower-level of the architecture, which utilizes direct collocation and model predictive control techniques to ensure that the velocity constraints are enforced during trajectory execution. The capabilities of the architecture are shown both in simulation and onboard a robotic platform, where vegetation is reasoned about and then overridden depending on the environment, platform, and object geometry. © 2024 International Society for Terrain-Vehicle Systems. All rights reserved.

关键词： Off road vehicles

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TECMH:Transformer-Based Cross-Modal Hashing For Fine-Grained Image-Text Retrieval

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computers, Materials & Continua 2023年第5期75卷 3713-3728页

作者： Qiqi Li Longfei Ma Zheng Jiang Mingyong Li Bo Jin College of Computer and Information Science Chongqing Normal UniversityChongqing401331China Institute of Systems and Robotics(ISR) Department of Electrical and Computer Engineering(DEEC)University of CoimbraCoimbraPortugal

In recent years,cross-modal hash retrieval has become a popular research field because of its advantages of high efficiency and low ***-modal retrieval technology can be applied to search engines,crossmodalmedical processing,*** existing main method is to use amulti-label matching paradigm to finish the retrieval ***,such methods do not use fine-grained information in the multi-modal data,which may lead to suboptimal *** avoid cross-modal matching turning into label matching,this paper proposes an end-to-end fine-grained cross-modal hash retrieval method,which can focus more on the fine-grained semantic information of multi-modal ***,the method refines the image features and no longer uses multiple labels to represent text features but uses BERT for ***,this method uses the inference capabilities of the transformer encoder to generate global fine-grained ***,in order to better judge the effect of the fine-grained model,this paper uses the datasets in the image text matching field instead of the traditional label-matching *** article experiment on Microsoft COCO(MS-COCO)and Flickr30K datasets and compare it with the previous *** experimental results show that this method can obtain more advanced results in the cross-modal hash retrieval field.

关键词： Deep learning cross-modal retrieval hash learning transformer

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Person Segmentation and Action Classification for Multi-Channel Hemisphere Field of View LiDAR Sensors

Person Segmentation and Action Classification for Multi-Chan...

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2025 IEEE/SICE International Symposium on System Integration, SII 2025

作者： Seliunina, Svetlana Otelepko, Artem Memmesheimer, Raphael Behnke, Sven University of Bonn Autonomous Intelligent Systems Group Computer Science Institute VI - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics Germany

ISBN: (纸本)9798331531614

Robots need to perceive persons in their surroundings for safety and to interact with them. In this paper, we present a person segmentation and action classification approach that operates on 3D scans of hemisphere field of view LiDAR sensors. We recorded a data set with an Ouster OSDome-64 sensor consisting of scenes where persons perform three different actions and annotated it. We propose a method based on a MaskDINO model to detect and segment persons and to recognize their actions from combined spherical projected multi-channel representations of the LiDAR data with an additional positional encoding. Our approach demonstrates good performance for the person segmentation task and further performs well for the estimation of the person action states walking, waving, and sitting. An ablation study provides insights about the individual channel contributions for the person segmentation task. The trained models, code and dataset are made publicly available. © 2025 IEEE.

关键词： Signal encoding

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(POSTER) Drone Detection and Localization Using Low-Cost Microphone Arrays and Convolutional Neural Networks 19

(POSTER) Drone Detection and Localization Using Low-Cost Mic...

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19th Annual International Conference on Distributed Computing in Smart Systems and the Internet of Things, DCOSS-IoT 2023

作者： Racinskis, Peteris Arents, Janis Greitans, Modris Institute of Electronics and Computer Science Robotics and Machine Perception Laboratory Latvia

ISBN: (纸本)9798350346497

This paper examines the possibility of using low-cost commercial off-the-shelf audio recording equipment in combination with machine learning techniques to discover the presence of hostile UAVs. A convolutional neural network (CNN) was trained to detect and localize two types of quadrotor drones using ground truth position data collected with motion capture equipment. System performance was evaluated on pre-recorded validation data sets and in realtime operation. In both cases, drones can be successfully detected and localized within the constrained working volumes studied, achieving angular accuracies in the 8-13° range. However, further work remains to be done before system feasibility in outdoor conditions can be established. © 2023 IEEE.

关键词： Drones

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CLASSIFICATION OF DIABETES USING ENSEMBLE MACHINE LEARNING TECHNIQUES

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Scalable Computing 2024年第4期25卷 3172-3180页

作者： Ashisha, G.R. Mary, X Anitha Raja, J Mahimai Electronics and Instrumentation Engineering Karunya Institute of Technology and Sciences Coimbatore India Robotics Engineering Karunya Institute of Technology and Sciences Coimbatore India Computer Science Engineering Karunya Institute of Technology and Sciences Coimbatore India

Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a variety of classifiers, including Light Gradient Boosting Machine (LightGBM), Gradient Boost classifier (GBC), and Random Forest (RF). The predictions are aggregated using voting methods to get a final classification result. The research is carried out using two benchmarking datasets: the Pima Indian Diabetes Dataset (PIDD) and the German Dataset. The Boruta technique is used to choose the best attributes from the datasets, while the Random Over Sampling approach balances the range of classes and eliminates abnormal data using the interquartile range approach. The findings showed that the combination of the Boruta feature selection algorithm and ensemble Voting Classifier performed better for both PIDD and German datasets with an accuracy of 93% and 90% respectively. These algorithms are evaluated and the maximum accuracy is produced using the combination of the Boruta feature selection algorithm and ensemble Voting Classifier. This research helps medical professionals in the early prediction of diabetes, reducing physician’s time. © 2024 SCPE.

关键词： Forecasting

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MESSI: Task Mapping and Scheduling Strategy for FPGA-based Heterogeneous Real-Time Systems

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ACM Transactions on Design Automation of Electronic Systems 2025年第3期30卷 1-29页

作者： Ahmadi-Pour, Sallar Saha, Sangeet Mcdonald-Maier, Klaus Drechsler, Rolf Institute of Computer Science University of Bremen Faculty 3 Mathematics and Computer Science Bremen Bremen Germany Robotics and Embedded Systems Research Group University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Cyber-Physical Systems DFKI Rheinland-Pfalz Kaiserslautern Germany

Continuous demands for improved performance within constrained resource budgets are driving a move from homogeneous to heterogeneous processing platforms for the implementation of today's Real-Time (RT) embedded systems. The applications executing on such systems are typically represented as a Precedence Task Graph (PTG), where a node represents a task or algorithm for one functionality and edges represent the complex interactions between multiple functionalities. Due to RT constraints, the task graph needs to be executed within a specified deadline. Although some existing studies have looked into solving this challenge, comprehensive studies that combine the theoretical features of RT task-graph mapping and scheduling with practical runtime architectural characteristics have mostly been ignored to date. Hence, in this article, we consider the challenge of scheduling an RT application modeled as a single PTG, with the objective of minimizing the overall execution time under Hardware (HW) resource and deadline constraints for heterogeneous Central Processing Unit (CPU) + Field Programmable Gate Array (FPGA) architectures. First, we introduce an optimal solution using Integer Linear Programming (ILP). However, this ILP-based optimal solution suffers from computational complexity and does not scale well even for moderately large problem sizes. Hence, we additionally propose heuristic algorithms for task mapping and scheduling. The efficiency of the proposed scheme, named MESSI, has been evaluated through experiments using PTG on a practical CPU+FPGA system regarding current technology restrictions. Our experiments demonstrate that performance gains of 55.6% and area usage reductions of 46.3% are possible compared to full Software (SW) and HW execution, respectively. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Integer programming

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FlowBot++: Learning Generalized Articulated Objects Manipulation via Articulation Projection 7

FlowBot++: Learning Generalized Articulated Objects Manipula...

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7th Conference on Robot Learning, CoRL 2023

作者： Zhang, Harry Eisner, Ben Held, David Robotics Institute School of Computer Science Carnegie Mellon University United States

Understanding and manipulating articulated objects, such as doors and drawers, is crucial for robots operating in human environments. We wish to develop a system that can learn to articulate novel objects with no prior interaction, after training on other articulated objects. Previous approaches for articulated object manipulation rely on either modular methods which are brittle or end-to-end methods, which lack generalizability. This paper presents FlowBot++, a deep 3D vision-based robotic system that predicts dense per-point motion and dense articulation parameters of articulated objects to assist in downstream manipulation tasks. FlowBot++ introduces a novel per-point representation of the articulated motion and articulation parameters that are combined to produce a more accurate estimate than either method on their own. Simulated experiments on the PartNet-Mobility dataset validate the performance of our system in articulating a wide range of objects, while real-world experiments on real objects' point clouds and a Sawyer robot demonstrate the generalizability and feasibility of our system in real-world scenarios. Videos are available on our anonymized website here. © 2023 Proceedings of Machine Learning Research. All Rights Reserved.

关键词： 3D Learning Articulated Objects Manipulation

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A Comparison of Prompt Engineering Techniques for Task Planning and Execution in Service robotics 23

A Comparison of Prompt Engineering Techniques for Task Plann...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Bode, Jonas Pätzold, Bastian Memmesheimer, Raphael Behnke, Sven Computer Science Institute Vi - Intelligent Systems and Robotics Lamarr Institute for Machine Learning and Artificial Intelligence Center for Robotics University of Bonn Autonomous Intelligent Systems group Germany

ISBN: (纸本)9798350373578

Recent advances in Large Language Models (LLMs) have been instrumental in autonomous robot control and human-robot interaction by leveraging their vast general knowledge and capabilities to understand and reason across a wide range of tasks and scenarios. Previous works have investigated various prompt engineering techniques for improving the performance of LLMs to accomplish tasks, while others have proposed methods that utilize LLMs to plan and execute tasks based on the available functionalities of a given robot platform. In this work, we consider both lines of research by comparing prompt engineering techniques and combinations thereof within the application of high-level task planning and execution in service robotics. We define a diverse set of tasks and a simple set of functionalities in simulation, and measure task completion accuracy and execution time for several state-of-the-art models. We make our code, including all prompts, available at https://***/AIS-Bonn/Prompt-Engineering. © 2024 IEEE.

关键词： Human robot interaction

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A study on Automated railway level crossing control system using FMCW radar for accident prevention 28

A study on Automated railway level crossing control system u...

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28th International Conference on Applied Electronics, AE 2023

作者： Greitans, Kristaps Maris Institute of Electronics and Computer Science Robotics and Machine Perceptions Laboratory Riga Latvia

ISBN: (纸本)9798350335538

This study paper explores the implementation of Intelligent Transportation Systems (ITS) in railway transportation, with a focus on level crossings, which have been a significant issue in railway safety. The paper discusses the limitations of current technologies such as laser technology and video surveillance and presents a novel solution utilizing a 79GHz Frequency Modulated Continuous Wave (FMCW) radar in combination with a CCTV camera for a dual sensor module. The FMCW radar is shown to be more accurate and cost-effective than previous technologies and can detect both large and small objects in the defined area at level crossings and difficult weather situations. The paper also presents a system architecture that takes into account vehicle detection time, which is crucial in reducing the risk of accidents and improving the safety of level crossings. This study aimed to compare the performance of different object detection modules under varying weather conditions. Three object detection methods were tested: a camera with the object detection algorithm Yolov5, FMCW radar with a built-in tracker, and manual observation. Four measurement sessions were conducted, each with a different combination of weather and time of day. The results showed that all modules performed similarly well under clear weather conditions, with the camera and radar modules detecting all vehicles approaching. However, under rainy and dark conditions, the radar module outperformed the camera, detecting 5 more objects. © 2023 IEEE.

关键词： Cameras

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APriCoT: Action Primitives based on Contact-state Transition for In-Hand Tool Manipulation 23

APriCoT: Action Primitives based on Contact-state Transition...

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23rd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2024

作者： Saito, Daichi Kanehira, Atsushi Sasabuchi, Kazuhiro Wake, Naoki Takamatsu, Jun Koike, Hideki Ikeuchi, Katsushi Institute of Science Tokyo Department of Computer Science Tokyo Japan Applied Robotics Research Microsoft RedmondWA United States

ISBN: (纸本)9798350373578

In-hand tool manipulation is an operation that not only manipulates a tool within the hand (i.e., in-hand manipulation) but also achieves a grasp suitable for a task after the manipulation. This study aims to achieve an in-hand tool manipulation skill through deep reinforcement learning. The difficulty of learning the skill arises because this manipulation requires (A) exploring long-term contact-state changes to achieve the desired grasp and (B) highly-varied motions depending on the contact-state transition. (A) leads to a sparsity of a reward on a successful grasp, and (B) requires an RL agent to explore widely within the state-action space to learn highly-varied actions, leading to sample inefficiency. To address these issues, this study proposes Action Primitives based on Contact-state Transition (APriCoT). APriCoT decomposes the manipulation into short-term action primitives by describing the operation as a contact-state transition based on three action representations (detach, crossover, attach). In each action primitive, fingers are required to perform short-term and similar actions. By training a policy for each primitive, we can mitigate the issues from (A) and (B). This study focuses on a fundamental operation as an example of in-hand tool manipulation: rotating an elongated object grasped with a precision grasp by half a turn to achieve the initial grasp. Experimental results demonstrated that ours succeeded in both the rotation and the achievement of the desired grasp, unlike existing studies. Additionally, it was found that the policy was robust to changes in object shape. © 2024 IEEE.

关键词： Hand tools

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