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IEEE International Conference on Mechatronics and Automation

作者： Liwei Shi Rui Xiao Shuxiang Guo Ping Guo Shaowu Pan Yanlin He Key Laboratory of Convergence Medical Engineering System and Healthcare Technology the Ministry of Industry and Infor-motion Technology. Beiiino Institute ot Technoloov Beiiino China Key Laboratory Biomim tic RobotsSystems Ministry of Education Beijing Institute of Technology China Faculty of Engineering Kagawa University Takamatsu Kagawa Japan Beijing Institute of Technology Beijing Beijing CN

ISBN: (纸本)9781479970995

As one of the most effective tools for exploring the ocean, automatic underwater vehicles have attracted a lot of attentions for years. But some key problems have not been solved properly. It is especially difficult to design underwater vehicles in small size. In this paper, three inertial sensors were adopted to fabricate an attitude estimation system, which provided posture information for our amphibious spherical robot to realize motion control and autonomous navigation. The pitch, roll and heading angel were acquired from current robot attitude matrix, which was calculated from the quaternion algorithm. And the attitude was corrected by the fusion of accelerometer and magnetic sensor. Experimental results verified the validation and precision of the robotic attitude estimation system. It has manifested that the system is effective to realize the robot control and navigation.

关键词： Robot sensing systems Mathematical model Magnetic sensors sensor systems Quaternions

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autonomous Aerial Swarming in GNSS-denied Environments with High Obstacle Density

Autonomous Aerial Swarming in GNSS-denied Environments with ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Afzal Ahmad Viktor Walter Pavel Petráček Matěj Petrlík Tomáš Báča David Žaitlík Martin Saska Faculty of Electrical Engineering Czech Technical University in Prague Prague

The compact flocking of relatively localized Un-manned Aerial Vehicles (UAVs) in high obstacle density areas is discussed in this paper. The presented work tackles realistic scenarios in which the environment map is not known apriori and the use of a global localization system and communication infrastructure is difficult due to the presence of obstacles. To achieve flocking in such a constrained environment, we propose a fully decentralized, bio-inspired control law that uses only onboard sensor data for safe flocking through the environment without any communication with other agents. In the proposed approach, each UAV agent uses onboard sensors to self-localize and estimate the relative position of other agents in its local reference frame. The usability and performance of the proposed approach were verified and evaluated using various experiments in a realistic robotic simulator and a natural forest. The presented experiments also validate the utility of onboard relative localization for autonomous multi-UAV applications in the absence of global localization information and communication.

关键词： Location awareness Global navigation satellite system Navigation Conferences Green products Forestry Robot sensing systems

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Map API - scalable decentralized map building for robots

Map API - scalable decentralized map building for robots

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IEEE International Conference on robotics and Automation (ICRA)

作者： Titus Cieslewski Simon Lynen Marcin Dymczyk Stéphane Magnenat Roland Siegwart ETH Zurich Autonomous Systems Lab

Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.

关键词： Distributed databases Spatial indexes Robot sensing systems Buildings Bandwidth Protocols

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Communication Infrastructure and Cloud Computing in robotic Vessel as-a-Service Application

Communication Infrastructure and Cloud Computing in Robotic ...

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OCEANS

作者： Cosmin Delea Emanuele Coccolo Salvador Fernandez Covarrubias Filippo Campagnaro Federico Favaro Roberto Francescon Vincent Schneider Johannes Oeffner Michele Zorzi Fraunhofer Center for Maritime Logistics and Service Hamburg Germany University of Padova Padova Italy Wireless and More srl Padova Italy

ISBN: (数字)9781728154466

ISBN: (纸本)9781728184098

The current trend in robotics is to enhance robustness against uncertainties through complex modern control methods, applied to either single- or multi-agent systems. While indeed these approaches provide noticeable performance improvements, their implementation requires, among others, enclosed environments, where the operator has full access to the control systems of the robot during execution. With constant improvement of the Internet of Things, real-time systems needed different software architectures and communication systems to enable their remote operation, with the same degree of flexibility when operating the systems in-situ. The paradigm shifted towards cloud computing, which moved the computational effort needed for various real-time processing algorithms on the server-sided application and focused on having lightweight interfaces between components, with the exception of the lower-level control loops, which remained within the processing units of the robotic systems. This paper describes the software architecture and communication system for a state-of-the-art service to enable, support and monitor different robotics applications. The case study presents all features of a small-sized but scalable robotics application, having multiple challenges when building a service oriented platform: autonomous robots, over- and underwater long-distance communication, sensor fusion and command and control of multiple users. The goal is to create a centralised cloud computing environment, with decentralised microservices and redundant resources, supporting a plethora of port-specific operations conducted with the help of waterborne robotics with different equipment configurations, that can be remotely operated and monitored in real time over cross-platform, lightweight applications.

关键词： Cloud computing Software architecture Communication systems Robot sensing systems Real-time systems Robots Monitoring

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Distributed Connectivity-Maintenance control of a Team of Unmanned Aerial Vehicles Using Supervised Deep Learning

Distributed Connectivity-Maintenance Control of a Team of Un...

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International Conference on control, Automation and robotics (ICCAR)

作者： Muhammad Sunny Nazeer Marco Aggravi Paolo Robuffo Giordano Claudio Pacchierotti Department of Mechanical Engineering National University of Singapore Singapore CNRS Univ Rennes Inria IRISA Rennes France

ISBN: (数字)9798350373172

ISBN: (纸本)9798350373189

This paper presents the design of a decentralized connectivity-maintenance technique for the teleoperation of a team of multiple Unmanned Aerial Vehicles (UAV). Different from current, typical connectivity-maintenance approaches, the proposed technique uses machine learning to attain significantly more scalability in terms of number of UAVs that can be part of the robotic team. It uses Supervised Deep Learning (SDL) with Artificial Neural Networks (ANN), so that each robot can extrapolate the necessary actions for keeping the team connected in one computation step, regardless the size of the team. We compared the performance of our proposed approach vs. a state-of-the-art model-based connectivity-maintenance algorithm when managing a team composed of two, four, six, and ten aerial mobile robots. Results show that our approach can keep the computational cost almost constant as the number of drones increases, reducing it significantly with respect to model-based techniques. For example, our SDL approach needs 83% less time than a state-of-the-art model-based connectivity-maintenance algorithm when managing a team of ten drones.

关键词： Training Deep learning Recurrent neural networks Computational modeling sensor phenomena and characterization Robot sensing systems autonomous aerial vehicles

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iKalibr: Unified Targetless Spatiotemporal Calibration for Resilient Integrated Inertial systems

arXiv

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arXiv 2024年

作者： Chen, Shuolong Li, Xingxing Li, Shengyu Zhou, Yuxuan Yang, Xiaoteng Wuhan University Wuhan430070 China

The integrated inertial system, typically integrating an IMU and an exteroceptive sensor such as radar, LiDAR, and camera, has been widely accepted and applied in modern robotic applications for ego-motion estimation, motion control, or autonomous exploration. To improve system accuracy, robustness, and further usability, both multiple and various sensors are generally resiliently integrated, which benefits the system performance regarding failure tolerance, perception capability, and environment compatibility. For such systems, accurate and consistent spatiotemporal calibration is required to maintain a unique spatiotemporal framework for multi-sensor fusion. Considering that most existing calibration methods (i) are generally oriented to specific integrated inertial systems, (ii) often focus on spatial-only determination, (iii) usually require artificial targets, lacking convenience and usability, we propose iKalibr: a unified targetless spatiotemporal calibration framework for resilient integrated inertial systems, which overcomes the above issues, and enables both accurate and consistent calibration. Altogether four commonly employed sensors are supported in iKalibr currently, namely IMU, radar, LiDAR, and camera. The proposed method starts with a rigorous and efficient dynamic initialization, where all parameters in the estimator would be accurately recovered. Subsequently, several continuous-time batch optimizations are conducted to refine the initialized parameters toward better states. Sufficient real-world experiments were conducted to verify the feasibility and evaluate the calibration performance of iKalibr. The results demonstrate that iKalibr can achieve accurate resilient spatiotemporal calibration. We open-source our implementations at (https://***/Unsigned-Long/iKalibr) to benefit the research community. Copyright © 2024, The Authors. All rights reserved.

关键词： Continuous time systems

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Competing Objective Optimization in Networked Swarm systems

Competing Objective Optimization in Networked Swarm Systems

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Shankarachary Ragi Shawon Dey Azam Md Ali Hans D. Mittelmann Department of Electrical Engineering South Dakota School of Mines and Technology Rapid City SD USA School of Mathematical and Statistical Sciences Arizona State University Tempe AZ USA

ISBN: (数字)9781728114163

ISBN: (纸本)9781728114170

In this paper, we develop a decentralized collaborative sensing algorithm where the sensors are located on-board autonomous unmanned aerial vehicles. We develop this algorithm in the context of a target tracking application, where the objective is to maximize the tracking performance measured by the mean-squared error between the target state estimate and the ground truth. The tracking performance depends on the quality of the target measurements made at the sensors, which depends on the relative location of the sensors with respect to the target. Our goal is to control the motion of the swarm of vehicles with on-board sensors to maximize target tracking performance. Each sensor (on-board the vehicle) generates local noisy measurements of the target location, and the sensors maintain and update target state estimates via Bayesian data fusion rules using local measurements and the information received from neighboring sensors. The quality of the data fusion depends on the network graph over which the sensors exchange information, and this determines the overall target tracking performance. We also assume that each sensor is powered by a limited energy source; which we assume is drained by how frequently sensors exchange information. The goal is to optimize the collective motion of the vehicles/sensors (also determines the network graph connectivity) such that the mean-squared target tracking error and the network energy costs are jointly minimized. This problem belongs to a class of hard optimization problems called conflicting objective limited resource optimization (COLRO). We develop a fast heuristic algorithm, using dynamic programming principles, to solve this COLRO problem in real-time.

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A multi-robot system for the detection of explosive devices

arXiv

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arXiv 2024年

作者： Hasselmann, Ken Malizia, Mario Caballero, Rafael Polisano, Fabio Govindaraj, Shashank Stigler, Jakob Ilchenko, Oleksii Bajic, Milan De Cubber, Geert The Robotics and Autonomous Systems Lab The Royal Military Academy of Belgium Brussels Belgium Sevilla Spain Space Applications Services NV/SA Sint-Stevens-Woluwe Belgium Fraunhofer Insitute for High-Speed-Dynamics Ernst-Mach-Institut Freiburg Germany Lightnovo ApS Birkerød Denmark Scientific Council HCR—Center for Testing Development and Training Zagreb Croatia

In order to clear the world of the threat posed by landmines and other explosive devices, robotic systems can play an important role. However, the development of such field robots that need to operate in hazardous conditions requires the careful consideration of multiple aspects related to the perception, mobility, and collaboration capabilities of the system. In the framework of a European challenge, the Artificial Intelligence for Detection of Explosive Devices - eXtended (AIDEDeX) project proposes to design a heterogeneous multi-robot system with advanced sensor fusion algorithms. This system is specifically designed to detect and classify improvised explosive devices, explosive ordnances, and landmines. This project integrates specialised sensors, including electromagnetic induction, ground penetrating radar, X-Ray backscatter imaging, Raman spectrometers, and multimodal cameras, to achieve comprehensive threat identification and localisation. The proposed system comprises a fleet of unmanned ground vehicles and unmanned aerial vehicles. This article details the operational phases of the AIDEDeX system, from rapid terrain exploration using unmanned aerial vehicles to specialised detection and classification by unmanned ground vehicles equipped with a robotic manipulator. Initially focusing on a centralised approach, the project will also explore the potential of a decentralised control architecture, taking inspiration from swarm robotics to provide a robust, adaptable, and scalable solution for explosive detection. © 2024, CC BY-NC-ND.

关键词： Explosives detection

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Navigation control of a mobile robotic system in semi-structured and dynamic environment for controlled dose delivery of pesticides

Navigation control of a mobile robotic system in semi-struct...

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International Conference on Trends in Automation, Communications and Computing Technology

作者： Manoj Kumar Patel Hemant Kumar Sahoo Chiranjiv Singh Kathuria Manoj Kumar Nayak Aneesh Singla C Ghanshyam AcSIR-CSIO Chandigarh 160 030 CSIR-CSIO Chandigarh 160 030 CSIR-CSIO Chandigarh 160 030 INDIA Thapar University Patiala Punjab 147 004 INDIA

ISBN: (纸本)9781467366687

A major portion of the autonomous mobile robotics research has been in the development of reliable means for navigation and control of autonomous systems in a complex, irregular, unstructured and dynamic environment for farm duties such as seeding, pruning, pesticide spraying, reaping etc. Several practices have been established by numerous researchers in the motion planning of mobile robots. This paper presents an ultrasonic multi-sensor fusion based technique for navigation and control of the autonomous mobile robotic system for collision avoidance, target detection and canopy mapping. The autonomous system is a combination of real time monitoring and navigation, detection of the target and canopy mapping which delivers controlled dose of pesticide in a targeted manner according to the information gathered from the sensory attributes of precision farming data set through online and real time monitoring of the objects. Real time monitoring and navigation control, complex algorithm, data acquisition and storage, calculation and processing are designed with motors and sensors interfaced with microcontroller. Test results showed that the ultrasonic sensor fusion technique was capable of performing satisfactory guidance in dynamic environment and desired paths. The sensor fusion navigation was capable of compensating large error and resulting in satisfactory agricultural vehicle guidance.

关键词： Navigation control Multi-sensor fusion Canopy Mapping Precision Farming Data Set (PFDS) Precision Agriculture Data Set (PADS)

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采摘机器人全果园视觉感知及自主作业综述

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智慧农业（中英文） 2024年第5期6卷 20-39页

作者：陈明猷罗陆锋刘威韦慧玲王金海卢清华骆少明佛山大学机电工程与自动化学院广东佛山528231

[目的/意义]采摘机器人是智慧农业的重要组成部分,其感知、规划、控制相关基础方法理论目前已有系统化研究。然而,构建具备全果园“感知-移动-采摘”一体化作业能力的实用型采摘系统仍面临诸多挑战。针对该问题,本文调研并报道了本领域... 详细信息

[目的/意义]采摘机器人是智慧农业的重要组成部分,其感知、规划、控制相关基础方法理论目前已有系统化研究。然而,构建具备全果园“感知-移动-采摘”一体化作业能力的实用型采摘系统仍面临诸多挑战。针对该问题,本文调研并报道了本领域近期案例,将全果园自主作业的关键技术划分为局部目标感知、全局地图构建和自主作业行为规划三个子问题并进行综述。[进展]首先回顾了近距离、局部范围内水果目标的精细视觉感知方法,包括基于低级特征融合、高级特征学习、RGB-D信息融合,以及多视角信息融合的4种方法;介绍与分析了全局尺度下的果园地图构建与大规模场景视觉感知案例;在感知的基础上,调研分析采摘机器人自主作业行为规划方法,包括底盘移动路径规划、机械臂视点规划与避障路径规划等方面的最新研究;最后对采摘机器人自主作业系统构建案例进行报道与分析。[结论/展望]感知、移动、采摘模块的高效协同是实现采摘机器人从基础功能样机进一步迈向实用型机器的关键,已有的视觉感知、规划与控制算法的鲁棒性与稳定性均需增强,协同程度需进一步提高。此外,提及了采摘机器人应用的几个开放性研究问题,并描述了其未来发展趋势。

关键词：采摘机器人自主作业局部感知全局建图行为规划

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