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A Kubernetes-Based Edge Architecture for Controlling the Trajectory of a Resource-Constrained Aerial Robot by Enabling Model Predictive Control

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arXiv 2023年

作者： Seisa, Achilleas Santi Satpute, Sumeet Gajanan Nikolakopoulos, George The Robotics and AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

In recent years, cloud and edge architectures have gained tremendous focus for offloading computationally heavy applications. From machine learning and Internet of Thing (IOT) to industrial procedures and robotics, cloud computing have been used extensively for data processing and storage purposes, thanks to its "infinite" resources. On the other hand, cloud computing is characterized by long time delays due to the long distance between the cloud servers and the machine requesting the resources. In contrast, edge computing provides almost real-time services since edge servers are located significantly closer to the source of data. This capability sets edge computing as an ideal option for real-time applications, like high level control, for resource-constrained platforms. In order to utilize the edge resources, several technologies, with basic ones as containers and orchestrators like Kubernetes, have been developed to provide an environment with many features, based on each application's requirements. In this context, this works presents the implementation and evaluation of a novel edge architecture based on Kubernetes orchestration for controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle (UAV) by enabling Model Predictive Control (MPC). © 2023, CC BY.

关键词： Model predictive control

Multimodal Dataset from Harsh Sub-Terranean Environment with Aerosol Particles for Frontier Exploration

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arXiv 2023年

作者： Kyuroson, Alexander Dahlquist, Niklas Stathoulopoulos, Nikolaos Viswanathan, Vignesh Kottayam Koval, Anton Nikolakopoulos, George The Robotics and AI Team Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

Algorithms for autonomous navigation in environments without Global Navigation Satellite System (GNSS) coverage mainly rely on onboard perception systems. These systems commonly incorporate sensors like cameras and Light Detection and Rangings (LiDARs), the performance of which may degrade in the presence of aerosol particles. Thus, there is a need of fusing acquired data from these sensors with data from Radio Detection and Rangings (RADARs) which can penetrate through such particles. Overall, this will improve the performance of localization and collision avoidance algorithms under such environmental conditions. This paper introduces a multimodal dataset from the harsh and unstructured underground environment with aerosol particles. A detailed description of the onboard sensors and the environment, where the dataset is collected are presented to enable full evaluation of acquired data. Furthermore, the dataset contains synchronized raw data measurements from all onboard sensors in Robot Operating System (ROS) format to facilitate the evaluation of navigation, and localization algorithms in such environments. In contrast to the existing datasets, the focus of this paper is not only to capture both temporal and spatial data diversities but also to present the impact of harsh conditions on captured data. Therefore, to validate the dataset, a preliminary comparison of odometry from onboard LiDARs is presented. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

Understanding URDF: A Survey Based on User Experience

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arXiv 2023年

作者： Tola, Daniella Corke, Peter The Department of Electrical and Computer Engineering Aarhus University Aarhus Denmark Centre for Robotics QUT Brisbane Australia

With the increasing complexity of robot systems, it is necessary to simulate them before deployment. To do this, a model of the robot’s kinematics or dynamics is required. One of the most commonly used formats for modeling robots is the Unified Robot Description Format (URDF). The goal of this article is to understand how URDF is currently used, what challenges people face when working with it, and how the community sees the future of URDF. The outcome can potentially be used to guide future research. This article presents the results from a survey based on 510 anonymous responses from robotic developers of different backgrounds and levels of experience. We find that 96.8% of the participants have simulated robots before, and of them 95.5% had used URDF. We identify a number of challenges and limitations that complicate the use of URDF, such as the inability to model parallel linkages and closed-chain systems, no real standard, lack of documentation, and a limited number of dynamic parameters to model the robot. Future perspectives for URDF are also determined, where 53.5% believe URDF will be more commonly used in the future, 12.2% believe other standards or tools will make URDF obsolete, and 34.4% are not sure what the future of URDF will be. Most participants agree that there is a need for better tooling to ensure URDF’s future use. © 2023, CC BY.

关键词： Robots

Integration of Convolutional Neural Networks for Real-Time Monitoring of Soil Health in Precision Agriculture 8

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Integration of Convolutional Neural Networks for Real-Time M...

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Sunil, Tummapudi Pachiappan, Krishnagandhi Senthilrajan, S. Nagendar, Y. Maaliw, Renato R. Pavin, C. Malla Reddy Engineering College Malkajgiri Department of Cse Hyderabad India Nandha Engineering College Electrical and Electronics Engineering Tamil Nadu Erode India Rajalakshmi Engineering College Department of Robotics& Automation Tamil Nadu Chennai India Sr University Department of School of Computer Science and Artificial Intelligence Telangana Warangal India Southern Luzon State University Department of Cse Quezon Lucban Philippines Yar Tech Services Biomedical Engineer Hyderabad India

ISBN: (纸本)9798350367904

Convolutional Neural Networks (CNNs) represent a revolutionary breakthrough in improving crop productivity and sustainability when integrated into real-time monitoring systems for soil health in precision agriculture. CNNs are used in a novel way to analyze soil parameters directly from high-resolution photos taken by in-field sensors, such as moisture content, p H levels, and nutrient concentration. To forecast soil health measures, a CNN model was trained and validated using a collection of soil samples. The model achieved an accuracy rate of approximately 9 2 %. The CNN-based technique shortens the time needed for soil health evaluation by almost 80 % when compared to conventional approaches that involve manual sampling and laboratory analysis, allowing for quick corrective actions in the field. Additionally, more accurate and timely soil health monitoring was found to boost crop output prediction accuracy by 15 %. An up to 20 % decrease in the amount of resources used, especially water and fertilizers, can be achieved by using real-time data processing to make educated decisions about irrigation, fertilizing, and pest management. The CNN model offers scalability and adaptability across a range of crop varieties and soil conditions, and it can be easily integrated into current agricultural IoT frameworks. All things considered, using CNNs for soil health monitoring offers an economical and effective way to improve the accuracy and output of contemporary farming methods. © 2024 IEEE.

关键词： Convolutional neural networks

Inverse Kinematics of Robotic Manipulators Using a New Learning-by-Example Method

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Inverse Kinematics of Robotic Manipulators Using a New Learn...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Jacket Demby’s Ramy Farag Guilherme N. DeSouza Department of Electrical Engineering and Computer Science (EECS) Vision-Guided and Intelligent Robotics (ViGIR) Laboratory University of Missouri-Columbia Columbia Missouri

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Inverse Kinematics (IK) is one of the most fundamental challenges in robotics. It refers to the process of determining the joint configurations required to achieve the desired position and orientation (pose) of a robot end-effector. Although numerous Data-Driven (DD) IK solvers have demonstrated encouraging results, they have not achieved the same accuracy when compared to other IK methods for complex robot configurations (e.g., numerical methods for higher Degrees of Freedom (DoF)). In this work, we propose a new Learning-by-Example method, and show that such a scheme considerably improves the IK learning results when compared to other DD learners. In our approach, the network input incorporates an example of joint-pose pair along with the query pose to predict the desired robot joint configuration. We show that the example joint-pose pair does not need to be too close to the query – i.e. example and query can be as far as 20 degrees apart in the joint configuration space. Furthermore, we investigate the utilization of residual and dense skip connections in Multilayer Perceptron for DDIK solvers and employ the resulting networks for two redundant robotic manipulators: a 7-DoF-7R commensurate robot and a 7DoF-2RP4R incommensurate robot. Our experimental results show that the resulting DDIK solver can reliably predict IK solutions with accuracy better than 1mm in position and 1deg in orientation.

关键词： Hands Accuracy Kinematics Network architecture Multilayer perceptrons Numerical models Reliability Collision avoidance Robots Intelligent robots

Evaluation of Lidar-based 3D SLAM algorithms in SubT environment

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arXiv 2023年

作者： Koval, Anton Kanellakis, Christoforos Nikolakopoulos, George The Robotics and AI Group Department of Computer Electrical and Space Engineering Luleå University of Technology Luleå Sweden

Autonomous navigation of robots in harsh and GPS denied subterranean (SubT) environments with lack of natural or poor illumination is a challenging task that fosters the development of algorithms for pose estimation and mapping. Inspired by the need for real-life deployment of autonomous robots in such environments, this article presents an experimental comparative study of 3D SLAM algorithms. The study focuses on state-of-the-art Lidar SLAM algorithms with open-source implementation that are i) lidar-only like BLAM, LOAM, A-LOAM, ISC-LOAM and hdl graph slam, or ii) lidar-inertial like LeGO-LOAM, Cartographer, LIO-mapping and LIO-SAM. The evaluation of the methods is performed based on a dataset collected from the Boston Dynamics Spot robot equipped with 3D lidar Velodyne Puck Lite and IMU Vectornav VN-100, during a mission in an underground tunnel. In the evaluation process poses and 3D tunnel reconstructions from SLAM algorithms are compared against each other to find methods with most solid performance in terms of pose accuracy and map quality. © 2023, CC BY-NC-ND.

关键词： Optical radar

SPVSoAP3D: A Second-order Average Pooling Approach to enhance 3D Place Recognition in Horticultural Environments

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SPVSoAP3D: A Second-order Average Pooling Approach to enhanc...

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： T. Barros C. Premebida S. Aravecchia C. Pradalier U. J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal IRL2958 GeorgiaTech-CNRS Metz France

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

3D LiDAR-based place recognition has been extensively researched in urban environments, yet it remains underexplored in agricultural settings. Unlike urban contexts, horticultural environments, characterized by their permeability to laser beams, result in sparse and overlapping LiDAR scans with suboptimal geometries. This phenomenon leads to intra-and inter-row descriptor ambiguity. In this work, we address this challenge by introducing SPVSoAP3D, a novel modeling approach that combines a voxel-based feature extraction network with an aggregation technique based on a second-order average pooling operator, complemented by a descriptor enhancement stage. Furthermore, we augment the existing HORTO-3DLM dataset by introducing two new sequences derived from horticultural environments. We evaluate the performance of SPVSoAP3D against state-of-the-art (SOTA) models, including OverlapTransformer, PointNetVLAD, and LOGG3D-Net, utilizing a cross-validation protocol on both the newly introduced sequences and the existing HORTO-3DLM dataset. The findings indicate that the average operator is more suitable for horticultural environments compared to the max operator and other first-order pooling techniques. Additionally, the results highlight the improvements brought by the descriptor enhancement stage. The code is publicly available at https://***/Cybonic/***

关键词： Solid modeling Three-dimensional displays Protocols Laser radar Urban areas Green products Laser modes Permeability Laser beams Intelligent robots

SPVSoAP3D: A Second-order Average Pooling Approach to enhance 3D Place Recognition in Horticultural Environments

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arXiv 2024年

作者： Barros, T. Premebida, C. Aravecchia, S. Pradalier, C. Nunes, U.J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal IRL2958 GeorgiaTechCNRS Metz57070 France

3D LiDAR-based place recognition has been extensively researched in urban environments, yet it remains underexplored in agricultural settings. Unlike urban contexts, horticultural environments, characterized by their permeability to laser beams, result in sparse and overlapping LiDAR scans with suboptimal geometries. This phenomenon leads to intra- and inter-row descriptor ambiguity. In this work, we address this challenge by introducing SPVSoAP3D, a novel modeling approach that combines a voxel-based feature extraction network with an aggregation technique based on a second-order average pooling operator, complemented by a descriptor enhancement stage. Furthermore, we augment the existing HORTO-3DLM dataset by introducing two new sequences derived from horticultural environments. We evaluate the performance of SPVSoAP3D against state-of-the-art (SOTA) models, including OverlapTransformer, PointNetVLAD, and LOGG3D-Net, utilizing a cross-validation protocol on both the newly introduced sequences and the existing HORTO-3DLM dataset. The findings indicate that the average operator is more suitable for horticultural environments compared to the max operator and other first-order pooling techniques. Additionally, the results highlight the improvements brought by the descriptor enhancement stage. The code is publicly available at https://***/Cybonic/*** © 2024, CC BY.

关键词： Feature extraction

Leveraging Predictions in Power System Frequency Control: An Adaptive Approach

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Leveraging Predictions in Power System Frequency Control: An...

IEEE Conference on Decision and Control

作者： Wenqi Cui Guanya Shi Yuanyuan Shi Baosen Zhang Department of Electrical and Computer Engineering University of Washington Seattle WA USA Robotics Institute Carnegie Mellon University PA USA Department of Electrical and Computer Engineering University of California San Diego

Ensuring the frequency stability of electric grids with increasing renewable resources is a key problem in power system operations. In recent years, a number of advanced controllers have been designed to optimize frequency control. These controllers, however, almost always assume that the net load in the system remains constant over a sufficiently long time. Given the intermittent and uncertain nature of renewable resources, it is becoming important to explicitly consider net load that is time-varying. This paper proposes an adaptive approach to frequency control in power systems with significant time-varying net load. We leverage the advances in short-term load forecasting, where the net load in the system can be accurately predicted using weather and other features. We integrate these predictions into the design of adaptive controllers, which can be seamlessly combined with most existing controllers including conventional droop control and emerging neural network-based controllers. We prove that the overall control architecture achieves frequency restoration decentralizedly. Case studies verify that the proposed method improves both transient and frequency-restoration performances compared to existing approaches.

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