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Collision-free landing of multiple UAVs on moving ground vehicles using time-varying control barrier functions

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arXiv 2025年

作者： Sankaranarayanan, Viswa Narayanan Saradagi, Akshit Satpute, Sumeet Nikolakopoulos, George Robotics and Artificial Intelligence Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

In this article, we present a centralized approach for the control of multiple unmanned aerial vehicles (UAVs) for landing on moving unmanned ground vehicles (UGVs) using control barrier functions (CBFs). The proposed control framework employs two kinds of CBFs to impose safety constraints on the UAVs’ motion. The first class of CBFs (LCBF) is a three-dimensional exponentially decaying function centered above the landing platform, designed to safely and precisely land UAVs on the UGVs. The second set is a spherical CBF (SCBF), defined between every pair of UAVs, which avoids collisions between them. The LCBF is time-varying and adapts to the motions of the UGVs. In the proposed CBF approach, the control input from the UAV’s nominal tracking controller designed to reach the landing platform is filtered to choose a minimally-deviating control input that ensures safety (as defined by the CBFs). As the control inputs of every UAV are shared in establishing multiple CBF constraints, we prove that the control inputs are shared without conflict in rendering the safe sets forward invariant. The performance of the control framework is validated through a simulated scenario involving three UAVs landing on three moving targets. © 2025, CC BY-NC-ND.

关键词： Drones

The Extended Optimal Control Problem and Numerical Techniques of Its Solving 9

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The Extended Optimal Control Problem and Numerical Technique...

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Diveev, Askhat Barabash, Artem Russian Academy of Sciences Federal Research Center 'Computer Science and Control' Department of Robotics Control Vavilova str. 44 Moscow119333 Russia Federal Research Center 'Computer Science and Control' of the Russian Academy of Sciences Vavilova str. 44 Moscow119333 Russia

ISBN: (纸本)9798350311402

A new numerical method for solving the optimal control problem in class practically implemented solutions is presented. The method uses an approach of the synthesized control and takes account uncertainties of initial states. Like as synthesized control the method move a control object changing location of stable equilibrium point. As a result it chooses from all possible optimal synthesized controls such, that less sensitive to changes of initial states. As an example, the optimal control problem of quadcopter with complex phase constraints in the form of obstacle areas and narrow bottle neck is considered. To solve this problem firstly the synthesis control problem is solved for obtaining stable equilibrium point in the state space by symbolic regression. After that positions of stable equilibrium points are searched according to source functional from the optimal control problem. As an example, the optimal control problem of quadcopter with complex phase constraints in the form of obstacle areas and narrow bottle neck is considered. To solve this problem firstly the synthesis control problem is solved for obtaining stable equilibrium point in the state space by symbolic regression. After that positions of stable equilibrium points are searched according to source functional from the optimal control problem. Additionally at the search of equilibrium point positions the goal functional is calculated as a sum of fuctional values for all given points of initial states. © 2023 IEEE.

关键词： Bottles

Clustering-Based Artificial Rabbit Optimization: Approach for Enhanced Data Clustering

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Clustering-Based Artificial Rabbit Optimization: Approach fo...

Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Balqees Ayal-Awwad Jamil Al-Sawwa Mohammad Almseidin Maen Alzubi Computer Science Department Tafila Technical University Tafila Jordan Department of Robotics and Artificial Intelligence Jadara University Irbid Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Data clustering is a data mining task used to group data points based on their similarity. The goal of clustering is to divide $N$ data points into $K$ clusters while minimizing the intra-cluster distance and maximizing the inter-cluster distance. Data clustering can be viewed as a combinatorial optimization problem, which is known as NP-hard. Thus, nature-inspired optimization algorithms have been applied to find better solutions for clustering problems. In this paper, the clustering-based artificial rabbit optimization (CARO) method is proposed. The main idea is to find the optimal centroids for the $K$ clusters by minimizing the sum of squared errors. Using seven benchmark datasets, the experimental results showed that the CARO achieved outstanding results compared to six nature-inspired optimization algorithms in terms of purity and entropy.

关键词： Rabbits Clustering algorithms Machine learning Benchmark testing Feature extraction Entropy Reliability Data mining Optimization Convergence

A Metaheuristic Technique for Cluster-Based Feature Selection of DNA Methylation Data for Cancer

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computers, Materials & Continua 2023年第2期74卷 2817-2838页

作者： Noureldin Eissa Uswah Khairuddin Rubiyah Yusof Ahmed Madani Centre for Artificial Intelligence&Robotics Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia Department of Computer Engineering Arab Academy for ScienceTechnology and Maritime TransportCairo2033Egypt Department of Mechanical Precision Engineering Malaysia-Japan International Institute of TechnologyUniversiti Teknologi MalaysiaKuala Lumpur54100Malaysia

Epigenetics is the study of phenotypic variations that do not alter DNA *** epigenetics has grown rapidly over the past few years as epigenetic alterations exist in all human *** of these alterations is DNA methylation;an epigenetic process that regulates gene expression and often occurs at tumor suppressor gene loci in ***,studying this methylation process may shed light on different gene functions that cannot otherwise be interpreted using the changes that occur in DNA ***,microarray technologies;such as Illumina Infinium BeadChip assays;are used to study DNA methylation at an extremely large number of varying *** each DNA methylation site,a beta value(β)is used to reflect the methylation ***,clustering this data from various types of cancers may lead to the discovery of large partitions that can help objectively classify different types of cancers aswell as identify the relevant loci without user *** study proposed a Nested Big Data Clustering Genetic Algorithm(NBDC-GA);a novel evolutionary metaheuristic technique that can perform cluster-based feature selection based on the DNA methylation *** efficacy of the NBDC-GA was tested using real-world data sets retrieved from The Cancer Genome Atlas(TCGA);a cancer genomics program created by the NationalCancer Institute(NCI)and the NationalHuman Genome Research *** performance of the NBDC-GA was then compared with that of a recently developed metaheuristic Immuno-Genetic Algorithm(IGA)that was tested using the same data *** NBDC-GA outperformed the IGA in terms of convergence ***,the NBDC-GA produced a more robust clustering configuration while simultaneously decreasing the dimensionality of features to a maximumof 67%and of 94.5%for individual cancer type and collective cancer,*** proposed NBDC-GA was also able to identify two chromosomes with highly contrastingDNAmethylations activities th

关键词： Cancer clustering DNA methylation feature selection metaheuristic technique the cancer genome atlas

Learning in deep factor graphs with Gaussian belief propagation 24

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Learning in deep factor graphs with Gaussian belief propagat...

Proceedings of the 41st International Conference on Machine Learning

作者： Seth Nabarro Mark Van Der Wilk Andrew J. Davison Dyson Robotics Lab Imperial College London UK Department of Computer Science University of Oxford UK

We propose an approach to do learning in Gaussian factor graphs. We treat all relevant quantities (inputs, outputs, parameters, activations) as random variables in a graphical model, and view training and prediction as inference problems with different observed nodes. Our experiments show that these problems can be efficiently solved with belief propagation (BP), whose updates are inherently local, presenting exciting opportunities for distributed and asynchronous training. Our approach can be scaled to deep networks and provides a natural means to do continual learning: use the BP-estimated posterior of the current task as a prior for the next. On a video denoising task we demonstrate the benefit of learnable parameters over a classical factor graph approach and we show encouraging performance of deep factor graphs for continual image classification.

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Performance Assessment of Lidar Odometry Frameworks: A Case Study at the Australian Botanic Garden Mount Annan

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arXiv 2024年

作者： Ouazghire, Mohamed Mourad Berrio, Julie Stephany Shan, Mao Worrall, Stewart Department of Computer Science Technical University of Darmstadt Germany The Australian Center For Robotics The University of Sydney Australia

Autonomous vehicles are being tested in diverse environments around the world. However, there is a notable gap in the evaluation of datasets representing natural unstructured environments such as forests or gardens. To address this, we present a study on localization at the Mount Annan Botanic Garden. This area encompasses open grassy areas, paved pathways, and densely vegetated sections with trees and other objects. The data set was recorded using a 128-beam LiDAR sensor and GPS and IMU readings to track the ego vehicle. This paper evaluates the performance of two state-of-the-art LiDAR-inertial odometry frameworks, COIN-LIO and LIO-SAM, on this dataset. We analyze trajectory estimates in both horizontal and vertical dimensions and assess relative translation and yaw errors over varying distances. Our findings reveal that while both frameworks perform adequately in the vertical plane, COIN-LIO demonstrates superior accuracy in the horizontal plane, particularly over extended trajectories. In contrast, LIO-SAM shows an increase in drift and yaw errors over longer distances. © 2024, CC BY-NC-ND.

关键词： Trajectories

Transformers for Image-Goal Navigation

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arXiv 2024年

作者： Pelluri, Nikhilanj V. Minnesota Robotics Institute Department of Computer Science and Engineering University of Minnesota Twin Cities United States

Visual perception and navigation have emerged as major focus areas in the field of embodied artificial intelligence. We consider the task of image-goal navigation, where an agent is tasked to navigate to a goal specified by an image, relying only on images from an onboard camera. This task is particularly challenging since it demands robust scene understanding, goal-oriented planning and long-horizon navigation. Most existing approaches typically learn navigation policies reliant on recurrent neural networks trained via online reinforcement learning. However, training such policies requires substantial computational resources and time, and performance of these models is not reliable on long-horizon navigation. In this work, we present a generative Transformer based model that jointly models image goals, camera observations and the robot’s past actions to predict future actions. We use state-of-the-art perception models and navigation policies to learn robust goal conditioned policies without the need for real-time interaction with the environment. Our model demonstrates capability in capturing and associating visual information across long time horizons, helping in effective navigation. © 2024, CC BY.

关键词： Navigation

Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

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arXiv 2024年

作者： Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George the Robotics and AI Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A∗ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

Communication-Efficient Multi-Modal Federated Learning via Dynamic Client-Modality Matching

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Communication-Efficient Multi-Modal Federated Learning via D...

IEEE International Conference on Big Data

作者： Tan Li Yanming Gong Hai Liu Zhen Chen Linqi Song Department of Computer Science The Hang Seng University of Hong Kong Centre for Artificial Intelligence and Robotics (CAIR) HKISI CAS Department of Computer Science City University of Hong Kong

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Multi-modal federated learning (MFL) offers the advantage of aggregating models from diverse data modalities to obtain a more powerful fused model while preserving data privacy. However, MFL faces three key challenges: 1) Communication overhead - only a limited number of clients can participate in training due to communication budget constraints; 2) Modality heterogeneity - different modalities contribute unequally to the fused model; 3) Client heterogeneity - clients exhibit variations in data quantity and quality across modalities. To address these challenges, we formulate a joint client-modality selection problem under communication budget constraints. The goal is to determine the participating clients and their uploaded modalities in each communication round, maximizing the performance of the fused model given a limited communication budget. We propose a dynamic many-to-many matching algorithm with two quota budgeting strategies: 1) Round-aware Modality Budgeting (RMB) determines the total number of uploaded modality models per round based on the current training process (i.e., how close the model is to convergence). 2) Modality-aware Client Allocation (MCB) adaptively allocates client quota for each modality by balancing the modality’s contribution to the fusion model against its model size. After quota budgeting, we construct preference lists for clients and modalities to find a stable many-to-many matching of (client, modality) pairs. Experiments demonstrate that our algorithm achieves better model performance than baselines under the same communication budget, validating the benefits of dynamic budget allocation and client scheduling.

关键词： Training Adaptation models Federated learning Heuristic algorithms Dynamic scheduling Linear programming Data models Resource management Faces Convergence