This paper presents a comprehensive framework for mission planning and execution with a heterogeneous multi-robot system, specifically designed to coordinate unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) in dynamic and unstructured environments. The proposed architecture evaluates the mission requirements, allocates tasks, and optimizes resource usage based on the capabilities of the available robots. It then executes the mission utilizing a decentralized control strategy that enables the robots to adapt to environmental changes and maintain formation stability in both 2D and 3D spaces. The framework's architecture supports loose coupling between its components, enhancing system scalability and maintainability. Key features include a robust task allocation algorithm, and a dynamic formation control mechanism, using a ROS 2 communication protocol that ensures reliable information exchange among robots. The effectiveness of this framework is demonstrated through a case study involving coordinated exploration and data collection tasks, showcasing its ability to manage missions while optimizing robot collaboration. This work advances the field of heterogeneous robotic systems by providing a scalable and adaptable solution for multi-robot coordination in challenging environments.
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http://dx.doi.org/10.3390/s24216881 | DOI Listing |
Phys Rev Lett
December 2024
Joint Center for Quantum Information and Computer Science, NIST and University of Maryland, College Park, Maryland 20742, USA.
A key objective in nuclear and high-energy physics is to describe nonequilibrium dynamics of matter, e.g., in the early Universe and in particle colliders, starting from the standard model of particle physics.
View Article and Find Full Text PDFJ Neurol
January 2025
Department of Neurology, LMU University Hospital, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany.
Background And Objective: Non-motor symptoms frequently develop throughout the disease course of Parkinson's disease (PD), and pose affected individuals at risk of complications, more rapid disease progression and poorer quality of life. Addressing such symptom burden, the 2023 revised "Parkinson's disease" guideline of the German Society of Neurology aimed at providing evidence-based recommendations for managing PD non-motor symptoms, including autonomic failure, pain and sleep disturbances.
Methods: Key PICO (Patient, Intervention, Comparison, Outcome) questions were formulated by the steering committee and refined by the assigned authors.
Adv Physiol Educ
January 2025
Assistant Professor, Department of Physiology, All India Institute of Medical Sciences, Deoghar, Jharkhand - 814152, India.
The integration of large language models (LLMs) in medical education offers both opportunities and challenges. While these AI-driven tools can enhance access to information and support critical thinking, they also pose risks like potential overreliance and ethical concerns. To ensure ethical use, students and instructors must recognize the limitations of LLMs, maintain academic integrity, handle data cautiously, and instructors should prioritize content quality over AI detection methods.
View Article and Find Full Text PDFEnviron Monit Assess
December 2024
Department of Botany, University of Jammu, Baba Saheb Ambedkar Road, Jammu Tawi, J&K, 180006, India.
The broad-scale inventories of alien species reveal macroecological patterns, but these often fall short in guiding local-level management strategies. Local authorities, tasked with on-the-ground management, require precise knowledge of the occurrence of invasive species tailored to their jurisdictional boundaries. What proves critical at the local scale may not hold the same significance at national or regional levels.
View Article and Find Full Text PDFEClinicalMedicine
January 2025
Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, UK.
Rapid advancements in medical AI necessitate targeted educational initiatives for clinicians to ensure AI tools are safe and used effectively to improve patient outcomes. To support decision-making among stakeholders in medical education, we propose three tiers of medical AI expertise and outline the challenges for medical education at different educational stages. Additionally, we offer recommendations and examples, encouraging stakeholders to adapt and shape curricula for their specific healthcare setting using this framework.
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