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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-10312023-164214


Tipo di tesi
Tesi di laurea magistrale
Autore
PIACENTINI, GIACOMO
URN
etd-10312023-164214
Titolo
Development in autopilot environment of a swarm robotics algorithm for hole detection in WSN
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
ARTIFICIAL INTELLIGENCE AND DATA ENGINEERING
Relatori
relatore Prof. Cimino, Mario Giovanni Cosimo Antonio
correlatore Prof.ssa Giada, Simionato
Parole chiave
  • algorithms
  • autonomous drones
  • autopilot software
  • drone technology
  • drones
  • Gazebo
  • hole detection
  • industrial
  • JMavSim
  • MAVSDK
  • mission management
  • multi-drone scenarios
  • open-source
  • PX4
  • sensor deployment
  • sensor deployment strategies
  • simulation environment
  • simulator
  • swarm robotics
  • swarm robotics applications
Data inizio appello
17/11/2023
Consultabilità
Non consultabile
Data di rilascio
17/11/2093
Riassunto
This thesis exploresinto the development of hole detection algorithms for swarm robotics, with a focus on advanced simulation environments. It explores the integration and comparative analysis of PX4 Autopilot and the JMAVSim-Gazebo combination as essential tools for evaluating and refining autonomous aerial systems. The research also introduces a novel "Localized Movement-Assisted Sensor Deployment Algorithm for Hole Detection and Healing" in the realm of wireless sensor networks. This algorithm leverages movement-assisted deployment to address the coverage hole problem and has shown superior performance in terms of accuracy, efficiency, and energy consumption. The practical implications of these domains span various real-world applications, emphasizing the importance of advanced simulation environments in technology development.

In this comprehensive investigation, i explored into the implementation of the sensor deployment algorithm, showcasing the transformative potential of autonomous drones. Real-case scenarios and simulations within the Gazebo Classic Simulator highlight the adaptability and precision of drone-based systems. The research contributes to the dynamic field of drone technology, emphasizing the role of efficient sensor deployment strategies in practical applications across industries.
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