Asset Details
Do you wish to reserve the book?
Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards
Do you wish to request the book?
Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Enhanced multi agent coordination algorithm for drone swarm patrolling in durian orchards
2025
Overview
This study proposes an enhanced multi-agent swarm control algorithm (EN-MASCA) to solve the problem of efficient patrolling of drone swarms in complex durian orchard environments. It introduces a virtual navigator model to dynamically adjust the patrol path of the drone swarm and perform obstacle avoidance and path optimization in real time according to environmental changes. Different from traditional algorithms that only rely on fixed path planning, the virtual navigator model significantly improves the flexibility and stability of the drone swarm in complex environments. It also applies deep reinforcement learning algorithms to path planning and obstacle avoidance of drone swarms for the first time, improving the algorithm’s adaptability and optimization capabilities by learning dynamic information in complex environments. This innovation significantly improves the applicability of existing methods in complex terrain and dynamic obstacle environments. Finally, it incorporates the simulation characteristics of biological swarm behavior, and on this basis, comprehensively optimizes the flight path, obstacle avoidance and swarm stability of the drone swarm. By improving control strategies and parameter design, it improves the trajectory consistency and mission completion efficiency of the UAV swarm during flight. In the experimental part, this study verified in detail the advantages of the EN-MASCA algorithm in terms of flight trajectory, flight stability, cluster consistency and task completion efficiency by constructing a six-degree-of-freedom UAV motion simulation model and real environment simulation. It provides an efficient and intelligent solution for collaborative patrol operations of drones in durian orchards, which has important practical application value and promotion prospects.
Publisher
Nature Publishing Group UK,Nature Publishing Group,Nature Portfolio
Subject
