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CPPNet: A Coverage Path Planning Network [PDF]

open access: yesOCEANS 2021: San Diego – Porto, 2021
This paper presents a deep-learning based CPP algorithm, called Coverage Path Planning Network (CPPNet). CPPNet is built using a convolutional neural network (CNN) whose input is a graph-based representation of the occupancy grid map while its output is an edge probability heat graph, where the value of each edge is the probability of belonging to the ...
Shen, Zongyuan   +4 more
openaire   +2 more sources

Coverage path planning for spraying drones

open access: yesComputers & Industrial Engineering, 2022
Submitted to Computers and Industrial ...
Vazquez-Carmona, E. Viridiana   +3 more
openaire   +3 more sources

Occlusion-Aware UAV Path Planning for Reconnaissance and Surveillance

open access: yesDrones, 2021
Unmanned Aerial Vehicles (UAVs) have become necessary tools for a wide range of activities including but not limited to real-time monitoring, surveillance, reconnaissance, border patrol, search and rescue, civilian, scientific and military missions, etc.
Jian Zhang, Hailong Huang
doaj   +1 more source

Energy-Aware Coverage Path Planning of UAVs [PDF]

open access: yes2015 IEEE International Conference on Autonomous Robot Systems and Competitions, 2015
Coverage path planning is the operation of finding a path that covers all the points of a specific area. Thanks to the recent advances of hardware technology, Unmanned Aerial Vehicles (UAVs) are starting to be used for photogrammetric sensing of large areas in several application domains, such as agriculture, rescuing, and surveillance.
DI FRANCO, CARMELO   +1 more
openaire   +1 more source

Smooth Complete Coverage Trajectory Planning Algorithm for a Nonholonomic Robot

open access: yesSensors, 2022
The complete coverage path planning is a process of finding a path which ensures that a mobile robot completely covers the entire environment while following the planned path.
Ana Šelek   +3 more
doaj   +1 more source

An Algorithm of Complete Coverage Path Planning for Unmanned Surface Vehicle Based on Reinforcement Learning

open access: yesJournal of Marine Science and Engineering, 2023
A deep reinforcement learning method to achieve complete coverage path planning for an unmanned surface vehicle (USV) is proposed. This paper firstly models the USV and the workspace required for complete coverage.
Bowen Xing   +4 more
doaj   +1 more source

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