.png){kind=icon}
Return
A Sampling-Based Distributed Exploration Method for UAV Cluster in Unknown Environments
.png){kind=icon}
OA
AI
DOI：10.3390/drones7040246.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Rapidly completing the exploration and construction of unknown environments is an important task of a UAV cluster. However, the formulation of an online autonomous exploration strategy based on a real-time detection map is still a problem that needs to be discussed and optimized. In this paper, we propose a distributed unknown environment exploration framework for a UAV cluster that comprehensively considers the path and terminal state gain, which is called the Distributed Next-Best-Path and Terminal (DNBPT) method. This method calculates the gain by comprehensively calculating the new exploration grid brought by the exploration path and the guidance of the terminal state to the unexplored area to guide the UAV's next decision. We propose a suitable multistep selective sampling method and an improved Discrete Binary Particle Swarm Optimization algorithm for path optimization. The simulation results show that the DNBPT can realize rapid exploration under high coverage conditions in multiple scenes.
Keywords:
exploration of unknown environment
UAV cluster
sampling and optimization
distributed path planning
particle swarm optimization
Journal
D
IF:
4.8
Papers:
3.9K
Citations:
8.3K
Organization
Cited Papers
ShareSaveMulti-UAV Autonomous Path Planning in Reconnaissance Missions Considering Incomplete Information: A Reinforcement Learning Method.png)
DRONESOAAI
DRONESIF4.8
OAAIShareSaveShareSaveShareSaveShareSave
View more