Online Identification-Verification-Prediction Method for Parallel System Control of UAVs

Huang, Yixin; Xiang, Xuyu; Zhou, Han; Tang, Daquan; Sun, Yihao

doi:10.3390/aerospace8040099

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…AIpowered algorithmic trading possibilities, intelligent daily trade drone deliveries, and automated trading systems are still semi-machine. The AI-trading signals, market trading bots, intelligent trade financing, and intelligent selfsufficient trading application among others are still having technical issues altogether [151].…”

Section: Intelligently Trade Stocksmentioning

confidence: 99%

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Shafik

Matinkhah

Shokoor

et al. 2022

EAI Endorsed Trans IoT

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Machine learning (ML) entails artificial procedures that improve robotically through experience and using data. Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML. This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context. This is a comparison to supervised and non-supervised learning due to the interactive nature of the environment. Exploiting a forthcoming accumulative compensation and its stimulus of machines, complex policy decisions. The study further analyses and presents ML perspectives depicting state-of-the-art developments with advancement, relatively depicting the future trend of RL based on its applicability in technology. It's a challenge to an Internet of Things (IoT) and demonstrates what possibly can be adopted as a solution. This study presented a summarized perspective on identified arenas on the analysis of RL. The study scrutinized that a reasonable number of the techniques engrossed in alternating policy values instead of modifying other gears in an exact state of intellectual. The study presented a strong foundation for the current studies to be adopted by the researchers from different research backgrounds to develop models, and architectures that are relevant.

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Section: Intelligently Trade Stocksmentioning

confidence: 99%

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Shafik

Matinkhah

Shokoor

et al. 2022

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…However, with the increasing diversification of missions and the complexity of flight environments, more and more users are reaching the limitations of single UAVs when performing missions [3]. Therefore, multiple UAV systems have gradually grown into a popular and challenging research topic [4]. As the most basic problem in the research of multi-UAV systems, the cooperative path planning technology of multiple UAVs directly affects the ability of UAVs to complete tasks smoothly, safely, and autonomously [5,6].…”

Section: Introductionmentioning

confidence: 99%

Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Guo

Liu

Wei

et al. 2023

Drones

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In this paper, a four-dimensional (4D) dynamic cooperative path planning algorithm for multiple unmanned aerial vehicles (UAVs) is proposed, in which the cooperative time variables of UAVs, as well as conflict and threat avoidance, are considered. The algorithm proposed in this paper uses a hierarchical framework that is divided into a 4D cooperative planning layer and a local threat avoidance planning layer. In the cooperative planning layer, the proposed algorithm, named dynamic priority rapidly exploring random trees (DPRRT*), would be used for the 4D cooperative path planning of all UAVs involved in a given task. We first designed a heuristic prioritization strategy in the DPRRT* algorithm to rank all UAVs to improve the efficiency of cooperative planning. Then, the improved RRT* algorithm with the 4D coordination cost function was used to plan the 4D coordination path for each UAV. Whenever the environment changes dynamically (i.e., sudden static or moving threats), the proposed heuristic artificial potential field algorithm (HAPF) in the local threat avoidance planning layer is used to plan the local collision avoidance path. After completing local obstacle avoidance planning, the DPRRT* of the 4D cooperative planning layer is again called upon for path replanning to finally realize 4D cooperative path planning for all UAVs. The simulation and comparison experiments prove the feasibility, efficiency, and robustness of the proposed algorithm.

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Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions

Xu,

Gu,

Wang

et al. 2024

IEEE Trans. Intell. Veh.

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Online Identification-Verification-Prediction Method for Parallel System Control of UAVs

Cited by 5 publications

References 26 publications

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Collision-Free 4D Dynamic Path Planning for Multiple UAVs Based on Dynamic Priority RRT* and Artificial Potential Field

Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions

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