Development of an MUM-T Integrated Simulation Platform

Kim, Seongheon; Kim, Yoonsoo

doi:10.1109/access.2023.3248096

Cited by 5 publications

(5 citation statements)

References 17 publications

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“…In addition, the command sets should be composed of frequently used words for the pilot's convenience and consist of words that are easy for speech recognition. There have been several international cooperation projects related to the collaborative operation of MAVs and UAVs, and various related reports and standards have been published, including the Standardization Agreement (STANAG)-4586, Manned-Unmanned Teaming (MUM-T), and Manned-Unmanned Systems Integration Capability (MUSIC) [18,19]. By analyzing the documents, we investigated the collaboration situations and missions between MAVs and UAVs and specified the division of roles between MAVs and UAVs according to cooperative operation.…”

Section: Definition Of Voice Commands For Training Data Collectionmentioning

confidence: 99%

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Park,

Geng

2023

Aerospace

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Most conventional speech recognition systems have mainly concentrated on voice-driven control of personal user devices such as smartphones. Therefore, a speech recognition system used in a special environment needs to be developed in consideration of the environment. In this study, a speech recognition framework for voice-driven control of unmanned aerial vehicles (UAVs) is proposed in a collaborative environment between manned aerial vehicles (MAVs) and UAVs, where multiple MAVs and UAVs fly together, and pilots on board MAVs control multiple UAVs with their voices. Standard speech recognition systems consist of several modules, including front-end, recognition, and post-processing. Among them, this study focuses on recognition and post-processing modules in terms of in-vehicle speech recognition. In order to stably control UAVs via voice, it is necessary to handle the environmental conditions of the UAVs carefully. First, we define control commands that the MAV pilot delivers to UAVs and construct training data. Next, for the recognition module, we investigate an acoustic model suitable for the characteristics of the UAV control commands and the UAV system with hardware resource constraints. Finally, two approaches are proposed for post-processing: grammar network-based syntax analysis and transaction-based semantic analysis. For evaluation, we developed a speech recognition system in a collaborative simulation environment between a MAV and an UAV and successfully verified the validity of each module. As a result of recognition experiments of connected words consisting of two to five words, the recognition rates of hidden Markov model (HMM) and deep neural network (DNN)-based acoustic models were 98.2% and 98.4%, respectively. However, in terms of computational amount, the HMM model was about 100 times more efficient than DNN. In addition, the relative improvement in error rate with the proposed post-processing was about 65%.

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Section: Definition Of Voice Commands For Training Data Collectionmentioning

confidence: 99%

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Park,

Geng

2023

Aerospace

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“…This trend has accelerated with advancements in control, networking, and AI technologies. Examples include multidrone entertainment such as drone shows [1,2], disinfection and pest control systems using multiple robots, goods movement within logistics centers [3], and military applications such as Manned-Unmanned Teaming (MUM-T) [4,5]. These innovations represent significant progress in the utilization of multi-robot systems across diverse fields.…”

Section: Introductionmentioning

confidence: 99%

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

Kwon,

Yun,

Kim

et al. 2024

Applied Sciences

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This paper proposes a coupled-error-based formation control algorithm for the rapid formation completion of multi-robot systems. We consider a multi-robot system with omni-directional robots with swerve-driving mechanisms and a communication system with minimized constraints. This paper introduces a coupled error that links the distance errors with the leading robot and the following robot through a coupling ratio. We propose a controller using the coupled error to achieve the control objectives of this paper. Unlike existing results that only use the information of the preceding robot, this algorithm couples the information of both the preceding robot and one’s follower. Using the proposed error-coupling-based formation control algorithm, multi-robot systems can quickly establish formations for collaboration, allowing tasks to commence swiftly and reducing deformations in formations due to speed variations. With stability analysis and simulation results for the practical application of the proposed algorithm, the approach has been verified to improve the speed of both the completion of the formation and overall system trajectory tracking, balancing trade-offs between them.

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“…Yang et al [18] proposed a life cycle cost capacity allocation method based on a genetic algorithm considering load power shortage constraints. Aykut et al [19] study introduces a multi-objective optimization of an islanded green energy system, and use a novel hybrid of the Firefly and particle swarm optimization algorithms (HFAPSO) to ensure optimal sizing of hybrid renewable energy system. The results demonstrate the supremacy of the HFAPSO algorithm in sizing system.…”

Section: Introductionmentioning

confidence: 99%

Multi‐factor Analysis of Capacity Allocation Optimization for Novel Wind‐Hydrogen Coupled Power Generation Systems

Meng,

Liu,

et al. 2024

Energy Tech

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Wind power generation has the problem of wind resource waste. Wind‐hydrogen coupled can enhance wind power's utilization and revenue. Currently, there is no discussion of the impact of multiple factors on the system configuration. In this study, take the annual profit of the wind‐hydrogen coupled power generation systems (WHCPGS) as the objective function, and construct the multi‐factor capacity configuration model. Using particle swarm optimization (PSO) to solve the model, and analyzes the impact of multi‐factors on the system's annual profit and revenue mode. The results show that with 30 wind turbines, the coupled system annual profit increases by 43% compared to the alone system. The higher investment does not make the system more rewarding. The configuration results are the same for investments of 0.8 billion ¥ and 1 billion ¥. The price at 0.20 ¥ kW−1 h−1, the coupled system has the optimal annual profit. Fuel cells’ (FCs) electricity price and hydrogen transportation distance are crucial factors influencing the mode of the system profit. This study has specific reference significance for the practical application of capacity configuration of the system.

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Development of an MUM-T Integrated Simulation Platform

Abstract: Date of publication xxxx 00, 0000, date of current version xxxx 00, 0000.

Cited by 5 publications

References 17 publications

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

In-Vehicle Speech Recognition for Voice-Driven UAV Control in a Collaborative Environment of MAV and UAV

Coupled-Error-Based Formation Control for Rapid Formation Completion by Omni-Directional Robots

Multi‐factor Analysis of Capacity Allocation Optimization for Novel Wind‐Hydrogen Coupled Power Generation Systems

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