A Hybrid Systems-Based Hierarchical Control Architecture for Heterogeneous Field Robot Teams

Ju, Chanyoung; Son, Hyoung Il

doi:10.1109/tcyb.2021.3133631

Cited by 11 publications

(2 citation statements)

References 52 publications

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“…For cooperative control of other unmanned vehicles, a clear system must be established. Heterogeneous cooperation requires not only USVs and UUVs, but also a detailed hierarchical control system between heterogeneous vehicles with different functions and platforms [ 9 , 184 ].…”

Section: Discussionmentioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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With the recent development of artificial intelligence (AI) and information and communication technology, manned vehicles operated by humans used on the ground, air, and sea are evolving into unmanned vehicles (UVs) that operate without human intervention. In particular, unmanned marine vehicles (UMVs), including unmanned underwater vehicles (UUVs) and unmanned surface vehicles (USVs), have the potential to complete maritime tasks that are unachievable for manned vehicles, lower the risk of man power, raise the power required to carry out military missions, and reap huge economic benefits. The aim of this review is to identify past and current trends in UMV development and present insights into future UMV development. The review discusses the potential benefits of UMVs, including completing maritime tasks that are unachievable for manned vehicles, lowering the risk of human intervention, and increasing power for military missions and economic benefits. However, the development of UMVs has been relatively tardy compared to that of UVs used on the ground and in the air due to adverse environments for UMV operation. This review highlights the challenges in developing UMVs, particularly in adverse environments, and the need for continued advancements in communication and networking technologies, navigation and sound exploration technologies, and multivehicle mission planning technologies to improve UMV cooperation and intelligence. Furthermore, the review identifies the importance of incorporating AI and machine learning technologies in UMVs to enhance their autonomy and ability to perform complex tasks. Overall, this review provides insights into the current state and future directions for UMV development.

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Section: Discussionmentioning

confidence: 99%

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Bae

Hong

2023

Sensors

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“…In the work by Ju and Son, 34 the authors proposed a hybrid system that combined a continuous-time system and DES for large-scale dynamic systems, such as field UAVs. Their research expanded on a previous study 35,36 by considering partial observation caused by the inability to monitor events using sensors because of noise, disturbances, and failure.…”

Section: Related Workmentioning

confidence: 99%

Leader–follower control of multi-unmanned aerial vehicle based on supervisory control theory for a broad tributary area mapping scenario

Seol

Son

2023

Proceedings of the Institution of Mechanical Engineers, Part I:

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This study proposes a discrete event system-based control strategy for autonomous tributary mapping using multi-unmanned aerial vehicle. When considering the unmanned aerial vehicles as discrete event systems, supervisory control theory is used to model and control individual unmanned aerial vehicle behavior in the system. In tributary mapping, the situation changes each time depending on environmental factors (e.g. weather) and the work must be performed in an unstructured environment. Therefore, this article proposes a multi-unmanned aerial vehicle-based supervisory control system to solve real-field problems. Unlike the control systems of recent studies, which mainly deal with continuous-time dynamics, we modeled a multi-unmanned aerial vehicle system based on a discrete event system in which the dynamic states are transitioned by asynchronous events. The proposed multi-unmanned aerial vehicle-based supervisory control system was validated in dynamic simulators and demonstrated that multi-unmanned aerial vehicle satisfies the behavior specifications. The supervisor proposed in this study was validated using a physics-based simulator.

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Multiplayer hierarchical decision‐making for discrete‐time nonlinear networks of service via value iteration adaptive dynamic programming

Cai,

Gao,

Liu

et al. 2024

Intl J Robust & Nonlinear

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In this article, the multiplayer hierarchical decision‐making problem for discrete‐time nonlinear networks of service is studied from the perspective of Nash–Stackelberg–Nash games. A novel two‐level value iteration adaptive dynamic programming algorithm is developed to solve the coupled nonlinear Hamilton–Jacobi–Bellman equations associated with the game problem, which neither requires the system drift dynamics to be known as a priori nor requests the initial strategies to be admissible. Moreover, both the value function sequences and the strategy sequences generated by the algorithm converge to their theoretical optimality. An implementation framework for the algorithm is constructed by leveraging the regularized least‐squares method, and a convergence criterion that guarantees the admissibility of the approximated strategies is also proposed. The effectiveness of the proposed algorithm and implementation framework are finally demonstrated through two numerical simulation examples.

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A Hybrid Systems-Based Hierarchical Control Architecture for Heterogeneous Field Robot Teams

Cited by 11 publications

References 52 publications

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Survey on the Developments of Unmanned Marine Vehicles: Intelligence and Cooperation

Leader–follower control of multi-unmanned aerial vehicle based on supervisory control theory for a broad tributary area mapping scenario

Multiplayer hierarchical decision‐making for discrete‐time nonlinear networks of service via value iteration adaptive dynamic programming

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