In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and lea...In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.展开更多
In light of the intricate and volatile nature of battlefield environments,unmanned aerial vehicle(UAV)swarms have become a critical asset in contemporary military operations.The autonomous obstacle avoidance capabilit...In light of the intricate and volatile nature of battlefield environments,unmanned aerial vehicle(UAV)swarms have become a critical asset in contemporary military operations.The autonomous obstacle avoidance capabilities of UAV swarms are crucial for enhancing their operational effectiveness and survivability in complex battlefield conditions.Consequently,this technology has garnered significant attention from researchers globally,positioning it as a key area of advanced military technology.In response to the diverse characteristics of obstacles in combat environments,a cooperative obstacle avoidance strategy for UAV swarms on the basis of an improved artificial potential field(APF)method and a variable topology structure is proposed in this study.By considering the properties of static and dynamic obstacles on the battlefield,the proposed strategy models the flight space with obstacles as being segmented into multiple smaller navigable regions between these obstacles.To address the limitations of the traditional APF method,this study introduces velocity adaptation components,angle factors,and auxiliary traction forces to optimize the repulsive force component of the traditional APF method.Additionally,combining this approach with a coalition-based variable topology formation reconfiguration algorithm,the strategy realizes autonomous obstacle avoidance for UAV swarms in battlefield obstacle environments.This method not only resolves the common issue of local minima in traditional APF obstacle avoidance techniques but also ensures effective obstacle avoidance by UAV swarms when large obstacles are encountered.The results of simulation experiments demonstrate the feasibility and performance of the proposed strategy.展开更多
基金supported in part by the National Natural Science Foundation of China (62033009)the Creative Activity Plan for Science and Technology Commission of Shanghai (20510712300,21DZ2293500)the Supported by Science Foundation of Donghai Laboratory。
文摘In this paper, the fixed-time event-triggered obstacle avoidance consensus control for a multi-AUV time-varying formation system in a 3D environment is presented by using an improved artificial potential field and leader-follower strategy(IAPF-LF). Firstly, the proposed fixed-time control can achieve the desired multi-AUV formation within a fixed settling time in any initial system state. Secondly, an event-triggered communication strategy is developed to govern the communication among AUVs, and the communication energy consumption can be decremented. The time-varying formation obstacle avoidance control algorithm based on IAPF-LF is designed to avoid static and dynamic obstacles, the desired formation is maintained in the presence of external disturbances, and there is no Zeno behavior under the fixed-time event-triggered consensus control strategy.The stability of the system is proved by the Lyapunov function and inequality scaling. Finally, simulation examples and water pool experiments are reported to verify the performance of the proposed theoretical algorithms.
基金supported by the National Natural Science Foundation of China(Grant No.72471204)。
文摘In light of the intricate and volatile nature of battlefield environments,unmanned aerial vehicle(UAV)swarms have become a critical asset in contemporary military operations.The autonomous obstacle avoidance capabilities of UAV swarms are crucial for enhancing their operational effectiveness and survivability in complex battlefield conditions.Consequently,this technology has garnered significant attention from researchers globally,positioning it as a key area of advanced military technology.In response to the diverse characteristics of obstacles in combat environments,a cooperative obstacle avoidance strategy for UAV swarms on the basis of an improved artificial potential field(APF)method and a variable topology structure is proposed in this study.By considering the properties of static and dynamic obstacles on the battlefield,the proposed strategy models the flight space with obstacles as being segmented into multiple smaller navigable regions between these obstacles.To address the limitations of the traditional APF method,this study introduces velocity adaptation components,angle factors,and auxiliary traction forces to optimize the repulsive force component of the traditional APF method.Additionally,combining this approach with a coalition-based variable topology formation reconfiguration algorithm,the strategy realizes autonomous obstacle avoidance for UAV swarms in battlefield obstacle environments.This method not only resolves the common issue of local minima in traditional APF obstacle avoidance techniques but also ensures effective obstacle avoidance by UAV swarms when large obstacles are encountered.The results of simulation experiments demonstrate the feasibility and performance of the proposed strategy.
