Leader-following flocking for unmanned aerial vehicle swarm with distributed topology control

Liu, Chao; Wang, Meng; Zeng, Qian; Huangfu, Wei

doi:10.1007/s11432-019-2763-5

Cited by 17 publications

(7 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Liu et al [67] suggested a kinetic controller, distributed β-angle test (BAT)-based topology control algorithm, and Flying ad-hoc network (FANET) for UAV flocking. This mechanism could perform neighbor selection and reduce the communication overhead significantly.…”

Section: Related Surveymentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

View full text Add to dashboard Cite

The unmanned aerial vehicle (UAV) swarm is gaining massive interest for researchers as it has huge significance over a single UAV. Many studies focus only on a few challenges of this complex multidisciplinary group. Most of them have certain limitations. This paper aims to recognize and arrange relevant research for evaluating motion planning techniques and models for a swarm from the viewpoint of control, path planning, architecture, communication, monitoring and tracking, and safety issues. Then, a state-of-the-art understanding of the UAV swarm and an overview of swarm intelligence (SI) are provided in this research. Multiple challenges are considered, and some approaches are presented. Findings show that swarm intelligence is leading in this era and is the most significant approach for UAV swarm that offers distinct contributions in different environments. This integration of studies will serve as a basis for knowledge concerning swarm, create guidelines for motion planning issues, and strengthens support for existing methods. Moreover, this paper possesses the capacity to engender new strategies that can serve as the grounds for future work.

show abstract

Section: Related Surveymentioning

confidence: 99%

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Iqbal¹,

Ali²,

Khan³

et al. 2022

Aeronautics - New Advances

View full text Add to dashboard Cite

show abstract

“…In our work, we apply the behavior of flocks of starlings following a topological interaction rule in order to achieve robust swarm flight behavior [19]. In prior works, this type of biological insight has inspired flocking control design [20] and real-world deployment of drone swarms [21]. Somewhat more related, [22] implements end-to-end-learned flocking control based on classical swarming models.…”

Section: Related Workmentioning

confidence: 99%

Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning

Ourari¹,

Cui²,

Elshamanhory³

et al. 2021

Preprint

View full text Add to dashboard Cite

Collision avoidance algorithms are of central interest to many drone applications. In particular, decentralized approaches may be the key to enabling robust drone swarm solutions in cases where centralized communication becomes computationally prohibitive. In this work, we draw biological inspiration from flocks of starlings (Sturnus vulgaris) and apply the insight to end-to-end learned decentralized collision avoidance. More specifically, we propose a new, scalable observation model following a biomimetic topological interaction rule that leads to stable learning and robust avoidance behavior. Additionally, prior work primarily focuses on invoking a separation principle, i.e. designing collision avoidance independent of specific tasks. By applying a general reinforcement learning approach, we propose a holistic learning-based approach to integrating collision avoidance with various tasks and dynamics. To validate the generality of this approach, we successfully apply our methodology to a number of configurations. Our learned policies are tested in simulation and subsequently transferred to real-world drones to validate their real-world applicability.

show abstract

“…Autonomous ight and evasion control of swarm system has become an urgent problem to be solved. For a swarm system, there are currently two main control methods: leader follower [5][6][7] and behavior control [8][9][10]. e idea of the leader follower method is to introduce a leader and use the distributed control method to control other followers in the swarm, so that the state (such as speed) of all followers gradually follows the leader and nally achieves consistency.…”

Section: Introductionmentioning

confidence: 99%

Anticollision Decision and Control of UAV Swarm Based on Intelligent Cognitive Game

Huan

Han

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

UAV swarm anticollision system is very important to improve the flight safety of the whole swarm formation, while the existing system design methods are still insufficient in realizing autonomous and cooperative anticollision. Based on the cognitive game theory, an intelligent decision-making and control method for UAV swarm anticollision is designed. Firstly, by using the idea of swarm intelligence, basic flight behaviors of UAV swarm are defined as five basic flight rules, such as cohesion, following, self-guidance, dispersion, and alliance. Further, the cognitive security domain of UAV swarm is constructed by setting the overall anticollision rules of the swarm and the anticollision rules of individual members. On this basis, the anticollision problem of UAV swarm is transformed into a game problem involving two parties, and the solution method of decision and control strategy set is proposed. Finally, the stability of anticollision decision and control method is proved through eigenvalue theory. The simulation results show that the method proposed in this paper can effectively realize the autonomous cooperative anticollision of UAV swarm and also has good algorithm real-time solution ability while ensuring flight safety.

show abstract

Leader-following flocking for unmanned aerial vehicle swarm with distributed topology control

Cited by 17 publications

References 32 publications

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Motion Planning of UAV Swarm: Recent Challenges and Approaches

Nearest-Neighbor-based Collision Avoidance for Quadrotors via Reinforcement Learning

Anticollision Decision and Control of UAV Swarm Based on Intelligent Cognitive Game

Contact Info

Product

Resources

About