Flocking control for two-dimensional multiple agents with limited communication ranges

Bình, Nguyễn Thanh; Dai, Pham Duc; Quang, Nguyen Hong; Ty, Nguyen Trung; Hùng, Nguyễn Mạnh

doi:10.1080/00207179.2019.1707878

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Kumar S. [17] proposed a hybrid approach of intelligent water drop and genetic algorithm technique and executed it in order to obtain global optimal path by replacing local optimal points. Many studies have proved the effectiveness and advantages of flocking control for multi-robot formation, coordination control, and obstacle avoidance control [18][19][20].…”

Section: Related Workmentioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

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Robots have poor adaptive ability in terms of formation control and obstacle avoidance control in unknown complex environments. To address this problem, in this paper, we propose a new motion planning method based on flocking control and reinforcement learning. It uses flocking control to implement a multi-robot orderly motion. To avoid the trap of potential fields faced during flocking control, the flocking control is optimized, and the strategy of wall-following behavior control is designed. In this paper, reinforcement learning is adopted to implement the robotic behavioral decision and to enhance the analytical and predictive abilities of the robot during motion planning in an unknown environment. A visual simulation platform is developed in this paper, on which researchers can test algorithms for multi-robot motion control, such as obstacle avoidance control, formation control, path planning and reinforcement learning strategy. As shown by the simulation experiments, the motion planning method presented in this paper can enhance the abilities of multi-robot systems to self-learn and self-adapt under a fully unknown environment with complex obstacles.

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Section: Related Workmentioning

confidence: 99%

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

2021

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“…A coverage planning for ground mobile robots was introduced in [23] to solve a problem of obstacle-cluttered environments in the twodimensional space. The potential field approach was developed to tackle a collision issue in [18], [21], [24], [25]. The authors in [18], [19] showed outdoor experimental results of formation control of multiple UAVs with consideration to inter-vehicle collisions.…”

Section: Introductionmentioning

confidence: 99%

Collision-free Formation Control of Multiple Nano-quadrotors

Nguyen,

Lee,

Nguyen

et al. 2021

Preprint

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The utilisation of unmanned aerial vehicles has witnessed significant growth in real-world applications including surveillance tasks, military missions, and transportation deliveries. This letter investigates practical problems of formation control for multiple nano-quadrotor systems. To be more specific, the first aim of this work is to develop a theoretical framework for the time-varying formation flight of the multi-quadrotor system regarding anti-collisions. In order to achieve this goal, the finite cut-off potential function is devoted to avoiding collisions among vehicles in the group as well as between vehicles and an obstacle. The control algorithm navigates the group of nano-quadrotors to asymptotically reach an anticipated time-varying formation. The second aim is to implement the proposed algorithm on Crazyflies nanoquadrotors, one of the most ubiquitous indoor experimentation platforms. Several practical scenarios are conducted to tendentiously expose anti-collision abilities among group members as well as between vehicles and an obstacle. The experimental outcomes validate the effectiveness of the proposed method in the formation tracking and the collision avoidance of multiple nano-quadrotors.

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“…This problem arises in a number of applications, such as swarming, schooling, flocking, or rendezvous, and for this reason, it can be regarded as fundamental for other forms of swarm system control. The consensus can be regarded as the fundamental step for solving other control problems for swarm systems: the formation problem where the aim is to control agents to form and maintain some relative positions or a prescribed shape [7]; flocking where agents reach a cohesiveness from their initial positions and move in the same direction at the same speed [8], [9]; and consensus/cohesion tracking where a group of agents self-organizes to track a reference trajectory [10], [11].…”

mentioning

confidence: 99%

A Generalized Gazi–Passino Model With Coordinate-Coupling Matrices for Swarm Formation With Rotation Behavior

Fedele

D’Alfonso²,

Gazi

2022

IEEE Trans. Control Netw. Syst.

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In this article, a kinematic continuous time swarm model is considered by extending the results in the paper "A class of attractions/repulsion functions for stable swarm aggregations" (as shown in the International Journal of Control, vol. 77, pages 1567-1579). The extension regards a more general class of attraction/repulsion functions that can be used to explain the formation of rotational patterns in a swarm of agents. The main characteristic of this model is the introduction of two coordinatecoupling matrices weighting both the attractive and the repulsive interactions among the agents. A stability analysis is presented to characterize the swarm in terms of size and cohesiveness. Moreover, a class of attraction/repulsion functions is presented to guarantee the cohesiveness of the swarm in a polytopic region and a steady-state motion of the agents in a rotational frame around the swarm centroid. Numerical simulations are provided to illustrate the obtained results.

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Flocking control for two-dimensional multiple agents with limited communication ranges

Cited by 5 publications

References 22 publications

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Research on Motion Planning Based on Flocking Control and Reinforcement Learning for Multi-Robot Systems

Collision-free Formation Control of Multiple Nano-quadrotors

A Generalized Gazi–Passino Model With Coordinate-Coupling Matrices for Swarm Formation With Rotation Behavior

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