PSO Algorithm in Quadcopter Cluster for Unified Trajectory Planning Using Wireless Ad Hoc Network

Sona, Deepika Rani; Das, Rashmi Ranjan; Dubey, Sarthak; Kaswan, Yogesh

doi:10.1007/978-81-322-2671-0_77

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…Flocking and swarming are the major role players in cluster formations with the aerial vehicles . Recently, the particle swarm optimization algorithm has been used to form a cluster between the quadcopters for efficient control and navigation . This nature‐inspired algorithm controls the manoeuvres as well as provides better control of vehicles while operating mutually.…”

Section: Taxonomy Of Unmanned Aerial Vehicle Networkmentioning

confidence: 99%

Cooperative frameworks and network models for flying ad hoc networks: a survey

Sharma

Kumar

2016

Concurrency and Computation

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Summary Integrated frameworks have extended the applications of networks beyond a simple data sharing unit. Simultaneously, operating networks can form a layered structure that can operate as homogeneous as well as dissociated units. Networks using unmanned aerial vehicles follow similar criteria in their operability. Unmanned aerial vehicles can act as single searching unit controlled by human or can form an aerial swarm that can fly autonomously with the capability of forming an aerial network. Such aerial swarms are categorized as aerial ad hoc networks. Cooperation amongst different networks can be realized using various frameworks, models, architectures and middlewares. Several solutions have been developed that can provide easy network deployment of aerial nodes. However, a combined literature is not present that provides a comparison between these approaches. Keeping this in view, various cooperative approaches for similar formation using aerial vehicles have been discussed in this paper. The detailed study and comparative analysis of these approaches have been included. Further, the paper also includes various software solutions and their comparisons based on common parameters. Finally, various open issues have been discussed that can provide insight of ongoing research and problems that are yet to be resolved in these networks. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Taxonomy Of Unmanned Aerial Vehicle Networkmentioning

confidence: 99%

Cooperative frameworks and network models for flying ad hoc networks: a survey

Sharma

Kumar

2016

Concurrency and Computation

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“…Another advantage of CS compared to PSO and GA is that it uses a smaller number of parameters to be tuned, which makes it more adaptable [21]. Due to these advantages, PSO and CS are still successfully applied in recent works for control problems [23][24][25], and PSO is especially used in tuning PID controllers for quadrotor's control [26,27]. Therefore, regarding PSO and CS programs, there are many common points.…”

Section: Introductionmentioning

confidence: 99%

Particle Swarm Optimization and Cuckoo Search-Based Approaches for Quadrotor Control and Trajectory Tracking

et al. 2019

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This paper explores the full control of a quadrotor Unmanned Aerial Vehicles (UAVs) byexploiting the nature-inspired algorithms of Particle Swarm Optimization (PSO), Cuckoo Search(CS), and the cooperative Particle Swarm Optimization-Cuckoo Search (PSO-CS). The proposedPSO-CS algorithm combines the ability of social thinking in PSO with the local search capability inCS, which helps to overcome the problem of low convergence speed of CS. First, the quadrotordynamic modeling is defined using Newton-Euler formalism. Second, PID (Proportional, Integral,and Derivative) controllers are optimized by using the intelligent proposed approaches and theclassical method of Reference Model (RM) for quadrotor full control. Finally, simulation resultsprove that PSO and PSO-CS are more efficient in tuning of optimal parameters for the quadrotorcontrol. Indeed, the ability of PSO and PSO-CS to track the imposed trajectories is well seen from3D path tracking simulations and even in presence of wind disturbances.

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