Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

Salamat, Babak; Tonello, Andrea M.

doi:10.3390/aerospace4020027

Cited by 27 publications

(11 citation statements)

References 23 publications

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“…Intelligent swarm UAV cooperative search strategies in a hazardous environment were presented and analyzed based on a number of deployed UAVs and search time [69]. PSO was used for multi-UAV trajectory optimization [70], and the genetic algorithm was simulated to make a comparison with the proposed algorithm regarding execution time and effectiveness in finding a minimum length trajectory. The UAV swarms were wireless ad hoc networks which form an aerial platform [71].…”

Section: IVmentioning

confidence: 99%

Survey on artificial intelligence based techniques for emerging robotic communication

Alsamhi

Ansari

2019

Telecommun Syst

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This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The study of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently.

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Section: IVmentioning

confidence: 99%

Survey on artificial intelligence based techniques for emerging robotic communication

Alsamhi

Ansari

2019

Telecommun Syst

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“…Firstly, the desired trajectories are generated. Feasible trajectories can be obtained for instance, with some waypoints that are located in the search space by the user [26], or they are generated by a stochastic approach [27], [28]. Another approach is using dynamic path planning which the tangent vector field guidance (TVFG) and the Lyapunov vector field guidance (LVFG) are used [29].…”

Section: Overall Control Algorithmmentioning

confidence: 99%

A Swash Mass Unmanned Aerial Vehicle: Design, Modeling and Control

Tonello,

Salamat

2019

Preprint

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In this paper, a new unmanned aerial vehicle (UAV) structure, referred to as swash mass UAV, is presented. It consists of a double blade coaxial shaft rotor and four swash masses that allow changing the orientation and maneuvering the UAV. The dynamical system model is derived from the Newton's law framework. The rotational behavior of the UAV is discussed as a function of the design parameters. Given the uniqueness and the form of the obtained non-linear dynamical system model, a back-stepping control mechanism is proposed. It is obtained following the Lyapunov's control approach in each iteration step. Numerical results show that the swashed mass UAV can be maneuvered with the proposed control algorithm so that linear and aggressive trajectories can be accurately tracked.

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“…We considered the problem of range rate tracking assuming that there were three (03) moving sensors in known locations, fixed without uncertainties; other problem statements can be found, for example, in [39,42]. In the first simulation, three sensors are used to measure the range rate between the target and the sensors.…”

Section: Multi-uavs Range-rate-only Target Trackingmentioning

confidence: 99%

Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

Benzerrouk

Nebylov

2018

Aerospace

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Multi-Unmanned Aerial Vehicle (UAV) Doppler-based target tracking has not been widely investigated, specifically when using modern nonlinear information filters. A high-degree Gauss-Hermite information filter, as well as a seventh-degree cubature information filter (CIF), is developed to improve the fifth-degree and third-degree CIFs proposed in the most recent related literature. These algorithms are applied to maneuvering target tracking based on Radar Doppler range/range rate signals. To achieve this purpose, different measurement models such as range-only, range rate, and bearing-only tracking are used in the simulations. In this paper, the mobile sensor target tracking problem is addressed and solved by a higher-degree class of quadrature information filters (HQIFs). A centralized fusion architecture based on distributed information filtering is proposed, and yielded excellent results. Three high dynamic UAVs are simulated with synchronized Doppler measurement broadcasted in parallel channels to the control center for global information fusion. Interesting results are obtained, with the superiority of certain classes of higher-degree quadrature information filters.

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Stochastic Trajectory Generation Using Particle Swarm Optimization for Quadrotor Unmanned Aerial Vehicles (UAVs)

Cited by 27 publications

References 23 publications

Survey on artificial intelligence based techniques for emerging robotic communication

Survey on artificial intelligence based techniques for emerging robotic communication

A Swash Mass Unmanned Aerial Vehicle: Design, Modeling and Control

Multi-UAV Doppler Information Fusion for Target Tracking Based on Distributed High Degrees Information Filters

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