Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction

Alejo, David; Cobano, J. A.; Heredia, Guillermo; Ollero, A.

doi:10.1007/s10846-013-9948-x

Cited by 48 publications

(23 citation statements)

References 22 publications

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“…This involves random objective functions or random constraints. These methods include random search methods [28], evolutionary computation methods [29,30], particle swarm optimization [31], ant colony optimization methods [32], simulated annealing [33], among others. Rapidly-exploring Random Tree (RRT) planning algorithm is also commonly used for path planning [12,34].…”

Section: State Of the Artmentioning

confidence: 99%

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Alejo

Cobano

Heredia

et al. 2015

Cooperative Robots and Sensor Networks 2015

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This chapter discusses the problem of trajectory planning for WSN (Wireless Sensor Network) data retrieving deployed in remote areas with a cooperative system of UAVs (Unmanned Aerial Vehicles). Three different path planners are presented in order to autonomously guide the UAVs during the mission. The missions are given by a set of waypoints which define WSN collection zones and each UAV should pass through them to collect the data while avoiding passing over forbidden areas and collisions between UAVs. The proposed UAV trajectory planners are based on Genetics Algorithm (GA), RRT (Rapidly-exploring Random Trees) and RRT* (Optimal Rapidly-exploring Random Trees). Simulations and experiments have been carried out in the airfield of Utrera (Seville, Spain). These results are compared in order to measure the performance of the proposed planners.

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Section: State Of the Artmentioning

confidence: 99%

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Alejo

Cobano

Heredia

et al. 2015

Cooperative Robots and Sensor Networks 2015

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“…The next sections describe the improvements done on the PSO implemented in [1]. This new implementation is named MS-PSO (Maneuver Selection-Particle Swarm Optimization).…”

Section: Implementation Of the Pso Algorithmmentioning

confidence: 99%

“…Multiple UAVs present important advantages to carry out cooperative missions [1], but the probability of collisions among vehicles increases in these scenarios. Moreover, the problem is more difficult to solve when flying in small-sized scenarios with several vehicles [2].…”

Section: Introductionmentioning

confidence: 99%

Collision-free trajectory planning based on Maneuver Selection-Particle Swarm Optimization

Alejo

Cobano

Heredia

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper presents a system for collisionfree trajectory planning with multiple Unmanned Aerial Vehicles (UAVs) which automatically identifies conflicts among them. After detecting conflicts between UAVs, the system resolves them cooperatively using a collisionfree trajectory planning algorithm based on a stochastic optimization technique named Particle Swarm Optimization (PSO). The new implementation of the PSO algorithm, named Maneuver Selection Particle Swarm Optimization (MS-PSO), presents improvements with respect to previous implementations. The execution time is reduced because the dimension of the problem is reduced, and different kinds of maneuvers can be selected to solve the detected conflicts: course/heading, speed or altitude changes. The MS-PSO has been validated with simulations in scenarios with multiple UAVs in a common airspace. Also, a comparison to a genetic algorithm and a PSO algorithm has been performed to highlight the advantages of the MS-PSO. The main advantage is that MS-PSO always ensures solution from the first iteration. This requirement is essential in safe cooperative missions.

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“…These advantages of VTOL UAV provide a cheap solution to dull, dirty and hazardous missions in many fields, such as target tracking (Vanegas et al, 2016), 3D terrain reconstruction , agriculture mapping (Perez-Ortiz et al, 2016), wildlife protection (Olivares-Mendez et al, 2015) and emergency evacuation . In addition, apart from individual missions VTOL UAVs, e.g., quadrotors, have been widely exploited to perform cooperative tasks such as crop monitoring (Valente et al, 2011), assembly and structure construction (Alejo et al, 2014), and payload transportation (Michael et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

Sarabakha¹,

Imanberdiyev²,

Kayacan

et al. 2017

Information Sciences

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In this paper, a Levenberg-Marquardt inspired sliding mode control theory based adaptation laws are proposed to train an intelligent fuzzy neural network controller for a quadrotor aircraft. The proposed controller is used to control and stabilize a quadrotor unmanned aerial vehicle in the presence of periodic wind gust. A proportional-derivative controller is firstly introduced based on which fuzzy neural network is able to learn the quadrotor's control model on-line. The proposed design allows handling uncertainties and lack of modelling at a computationally inexpensive cost. The parameter update rules of the learning algorithms are derived based on a Levenberg-Marquardt inspired approach, and the proof of the stability of two proposed control laws are verified by using the Lyapunov stability theory. In order to evaluate the performance of the proposed controllers extensive simulations and real-time experiments are conducted. The 3D trajectory tracking problem for a quadrotor is considered in the presence of time-varying wind conditions.

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Collision-Free 4D Trajectory Planning in Unmanned Aerial Vehicles for Assembly and Structure Construction

Cited by 48 publications

References 22 publications

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Efficient Trajectory Planning for WSN Data Collection with Multiple UAVs

Collision-free trajectory planning based on Maneuver Selection-Particle Swarm Optimization

Novel Levenberg–Marquardt based learning algorithm for unmanned aerial vehicles

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