Journal ArticleDOI
Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning
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TLDR
By using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible and a rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.Abstract:
The development of autonomous unmanned aerial vehicles (UAVs) is of high interest to many governmental and military organizations around the world. An essential aspect of UAV autonomy is the ability for automatic path planning. In this paper, we use the genetic algorithm (GA) and the particle swarm optimization algorithm (PSO) to cope with the complexity of the problem and compute feasible and quasi-optimal trajectories for fixed wing UAVs in a complex 3D environment, while considering the dynamic properties of the vehicle. The characteristics of the optimal path are represented in the form of a multiobjective cost function that we developed. The paths produced are composed of line segments, circular arcs and vertical helices. We reduce the execution time of our solutions by using the “single-program, multiple-data” parallel programming paradigm and we achieve real-time performance on standard commercial off-the-shelf multicore CPUs. After achieving a quasi-linear speedup of 7.3 on 8 cores and an execution time of 10 s for both algorithms, we conclude that by using a parallel implementation on standard multicore CPUs, real-time path planning for UAVs is possible. Moreover, our rigorous comparison of the two algorithms shows, with statistical significance, that the GA produces superior trajectories to the PSO.read more
Citations
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Journal ArticleDOI
DEDG: Cluster-Based Delay and Energy-Aware Data Gathering in 3D-UWSN with Optimal Movement of Multi-AUV
Reem Alkanhel,Amir Chaaf,Nagwan M. Abdel Samee,Manal Abdullah Alohali,Mohammed Saleh Ali Muthanna,Dmitry Poluektov,Ammar Muthanna +6 more
TL;DR: This work addresses the idea of reducing energy and delay by incorporating an AUVs-assisted, three-dimensional UWSN (3D-UWSN) called DEDG 3D- UWSN and a weighted method is presented to transfer emergency events by selecting forwarders to achieve better efficiency.
Book ChapterDOI
Intelligent Routing Algorithm Using Genetic Algorithm (IRAGA)
Nibras Abdullah,Nibras Abdullah,Ola A. Al-wesabi,Ola A. Al-wesabi,Mahmoud Baklizi,Mohammed M. Kadhum +5 more
TL;DR: IRAGA offers a better solution in a few iteration compared to other genetic algorithm-based routing protocols, such as genetic load balancing routing, adaptive routing method based on GA with QoS, and multi-constraint QoS Unicast Routing using GA, in terms of the time taken to find the feasible path.
Journal ArticleDOI
UAV Collision Avoidance Considering No-Fly-Zones
TL;DR: This paper validates the minimum separation and efficiency of the proposed algorithm both analytically and numerically.
Journal ArticleDOI
Enhanced Particle Swarm Optimization for Path Planning of Unmanned Aerial Vehicles
Kai Yit Kok,Parvathy Rajendran +1 more
TL;DR: Analysis of the impact of input parameters on the PSO performance in UAV path planning using various complex terrain maps with adequate repetitions to solve the tuning issues shows that inertial weight parameter is insignificant, and a 1.4 acceleration coefficient is optimum for UAV paths planning.
Proceedings ArticleDOI
Differential evolution for optimizing motion planning of mobile robot
Cong Hung Do,Huei-Yung Lin +1 more
TL;DR: The path smoothening B-spline technique is integrated with the DE approach to provide traversable path for mobile robots and has been implemented and evaluated on an Aria mobile robot in both simulated and real environments.
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Journal ArticleDOI
A new vibrational genetic algorithm enhanced with a Voronoi diagram for path planning of autonomous UAV
TL;DR: A new optimization algorithm called multi-frequency vibrational genetic algorithm (mVGA) that can be used to solve the path planning problems of autonomous unmanned aerial vehicles (UAVs) is significantly improved.