Comparison of Parallel Genetic Algorithm and Particle Swarm Optimization for Real-Time UAV Path Planning
TL;DR: 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.
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TL;DR: This article describes the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning, and provides insights into widespread and emerging modeling approaches to civil applications of UAVs.
Abstract: Unmanned aerial vehicles (UAVs), or aerial drones, are an emerging technology with significant market potential. UAVs may lead to substantial cost savings in, for instance, monitoring of difficult-to-access infrastructure, spraying fields and performing surveillance in precision agriculture, as well as in deliveries of packages. In some applications, like disaster management, transport of medical supplies, or environmental monitoring, aerial drones may even help save lives. In this article, we provide a literature survey on optimization approaches to civil applications of UAVs. Our goal is to provide a fast point of entry into the topic for interested researchers and operations planning specialists. We describe the most promising aerial drone applications and outline characteristics of aerial drones relevant to operations planning. In this review of more than 200 articles, we provide insights into widespread and emerging modeling approaches. We conclude by suggesting promising directions for future research.
576 citations
Cites background from "Comparison of Parallel Genetic Algo..."
...Thus, we do not include articles on obstacle-avoiding path planning from a given starting point to a given end point [12, 63, 108, 153, 194, 203, 220, 227, 245, 306] or on planning trajectories of drones flying in formation [220]....
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TL;DR: Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources, then paints a landscape of the scheduling problem and solutions, and a comprehensive survey of state-of-the-art approaches is presented systematically.
Abstract: A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon.
416 citations
Cites methods from "Comparison of Parallel Genetic Algo..."
...So far, real-time optimization is still a challenge to EC algorithms because of the population- and iteration-based characteristics of EC algorithms, despite efforts made on real-time algorithm design [Roberge et al. 2013]....
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...As the problem of cloud resource scheduling is seen NP-hard, its intractability is best tackled by an EC algorithm [Roberge et al. 2013; Shen et al. 2014]....
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TL;DR: The results suggest that unsupervised pre-training is a promising feature in RUL predictions subjected to multiple operating conditions and fault modes.
322 citations
TL;DR: In this paper, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed, inspired from the genetic algorithm, which mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF.
Abstract: The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.
304 citations
Cites background from "Comparison of Parallel Genetic Algo..."
...For the performances of intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear mixed-integer optimization problems, GA has been wildly used [22]–[24]....
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01 Apr 2019
TL;DR: The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length, and it exhibits a better performance regarding path length.
Abstract: In this paper, a membrane evolutionary artificial potential field (memEAPF) approach for solving the mobile robot path planning problem is proposed, which combines membrane computing with a genetic algorithm (membrane-inspired evolutionary algorithm with one-level membrane structure) and the artificial potential field method to find the parameters to generate a feasible and safe path. The memEAPF proposal consists of delimited compartments where multisets of parameters evolve according to rules of biochemical inspiration to minimize the path length. The proposed approach is compared with artificial potential field based path planning methods concerning to their planning performance on a set of twelve benchmark test environments, and it exhibits a better performance regarding path length. Experiments to demonstrate the statistical significance of the improvements achieved by the proposed approach in static and dynamic environments are shown. Moreover, the implementation results using parallel architectures proved the effectiveness and practicality of the proposal to obtain solutions in considerably less time.
257 citations
References
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Abstract: An Introduction to Genetic Algorithms is one of the rare examples of a book in which every single page is worth reading. The author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues, yet the book is concise (200 pages) and readable. Although Mitchell explicitly states that her aim is not a complete survey, the essentials of genetic algorithms (GAs) are contained: theory and practice, problem solving and scientific models, a \"Brief History\" and \"Future Directions.\" Her book is both an introduction for novices interested in GAs and a collection of recent research, including hot topics such as coevolution (interspecies and intraspecies), diploidy and dominance, encapsulation, hierarchical regulation, adaptive encoding, interactions of learning and evolution, self-adapting GAs, and more. Nevertheless, the book focused more on machine learning, artificial life, and modeling evolution than on optimization and engineering.
7,098 citations
TL;DR: An algorithm is given for computer control of a digital plotter that may be programmed without multiplication or division instructions and is efficient with respect to speed of execution and memory utilization.
Abstract: An algorithm is given for computer control of a digital plotter.
The algorithm may be programmed without multiplication or division instructions and is efficient with respect to speed of execution and memory utilization.
2,257 citations
Book•
26 Feb 2021
TL;DR: Introduction to Flight 6e Chapter 1: The First Aeronautical Engineers Chapter 2: Fundamental Thoughts Chapter 3: The Standard Atmosphere Chapter 4: Basic Aerodynamics Chapter 5: Airfoils, Wings, and Other Aerodynamics Shapes
Abstract: Introduction to Flight 6e Chapter 1: The First Aeronautical Engineers Chapter 2: Fundamental Thoughts Chapter 3: The Standard Atmosphere Chapter 4: Basic Aerodynamics Chapter 5: Airfoils, Wings, and Other Aerodynamics Shapes Chapter 6: Elements of Airplane Performance Chapter 7: Principles of Stability and Control Chapter 8: Space Flight (Astronautics) Chapter 9: Propulsion Chapter 10: Flight Vehicle Structures and Materials Chapter 11: Hypersonic Vehicles Appendix A Standard Atmosphere, SI Units Appendix B Standard Atmosphere, English Engineering Units Appendix C Symbols and Conversion Factors Appendix D Airfoil Data
808 citations
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.
241 citations