Hybrid RRT/DE Algorithm for High Performance UCAV Path Planning

Elkazzaz, Fathy; Abozied, Mohammed A. H.; Hu, Chunming

doi:10.1145/3171592.3171618

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…When evaluated, the new algorithm showed improved performance by reducing the number of steering actions and the maximum curvature of the paths. In [18] a path planning algorithm that hybridizes the rapidly exploring random tree algorithm (RRTs) and the differential evolution (DE) algorithm was presented. The hybrid algorithm shows the capability to generate a fast and optimal 3D collision-free path under complex environments.…”

Section: Literature Reviewmentioning

confidence: 99%

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

et al. 2021

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With the extensive developments in autonomous vehicles (AV) and the increase of interest in artificial intelligence (AI), path planning is becoming a focal area of research. However, path planning is an NP-hard problem and its execution time and complexity are major concerns when searching for optimal solutions. Thus, the optimal trade-off between the shortest path and computing resources must be found. This paper introduces a path planning algorithm, tide path planning (TPP), which is inspired by the natural tide phenomenon. The idea of the gravitational attraction between the Earth and the Moon is adopted to avoid searching blocked routes and to find a shortest path. Benchmarking the performance of the proposed algorithm against rival path planning algorithms, such as A*, breadth-first search (BFS), Dijkstra, and genetic algorithms (GA), revealed that the proposed TPP algorithm succeeded in finding a shortest path while visiting the least number of cells and showed the fastest execution time under different settings of environment size and obstacle ratios.

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Section: Literature Reviewmentioning

confidence: 99%

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

et al. 2021

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“…CFO algorithms have demonstrated their effectiveness in finding the feasible or optimal path under complicated constraints. In some cases, multiple algorithms have been jointly used to find the path for UAV [73]. A comprehensive survey regarding four fundamental path planning families was provided by Yang Liang et al [74].…”

Section: Background and Related Workmentioning

confidence: 99%

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

Majeed

Lee

2018

Electronics

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This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for UAV can be computed without sacrificing the guarantees on near-optimality and speed. Furthermore, we generate a sparse visibility graph from the circumscribed space and find the initial path, which is subsequently optimized. The proposed algorithm effectively resolves the efficiency and optimality trade-off by searching the path only from the high priority circumscribed space of a map. The simulation results obtained from various maps, and comparison with the existing methods show the effectiveness of the proposed algorithm and verify the aforementioned claims.

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“…And in this research, the tuning algorithm involved is Artificial Bee Colony [22,23,24]. Two factors underlying the selection of the tuning method are the better convergence rate compared to Genetic Algorithm (GA) [25,26,27,28] and low computational cost similar to Particle Swarm Optimization [10,21,23,29] and Differential Evolution (DE) [22,23,30]. Thus, adaptive SVSF is scaled up and ready to deploy.…”

Section: Introductionmentioning

confidence: 99%

The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm

Suwoyo,

Hajar,

Indriyanti

et al. 2024

Sinergi

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The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.

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Hybrid RRT/DE Algorithm for High Performance UCAV Path Planning

Cited by 4 publications

References 16 publications

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

Tide-Inspired Path Planning Algorithm for Autonomous Vehicles

A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm

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