Planning unmanned aerial vehicle's path for maximum information collection using evolutionary algorithms

Date

2011-01-01

Author

Ergezer, Halit
Leblebicioğlu, Mehmet Kemal

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

129
views

0
downloads

Path planning is a problem of designing the path the vehicle is supposed to follow in such a way that a certain objective is optimized. In our study the objective is to maximize collected amount of information from Desired Regions (DR), meanwhile flying over the Forbidden Regions is avoided. In this paper, the path planning problem for single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators; Pull-to-Desired-Region (PTDR), Push-From-Forbidden-Region (PFFR), Pull-to-Finish-Point (PTFP). Besides these newly proposed operators, mutation and crossover operators have been used. The algorithm has been tested using two different scenarios and obtained results are presented in section 5. The 6 Degree-of-Freedom equation of motion has been used. The equations of motion of 12 state equations and the autopilot have been simulated in MATLAB/Simulink. Unlike previous studies in this field, we try to maximize collected information, instead of minimizing total mission time. © 2011 IFAC.

Subject Keywords

Evolutionary algorithms, Path planning, Unmanned aerial vehicles

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866760680&origin=inward
https://hdl.handle.net/11511/102357

Journal

IFAC Proceedings Volumes (IFAC-PapersOnline)

DOI

https://doi.org/10.3182/20110828-6-it-1002.02977

Collections

Department of Electrical and Electronics Engineering, Article

Suggestions

OpenMETU
Core

3D path planning for unmanned aerial vehicles Halit, Ergezer; Leblebicioğlu, Mehmet Kemal (2013-05-31) Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path, the objectives are to maximize the collected information from Desired Regions (DR) while avoiding flying over Forbidden Regions (FR) and reaching the destination. In this paper, the path planning problem for a multiple Unmanned Air Vehicles (UAVs) is studied with the proposal of novel evolutionary operators. The initial populations seed-path for each UAV have been obtained both by utilizing th...
Path Planning for UAVs for Maximum Information Collection Ergezer, Halit; Leblebicioğlu, Mehmet Kemal (2013-01-01) Path planning considers the problem of designing the path a vehicle is supposed to follow. Along the designed path the objectives are to maximize the collected information (CI) from desired regions (DR), while avoiding flying over forbidden regions (FR) and reaching the destination. The path planning problem for a single unmanned air vehicle (UAV) is studied with the proposal of novel evolutionary operators: pull-to-desired-region (PTDR), push-from-forbidden-region (PFFR), and pull-to-final-point (PTFP). In...
Comparison of 3D Versus 4D Path Planning for Unmanned Aerial Vehicles Cicibas, Halil; Demir, Kadir Alpaslan; ARICA, NAFİZ (2016-11-01) This research compares 3D versus 4D (three spatial dimensions and the time dimension) multi-objective and multi-criteria path-planning for unmanned aerial vehicles in complex dynamic environments. In this study, we empirically analyse the performances of 3D and 4D path planning approaches. Using the empirical data, we show that the 4D approach is superior over the 3D approach especially in complex dynamic environments. The research model consisting of flight objectives and criteria is developed based on int...
Optimization of reward shaping function based on genetic algorithm applied to a cross validated deep deterministic policy gradient in a powered landing guidance problem Nugroho, Larasmoyo; Andiarti, Rika; Akmeliawati, Rini; Kutay, Ali Türker; Larasati, Diva Kartika; Wijaya, Sastra Kusuma (2023-04-01) One major capability of a Deep Reinforcement Learning (DRL) agent to control a specific vehicle in an environment without any prior knowledge is decision-making based on a well-designed reward shaping function. An important but little-studied major factor that can alter significantly the training reward score and performance outcomes is the reward shaping function. To maximize the control efficacy of a DRL algorithm, an optimized reward shaping function and a solid hyperparameter combination are essential. ...
Safe and Efficient Path Planning for Omni-directional Robots using an Inflated Voronoi Boundary Aldahhan, Mohammed Rabeea Hashim; Schmidt, Klaus Verner (2019-11-01) Path planning algorithms for mobile robots are concerned with finding a feasible path between a start and goal location in a given environment without hitting obstacles. In the existing literature, important performance metrics for path planning algorithms are the path length, computation time and path safety, which is quantified by the minimum distance of a path from obstacles. The subject of this paper is the development of path planning algorithms for omni-directional robots, which have the ability ...

Citation Formats

H. Ergezer and M. K. Leblebicioğlu, “Planning unmanned aerial vehicle’s path for maximum information collection using evolutionary algorithms,” IFAC Proceedings Volumes (IFAC-PapersOnline), vol. 44, no. 1 PART 1, pp. 5591–5596, 2011, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84866760680&origin=inward.