scholarly journals GAMPP: Genetic Algorithm for UAV Mission Planning Problems

2015 ◽  
pp. 167-176 ◽  
Author(s):  
Gema Bello-Orgaz ◽  
Cristian Ramirez-Atencia ◽  
Jaime Fradera-Gil ◽  
David Camacho
Keyword(s):  
Genetic Algorithm ◽  
Mission Planning ◽  
Planning Problems

scholarly journals Method of the Mission Planning for the Communication between the Small Satellite Clusters

2019 ◽  
Vol 3 (5) ◽  
Author(s):  
Duoduo Yao
Keyword(s):  
Genetic Algorithm ◽  
Satellite Communication ◽  
Mission Planning ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Satellite Clusters ◽  
The Mathematical Model ◽  
Planning Problems ◽  
Astronomical Satellite ◽  
Single Objective

Mission planning of space astronomical satellite is a complex optimization problem, which is to determine the communication activities needed by space astronomical and research the in-orbit plan. By abstracting the relevant elements of the mission planning problems of space astronomical satellite and establishing the mathematical model of mission planning of the space astronomical satellite, we introduce the Genetic Algorithm and design the single-objective Genetic Algorithm based on the communication mission window. In addition, based on the Genetic Algorithm, a multi-objective Genetic Algorithm based on the sequence of communication window is designed, which improves the coding ability of Genetic Algorithm and improves the flexibility and applicability of planning effect. From the results of planning simulation, this paper not only innovatively introduces Genetic Algorithm into mission planning of satellite and ground data in order to improve the efficiency of mission planning of space astronomical satellite, but also optimizes single-objective mission to multi-objective mission, which improves the applicability of mission planning of satellite communication and provides reference for other relevant researches in the future.

Collaborative Scheduling of Algorithms for Path Planning of Unmanned Systems

2021 ◽  
Vol 01 ◽  
Author(s):  
Ying Li ◽  
Chubing Guo ◽  
Jianshe Wu ◽  
Xin Zhang ◽  
Jian Gao ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Unmanned Systems ◽  
Ant Colony Optimization Algorithm ◽  
A Algorithm ◽  
Collaborative Scheduling ◽  
Simulation Results ◽  
Planning Problems ◽  
Effective Path

Background: Unmanned systems have been widely used in multiple fields. Many algorithms have been proposed to solve path planning problems. Each algorithm has its advantages and defects and cannot adapt to all kinds of requirements. An appropriate path planning method is needed for various applications. Objective: To select an appropriate algorithm fastly in a given application. This could be helpful for improving the efficiency of path planning for Unmanned systems. Methods: This paper proposes to represent and quantify the features of algorithms based on the physical indicators of results. At the same time, an algorithmic collaborative scheme is developed to search the appropriate algorithm according to the requirement of the application. As an illustration of the scheme, four algorithms, including the A-star (A*) algorithm, reinforcement learning, genetic algorithm, and ant colony optimization algorithm, are implemented in the representation of their features. Results: In different simulations, the algorithmic collaborative scheme can select an appropriate algorithm in a given application based on the representation of algorithms. And the algorithm could plan a feasible and effective path. Conclusion: An algorithmic collaborative scheme is proposed, which is based on the representation of algorithms and requirement of the application. The simulation results prove the feasibility of the scheme and the representation of algorithms.

Robot path planning based on genetic algorithm with hybrid initialization method

2021 ◽  
pp. 1-16
Author(s):  
Zhaojun Zhang ◽  
Rui Lu ◽  
Minglong Zhao ◽  
Shengyang Luan ◽  
Ming Bu
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Iteration Process ◽  
Initial Population ◽  
Local Optimum ◽  
Complex Environments ◽  
Random Initialization ◽  
Planning Problems ◽  
Complete Path

The research of path planning method based on genetic algorithm (GA) for the mobile robot has received much attention in recent years. GA, as one evolutionary computation model, mimics the process of natural evolution and genetics. The quality of the initial population plays an essential role in improving the performance of GA. However, when GA based on a random initialization method is applied to path planning problems, it will lead to the emergence of infeasible solutions and reduce the performance of the algorithm. A novel GA with a hybrid initialization method, termed NGA, is proposed to solve this problem in this paper. In the initial population, NGA first randomly selects three free grids as intermediate nodes. Then, a part of the population uses a random initialization method to obtain the complete path. The other part of the population obtains the complete path using a greedy-related method. Finally, according to the actual situation, the redundant nodes or duplicate paths in the path are deleted to avoid the redundant paths. In addition, the deletion operation and the reverse operation are also introduced to the NGA iteration process to prevent the algorithm from falling into the local optimum. Simulation experiments are carried out with other algorithms to verify the effectiveness of the NGA. Simulation results show that NGA is superior to other algorithms in convergence accuracy, optimization ability, and success rate. Besides, NGA can generate the optimal feasible paths in complex environments.

scholarly journals A Knowledge-Based Simulated Annealing Algorithm to Multiple Satellites Mission Planning Problems

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Da-Wei Jin ◽  
Li-Ning Xing
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Simulated Annealing Algorithm ◽  
Mission Planning ◽  
Knowledge Based ◽  
Combination Optimization ◽  
Annealing Algorithm ◽  
The Given ◽  
Planning Problems ◽  
Annealing Method

The multiple satellites mission planning is a complex combination optimization problem. A knowledge-based simulated annealing algorithm is proposed to the multiple satellites mission planning problems. The experimental results suggest that the proposed algorithm is effective to the given problem. The knowledge-based simulated annealing method will provide a useful reference for the improvement of existing optimization approaches.

scholarly journals Mission Planning for Unmanned Aerial Vehicles Based on Voronoi Diagram-Tabu Genetic Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Wei Tan ◽  
Yong-jiang Hu ◽  
Yue-fei Zhao ◽  
Wen-guang Li ◽  
Xiao-meng Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Unmanned Aerial Vehicles ◽  
Voronoi Diagram ◽  
Pso Algorithm ◽  
Mission Planning ◽  
Cooperative System ◽  
Aerial Vehicles ◽  
Rendezvous Problem ◽  
Aerial Vehicle ◽  
Multi Uav

Unmanned aerial vehicles (UAVs) are increasingly used in different military missions. In this paper, we focus on the autonomous mission allocation and planning abilities for the UAV systems. Such abilities enable adaptation to more complex and dynamic mission environments. We first examine the mission planning of a single unmanned aerial vehicle. Based on that, we then investigate the multi-UAV cooperative system under the mission background of cooperative target destruction and show that it is a many-to-one rendezvous problem. A heterogeneous UAV cooperative mission planning model is then proposed where the mission background is generated based on the Voronoi diagram. We then adopt the tabu genetic algorithm (TGA) to obtain multi-UAV mission planning. The simulation results show that the single-UAV and multi-UAV mission planning can be effectively realized by the Voronoi diagram-TGA (V-TGA). It is also shown that the proposed algorithm improves the performance by 3% in comparison with the Voronoi diagram-particle swarm optimization (V-PSO) algorithm.

Design and simulation in SAR satellites’ task planning system using genetic algorithm with entropy operator

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Wan Liu ◽  
Zeyu Li ◽  
Li Chen ◽  
Dexin Zhang ◽  
Xiaowei Shao
Keyword(s):  
Genetic Algorithm ◽  
Satellite Observation ◽  
Operating Costs ◽  
Planning System ◽  
Task Planning ◽  
Content Type ◽  
Emergency Rescue ◽  
Planning Algorithm ◽  
Planning Problems ◽  
Fitness Calculation

Purpose This paper aims to innovatively propose to improve the efficiency of satellite observation and avoid the waste of satellite resources, a genetic algorithm with entropy operator (GAE) of synthetic aperture radar (SAR) satellites’ task planning algorithm. Design/methodology/approach The GAE abbreviated as GAE introduces the entropy value of each orbit task into the fitness calculation of the genetic algorithm, which makes the orbit with higher entropy value more likely to be selected and participate in the remaining process of the genetic algorithm. Findings The simulation result shows that in a condition of the same calculate ability, 85% of the orbital revisit time is unchanged or decreased and 30% is significantly reduced by using the GAE compared with traditional task planning genetic algorithm, which indicates that the GAE can improve the efficiency of satellites’ task planning. Originality/value The GAE is an optimization of the traditional genetic algorithm. It combines entropy in thermodynamics with task planning problems. The algorithm considers the whole lifecycle of task planning and gets the desired results. It can greatly improve the efficiency of task planning in observation satellites and shorten the entire task execution time. Then, using the GAE to complete SAR satellites’ task planning is of great significance in reducing satellite operating costs and emergency rescue, which brings certain economic and social benefits.

Solving complex multi-UAV mission planning problems using multi-objective genetic algorithms

2016 ◽  
Vol 21 (17) ◽  
pp. 4883-4900 ◽  
Author(s):  
Cristian Ramirez-Atencia ◽  
Gema Bello-Orgaz ◽  
María D. R-Moreno ◽  
David Camacho
Keyword(s):  
Genetic Algorithms ◽  
Mission Planning ◽  
Multi Objective ◽  
Planning Problems ◽  
Multi Uav
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