scholarly journals Path planning of multiple UAVs using MMACO and DE algorithm in dynamic environment

2020 ◽  
pp. 002029402091572
Author(s):  
Zain Anwar Ali ◽  
Han Zhangang ◽  
Di Zhengru
Keyword(s):  
Path Planning ◽  
Differential Evolution ◽  
Unmanned Aerial Vehicles ◽  
Ant Colony Optimization ◽  
Dynamic Environment ◽  
Optimal Solution ◽  
Ant Colony ◽  
Aerial Vehicles ◽  
Multiple Unmanned Aerial Vehicles ◽  
Maximum Minimum

Cooperative path planning of multiple unmanned aerial vehicles is a complex task. The collision avoidance and coordination between multiple unmanned aerial vehicles is a global optimal issue. This research addresses the path planning of multi-colonies with multiple unmanned aerial vehicles in dynamic environment. To observe the model of whole scenario, we combine maximum–minimum ant colony optimization and differential evolution to make metaheuristic optimization algorithm. Our designed algorithm, controls the deficiencies of present classical ant colony optimization and maximum–minimum ant colony optimization, has the contradiction among the excessive information and global optimization. Moreover, in our proposed algorithm, maximum–minimum ant colony optimization is used to lemmatize the pheromone and only best ant of each colony is able to construct the path. However, the path escape by maximum–minimum ant colony optimization and it treated as the object for differential evolution constraints. Now, it is ensuring to find the best global colony, which provides optimal solution for the entire colony. Furthermore, the proposed approach has an ability to increase the robustness while preserving the global convergence speed. Finally, the simulation experiment results are performed under the rough dynamic environment containing some high peaks and mountains.

Survey of Cooperative Path Planning for Multiple Unmanned Aerial Vehicles

2014 ◽  
Vol 668-669 ◽  
pp. 388-393 ◽  
Author(s):  
Xiao Ming Cheng ◽  
Dong Cao ◽  
Chun Tao Li
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Cooperative Control ◽  
Control Methods ◽  
Future Time ◽  
Research Directions ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Problem Description ◽  
Multiple Unmanned Aerial Vehicles

As an important part of cooperative control for multiple unmanned aerial vehicles (UAVs), cooperative path planning can get optimal flight path which can satisfy different constraints. Research on cooperative path planning for multiple UAVs is summarized in this paper. Firstly, problem description and constraints are given. Then, solution frameworks and path coordination approaches are summarized. After that, several control methods commonly used in formation of multiple UAVs are introduced respectively. Lastly, possible research directions in the future time are put forward.

scholarly journals Equilibrium Optimizer with Generalized Opposition-based Learning for Multiple Unmanned Aerial Vehicles Path Planning

2021 ◽  
Author(s):  
Yang Chen ◽  
Dechang Pi ◽  
Bi Wang ◽  
Ali Wagdy Mohamed ◽  
Junfu Chen
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Dimensional Space ◽  
Optimization Techniques ◽  
High Dimensional ◽  
Population Activity ◽  
Opposition Based Learning ◽  
Aerial Vehicles ◽  
Multiple Uavs ◽  
Multiple Unmanned Aerial Vehicles

Abstract Multiple Unmanned Aerial Vehicles (UAVs) path planning is the benchmark problem of multiple UAVs application, which belongs to the non-deterministic polynomial problem. Its objective is to require multiple UAVs flying safely to the goal position according to their specific start position in three-dimensional space. This issue can be defined as a high-dimensional optimization problem, the solution of which requires optimization techniques with global optimization capabilities. Equilibrium optimizer (EO) is a population-based meta-heuristic algorithm. In order to improve the optimization ability of EO to solve high dimensional problems, this paper proposes a modified equilibrium optimizer with generalized opposition-based learning (MGOEO), which improves the population activity by increasing the internal mutation and cross of the population. In addition, the generalized opposition-based learning is used to construct the population, which can effectively ensure that the algorithm has ability to jump out of the limitation of local optimal. Firstly, numerical experiments show that MGOEO has better optimization precision than EO and several other swarm intelligent algorithms. Then, the paths of UAVs are simulated in three different obstacle environments. The simulation results show that MGOEO can obtain safe and smooth paths, which are better than EO and other eight state-of-the-art optimization algorithms.

Sign in / Sign up

Export Citation Format

Share Document