scholarly journals Complex Environment Path Planning for Unmanned Aerial Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5250
Author(s):  
Jing Zhang ◽  
Jiwu Li ◽  
Hongwei Yang ◽  
Xin Feng ◽  
Geng Sun
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Urban Environments ◽  
Planning Problem ◽  
Pruning Algorithm ◽  
Aerial Vehicles ◽  
Global Path Planning ◽  
Narrow Passage ◽  
Local Path ◽  
Path Planning Problem

Flying safely in complex urban environments is a challenge for unmanned aerial vehicles because path planning in urban environments with many narrow passages and few dynamic flight obstacles is difficult. The path planning problem is decomposed into global path planning and local path adjustment in this paper. First, a branch-selected rapidly-exploring random tree (BS-RRT) algorithm is proposed to solve the global path planning problem in environments with narrow passages. A cyclic pruning algorithm is proposed to shorten the length of the planned path. Second, the GM(1,1) model is improved with optimized background value named RMGM(1,1) to predict the flight path of dynamic obstacles. Herein, the local path adjustment is made by analyzing the prediction results. BS-RRT demonstrated a faster convergence speed and higher stability in narrow passage environments when compared with RRT, RRT-Connect, P-RRT, 1-0 Bg-RRT, and RRT*. In addition, the path planned by BS-RRT through the use of the cyclic pruning algorithm was the shortest. The prediction error of RMGM(1,1) was compared with those of ECGM(1,1), PCGM(1,1), GM(1,1), MGM(1,1), and GDF. The trajectory predicted by RMGM(1,1) was closer to the actual trajectory. Finally, we use the two methods to realize path planning in urban environments.

scholarly journals SmartPATH: An Efficient Hybrid ACO-GA Algorithm for Solving the Global Path Planning Problem of Mobile Robots

10.5772/58543 ◽  
2014 ◽  
Vol 11 (7) ◽  
pp. 94 ◽  
Author(s):  
Imen Châari ◽  
Anis Koubâa ◽  
Sahar Trigui ◽  
Hachemi Bennaceur ◽  
Adel Ammar ◽  
...  
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Planning Problem ◽  
Global Path Planning ◽  
Ga Algorithm ◽  
Path Planning Problem

scholarly journals Review on path planning algorithm for unmanned aerial vehicles

2021 ◽  
Vol 24 (2) ◽  
pp. 1017
Author(s):  
Nurul Saliha Amani Ibrahim ◽  
Faiz Asraf Saparudin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

Heterogeneous multi-unmanned aerial vehicle task planning: Simultaneous attacks on targets using the Pythagorean hodograph curve

2019 ◽  
Vol 233 (13) ◽  
pp. 4735-4749 ◽  
Author(s):  
Fei Yan ◽  
Xiaoping Zhu ◽  
Zhou Zhou ◽  
Yang Tang
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Unmanned Aerial Vehicle ◽  
Task Allocation ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Pythagorean Hodograph ◽  
Aerial Vehicle ◽  
Path Lengths ◽  
Target Path

The coupled task allocation and path planning problem for heterogeneous multiple unmanned aerial vehicles performing a search and attack mission involving obstacles and no-fly zones are addressed. The importance of the target is measured using a time-dependent value. A task allocation algorithm is proposed to obtain the maximum system utility. In the system utility function, the reward of the target, path lengths of unmanned aerial vehicles, and number of unmanned aerial vehicles to perform a simultaneous attack are considered. The path length of the unmanned aerial vehicles based on the Pythagorean hodograph curve is calculated, and it serves as the input for the task allocation problem. A resource management method for unmanned aerial vehicles is used, so that the resource consumption of the unmanned aerial vehicles can be balanced. To satisfy the requirement of simultaneous attacks and the unmanned aerial vehicle kinematic constraints in an environment involving obstacles and no-fly zones, a distributed cooperative particle swarm optimization algorithm is developed to generate flyable and safe Pythagorean hodograph curve trajectories for unmanned aerial vehicles to achieve simultaneous arrival. Monte Carlo simulations are conducted to demonstrate the performance of the proposed task allocation and path planning method.

scholarly journals Hybrid Form of Particle Swarm Optimization and Genetic Algorithm For Optimal Path Planning in Coverage Mission by Cooperated Unmanned Aerial Vehicles

2020 ◽  
Author(s):  
Hassan Haghighi ◽  
Seyed Hossein Sadati ◽  
S.M. Mehdi Dehghan ◽  
Jalal Karimi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Particle Swarm ◽  
Computational Time ◽  
Planning Problem ◽  
Hybrid Form ◽  
Swarm Optimization ◽  
Aerial Vehicles

In this paper, a new form of open traveling salesman problem (OTSP) is used for path planning for optimal coverage of a wide area by cooperated unmanned aerial vehicles (UAVs). A hybrid form of particle swarm optimization (PSO) and genetic algorithm (GA) is developed for the current path planning problem of multiple UAVs in the coverage mission. Three path-planning approaches are introduced through a group of the waypoints in a mission area: PSO, genetic algorithm, and a hybrid form of parallel PSO-genetic algorithm. The proposed hybrid optimization tries to integrate the advantages of the PSO, i.e. coming out from local minimal, and genetic algorithm, i.e. better quality solutions within a reasonable computational time. These three approached are compared in many scenarios with different levels of difficulty. Statistical analyses reveal that the hybrid algorithm is a more effective strategy than others for the mentioned problem.

Cooperative Path Planning of Unmanned Aerial Vehicles

2010 ◽  
Author(s):  
Antonios Tsourdos ◽  
Brian White ◽  
Madhavan Shanmugavel
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Aerial Vehicles
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