GenPath - A Genetic Multi-Round Path Planning Algorithm for Aerial Vehicles

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.

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Cooperative Search and Path Planning of Multi-Unmanned Aerial Vehicles in Uncertain Environment Based on Bayes Theory

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.336-338.843 ◽

2013 ◽

Vol 336-338 ◽

pp. 843-846 ◽

Cited By ~ 2

Author(s):

Xia Chen ◽

Jing Zhang ◽

Zhen Yu Lu

Keyword(s):

Path Planning ◽

Physical Properties ◽

Unmanned Aerial Vehicles ◽

Search Theory ◽

A Algorithm ◽

Uncertain Environment ◽

Aerial Vehicles ◽

Bayes Theory ◽

Planning Algorithm ◽

Path Planning Algorithm

In order to solve the question of cooperative searching target in uncertain environment, this paper comes up with a algorithm. Firstly it analysis the uncertainty about measure of UAV sensors and environment, we built the information model of uncertain environment. Then, considering about UAV physical properties and optimal search theory, it designs the award function, gives the path planning algorithm of cooperative searching based on the Bayes theory. The algorithm ensures that the UAV formation could search unknown environment as far as possible, evade the known environment and avoid no-fly zone completely. Finally, the simulation proves the rationality and effectiveness of algorithm.

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Ellipsoidal Path Planning for Unmanned Aerial Vehicles

Applied Sciences ◽

10.3390/app11177997 ◽

2021 ◽

Vol 11 (17) ◽

pp. 7997

Author(s):

Carlos Villaseñor ◽

Alberto A. Gallegos ◽

Gehova Lopez-Gonzalez ◽

Javier Gomez-Avila ◽

Jesus Hernandez-Barragan ◽

...

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicles ◽

Clustering Algorithms ◽

Computational Cost ◽

Mapping Algorithm ◽

Planning Strategy ◽

Aerial Vehicles ◽

Planning Algorithm ◽

Trained Neural Network ◽

Path Planning Algorithm

The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.

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