Abstract
Over the past decades, Unmanned Aerial Vehicle (UAV) have been effectively adapted to perform disaster missions, agricultural and various societal applications. The path planning plays a crucial role in bringing autonomy to the UAVs to attain the designated tasks by avoiding collision in the obstacles prone regions. Optimal path planning of UAV is considered to be a challenging issue in real time navigation during obstacle prone environments. The present article focused on implementing a well-known A* and variant of A* namely MEA* algorithm to determine an optimal path in the varied obstacle regions for the UAV applications which is novel. Simulation is performed to investigate the performance of each algorithm with respect to comparing their execution time, total distance travelled and number of turns made to reach the source to target. Further, experimental flight trails are made to examine the performance of these algorithms using a UAV. The desired position, velocity and yaw of UAV is obtained based on the waypoints of optimal path planned data and effective navigation is performed. The simulation and experimental results are compared for confirming the effectiveness of these algorithms.
Highlights
Unmanned Aerial Vehicles (UAVs) are of its prominence in military, societal, environmental, disaster response, infrastructure monitoring and wild life monitoring missions [1,2,3,4,5]
Ground Control Station (GCS) provides optimal waypoints, which is determined through an optimal path planning algorithm that will be fed into the flight controller
Simulation analysis results of these two algorithms with respect to varied obstacles having narrow passages confirmed that MEA* outperforms A* algorithm in terms of execution of time, distance travelled and total number of turns it achieved
Summary
Unmanned Aerial Vehicles (UAVs) are of its prominence in military, societal, environmental, disaster response, infrastructure monitoring and wild life monitoring missions [1,2,3,4,5]. Popular path planning algorithms on a grid-based generally suit low-dimensional environments. In order to resolve the issue of following the grid edges of individual grid cells to determine an optimal path, a Theta* [17,18] algorithm was proposed. It was found that Theta* performance is much slower than A* in finding an optimal path Another variant of A*, namely Field D* (FD*), was formulated [19] to avoid following the edges of the grid cell. The Memory Efficient A* (MEA*) algorithm [15] is becoming popular because of the fact that it does not require large memory and avoiding the edges of the grid cells in contrast to A* for finding an optimal path. Comparative evaluation analysis will be performed with standard A* algorithm with MEA* to show case the effectiveness of the present approach
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal for Simulation and Multidisciplinary Design Optimization
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
