A Novel Sparrow Particle Swarm Algorithm (SPSA) for Unmanned Aerial Vehicle Path Planning

Unmanned aerial vehicle (UAV) has been widely used in various fields, and meeting practical high-quality flight paths is one of the crucial functions of UAV. Many algorithms have the problem of too fast convergence and premature in UAV path planning. This study proposed a sparrow particle swarm algorithm for UAV path planning, the SPSA. The algorithm selects a suitable model for path initialization, changes the discoverer position update, and reinforces the influence of start-end line on path search, which can significantly reduce blind search. The number of target points reached is increased by adaptive variable speed escapes in areas of deadlock. In this case, the planned trajectory will fluctuate, and adaptive oscillation optimization can effectively reduce the fluctuation of the path. Finally, the optimal path is simplified, and the path nodes are interpolated with cubic splines to improve the smoothness of the path, which improves the smoothness of the UAV flight trajectory and makes it more suitable for use as the UAV real flight trajectory. By comparison, it can be concluded that the improved SPSA has good convergence speed and better search results and can avoid local optimality.

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Optimal path planning for an unmanned aerial vehicle under navigation relayed by multiple stations to intercept a moving target

2017 IEEE 56th Annual Conference on Decision and Control (CDC) ◽

10.1109/cdc.2017.8264057 ◽

2017 ◽

Cited By ~ 1

Author(s):

Mingfeng Qi ◽

Lihua Dou ◽

Bin Xin ◽

Jie Chen

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicle ◽

Optimal Path ◽

Moving Target ◽

Optimal Path Planning ◽

Aerial Vehicle

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Path planning based on improved multi-objective particle swarm algorithm

2020 IEEE 5th Information Technology and Mechatronics Engineering Conference (ITOEC) ◽

10.1109/itoec49072.2020.9141911 ◽

2020 ◽

Author(s):

YiQin Duan ◽

Yi Zhang ◽

Bin Zhang ◽

Yusen Wang

Keyword(s):

Path Planning ◽

Particle Swarm ◽

Particle Swarm Algorithm ◽

Multi Objective ◽

Swarm Algorithm

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Unmanned aerial vehicle dynamic path planning in an uncertain environment

Robotica ◽

10.1017/s0263574714000514 ◽

2014 ◽

Vol 33 (3) ◽

pp. 611-621 ◽

Cited By ~ 12

Author(s):

Min Yao ◽

Min Zhao

Keyword(s):

Path Planning ◽

Unmanned Aerial Vehicle ◽

Optimal Path ◽

Current Position ◽

Direction Angle ◽

Dynamic Path Planning ◽

Aerial Vehicle ◽

Dynamic Path ◽

Rolling Optimization ◽

Feedback Algorithm

SUMMARYAn unmanned aerial vehicle (UAV) dynamic path planning method is proposed to avoid not only static threats but also mobile threats. The path of a UAV is planned or modified by the potential trajectory of the mobile threat, which is predicted by its current position, velocity, and direction angle, because the positions of the UAV and mobile threat are dynamically changing. In each UAV planning path, the UAV incurs some costs, including control costs to change the direction angle, route costs to bypass the threats, and threat costs to acquire some probability to be destroyed by threats. The model predictive control (MPC) algorithm is used to determine the optimal or sub-optimal path with minimum overall costs. The MPC algorithm is a rolling-optimization feedback algorithm. It is used to plan the UAV path in several steps online instead of one-time offline to avoid sudden and mobile threats dynamically. Lastly, solution implementation is described along with several simulation results that demonstrate the effectiveness of the proposed method.

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Simulation and experimental approach for optimal path planning of UAV using A* and MEA* algorithms

International Journal for Simulation and Multidisciplinary Design Optimization ◽

10.1051/smdo/2021024 ◽

2021 ◽

Vol 12 ◽

pp. 24

Author(s):

Balasubramanian Esakki ◽

Gayatri Marreddy ◽

M. Sai Ganesh ◽

E. Elangovan

Keyword(s):

Path Planning ◽

Present Article ◽

Unmanned Aerial Vehicle ◽

Execution Time ◽

Crucial Role ◽

Experimental Approach ◽

Optimal Path ◽

Optimal Path Planning ◽

The Past ◽

Aerial Vehicle

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.

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Improved Particle Swarm Optimization of Three-Dimensional Path Planning for Fixed Wing Unmanned Aerial Vehicle

VNU Journal of Science Computer Science and Communication Engineering ◽

10.25073/2588-1086/vnucsce.235 ◽

2019 ◽

Vol 35 (2) ◽

Author(s):

Giang Thi - Huong Dang ◽

Quang - Huy Vuong ◽

Minh Hoang Ha ◽

Minh - Trien Pham

Keyword(s):

Particle Swarm Optimization ◽

Path Planning ◽

Unmanned Aerial Vehicle ◽

Particle Swarm ◽

Adaptive Mutation ◽

Planning Problem ◽

Swarm Optimization ◽

3D Environment ◽

Aerial Vehicle ◽

Improved Pso

Path planning for Unmanned Aerial Vehicle (UAV) targets at generating an optimal global path to the target, avoiding collisions and optimizing the given cost function under constraints. In this paper, the path planning problem for UAV in pre-known 3D environment is presented. Particle Swarm Optimization (PSO) was proved the efficiency for various problems. PSO has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. In this paper, the improved PSO with adaptive mutation to overcome its drawback in order to applied PSO the UAV path planning in real 3D environment which composed of mountains and constraints. The effectiveness of the proposed PSO algorithm is compared to Genetic Algorithm, standard PSO and other improved PSO using 3D map of Daklak, Dakrong and Langco Beach. The results have shown the potential for applying proposed algorithm in optimizing the 3D UAV path planning. Keywords: UAV, Path planning, PSO, Optimization.

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