UAV obstacle avoidance with PID control based on improved sparrow search algorithm

Hongze Wang

doi:10.1117/12.3027187

1 April 2024 UAV obstacle avoidance with PID control based on improved sparrow search algorithm

Hongze Wang

Proceedings Volume 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024); 130770S (2024) https://doi.org/10.1117/12.3027187
Event: 4th International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 2024, Chicago, IL, United States

Abstract

Research on Unmanned Aerial Vehicle (UAV) path planning is crucial for enhancing autonomous flight capabilities. Bioinspired heuristic algorithms have been proven to effectively solve such complex problems. The heuristic algorithm selected in this paper is the Sparrow Search Algorithm. However, due to the limitations of this original algorithm, i.e., the inclination to become trapped in local optima, low search accuracy, and insufficient population diversity, improvements are necessary. To address these shortcomings, this paper introduces the Improved Tent Chaotic Mapping, Opposite-Based Learning strategy (OBL), Gaussian-Cauchy mutation mechanism, and Adaptive adjustment strategy for discoverers and joiners to improve the original algorithm. The improved algorithm is named the Chaotic Mapping Adaptive Mutation-Sparrow Search Algorithm (CMAM-SSA). This algorithm is applied to UAV path planning in MATLAB simulations, combined with a simulation environment featuring mountainous terrain modeling and threat areas. The cost function integrates external environmental constraints, UAV performance limitations, and path planning objectives. Furthermore, a six-degree-of-freedom UAV path tracker is implemented using PID control on the Simulink platform. The simulation outcomes demonstrate that the CMAM-SSA algorithm exhibits a more rapid convergence rate and superior accuracy, affirming its effectiveness and superiority. The excellent performance of the Simulink path tracker provides further validation for the proposed improvements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongze Wang "UAV obstacle avoidance with PID control based on improved sparrow search algorithm", Proc. SPIE 13077, Fourth International Conference on Signal Processing and Machine Learning (CONF-SPML 2024), 130770S (1 April 2024); https://doi.org/10.1117/12.3027187

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KEYWORDS

Unmanned aerial vehicles

Simulations

Detection and tracking algorithms

3D tracking

MATLAB

Simulink

Mathematical optimization

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