Unmanned Aerial Vehicles Following Photography Path Planning Technology Based on Kinematic and Adaptive Models

As a representative invention of modern intelligent technology, unmanned aerial vehicles are receiving more and more attention in various fields. However, unmanned aerial vehicles cannot autonomously track path planning based on dynamic changes in conventional path planning. To address the aforementioned issues, this study proposes a path-planning algorithm for unmanned aerial vehicles following photography based on kinematic and adaptive models. A global coordinate system and an aircraft coordinate system are constructed based on the motion relationship between the unmanned aerial vehicles and the tracking target, and the two are converted into a horizontal projection coordinate system to digitize the observed data. On this basis, an adaptive control model is established based on the circular tracking path planning algorithm, and finally, simulation experiments and practical application tests are conducted in combination with the unmanned aerial vehicles following and shooting planning algorithm. The results showed that the best fitness of the proposed algorithm compared with the other two algorithms was 97.56, 93.87, and 92.79, and the path time and average speed of the studied algorithm were 38s and 3.4m/s, which were better than the other two algorithms. In the real machine experiment, there were six circular paths planned by the research algorithm, and the relative distance between the unmanned aerial vehicles and the target was within the range of 200m-600m. The actual trajectory had a high degree of overlap with the model planned trajectory. Research has shown that the proposed algorithm not only stabilizes the illumination angle within an effective range in path planning, but also has high convergence and superior path planning performance in practical applications.

@article{Xiao2024,
  title     = {Unmanned Aerial Vehicles Following Photography Path Planning Technology Based on Kinematic and Adaptive Models},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Sa Xiao},
  doi       = {10.14569/IJACSA.2024.0150772},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150772}
}