DOI: https://doi.org/10.36347/sjet.2024.v12i11.002

Pages: 332-336

Quadrotors, known for their agility and ability to perform intricate manoeuvres, have become increasingly popular in the last decade. Precisely tracking complex flight paths has been a major research focus. While trajectory optimization offers a continuous search space and allows for utilizing problem-specific knowledge, its reliance on local optimization methods necessitates a global planner for generating feasible trajectories from arbitrary starting points to goals. Designing such trajectories for quadrotors, with their five degrees of freedom and intricate dynamic constraints (e.g., limitations on state variables), presents a significant challenge. Existing global planners struggle to solve these trajectory generation problems for complex dynamical systems in a practical timeframe. This paper explores the use of metaheuristic optimization for planning quadrotor flight trajectories, specifically focusing on the FSA and FSASCA algorithms. These methods have demonstrated promising results in specific scenarios, but their performance depends heavily on the application and its constraints. The paper emphasizes a trade-off between solution quality and execution speed: seeking faster execution may require sacrificing solution quality, while prioritizing optimality may lead to longer execution times.