论文标题
增强了基于教学学习的优化，用于三型无人机的3D路径计划
Enhanced Teaching-Learning-based Optimization for 3D Path Planning of Multicopter UAVs
论文作者
论文摘要
本文介绍了一种基于基于教学学习的优化（TLBO）技术的无人机（UAV）的新路径计划算法。我们首先定义一个目标函数，该目标函数纳入了对路径长度的要求以及无人机的运动和安全操作的约束，以将路径计划转换为优化问题。然后提出了名为多主体TLBO的优化算法以最大程度地减少公式的目标函数。该算法是基于TLBO开发的，但通过包括突变，精英选择和多主体训练的新操作增强，以提高解决方案质量并加快收敛速度。已经进行了与最先进的算法和实际无人机实验的比较，以评估所提出算法的性能。该结果证实了其在复杂操作环境中为无人机生成最佳，无碰撞和可飞行路径的有效性和有效性。
This paper introduces a new path planning algorithm for unmanned aerial vehicles (UAVs) based on the teaching-learning-based optimization (TLBO) technique. We first define an objective function that incorporates requirements on the path length and constraints on the movement and safe operation of UAVs to convert the path planning into an optimization problem. The optimization algorithm named Multi-subject TLBO is then proposed to minimize the formulated objective function. The algorithm is developed based on TLBO but enhanced with new operations including mutation, elite selection and multi-subject training to improve the solution quality and speed up the convergence rate. Comparison with state-of-the-art algorithms and experiments with real UAVs have been conducted to evaluate the performance of the proposed algorithm. The results confirm its validity and effectiveness in generating optimal, collision-free and flyable paths for UAVs in complex operating environments.
