【摘要】 针对智能飞行器在复杂的飞行环境下不能精确定位校正而无法到达目的地的问题,提出了一种飞行器定位误差校正策略和飞行器转弯控制策略。当校正点集合存在问题校正点时,在保证成功到达概率的前提下,尽可能减少校正次数和路程,提出了一种概率参数优化策略,通过采用蒙特卡罗算法和NSGA2遗传算法来计算真实概率和航迹路线;为满足实际应用,建立了以飞行器航迹最短和校正次数最少的多目标优化的数学模型,并设计了一种圆环形包络面的数据预处理算法以提高模型的求解效率,最后进行了仿真计算和代码优化,仿真结果表明,提出的策略和算法能够有效地给出更为满意的航迹规划路线。更多还原

【Abstract】 Aiming at the problem that the intelligent vehicle can not accurately locate and correct and can not reach the destination in complex flight environment, an aircraft positioning error correction strategy and aircraft turning control strategy are proposed.when there are some problem points in the calibration point set, on the premise of ensuring the probability of successful arrival, the number and distance of calibration should be reduced as much as possible.A strategy of probability parameter optimization was proposed, which used Monte Carlo algorithm and NSGA2 genetic algorithm to calculate the true probability and track route; In order to meet the practical application, the mathematical model of multi-objective optimization with the shortest flight path and the least correction times was established, and a data preprocessing algorithm for the toroidal envelope was designed to improve the efficiency of the model.Finally, the simulation calculation and code optimization were carried out.The simulation results show that the proposed strategy and algorithm can effectively give a more satisfactory path for the trajectory planning line.更多还原