考虑安全飞行通道约束的无人机飞行轨迹多目标优化策略

doi:10.16183/j.cnki.jsjtu.2021.154

摘要/Abstract

摘要：

针对无人机在复杂环境下难以规划出兼顾平滑性和安全性等指标的时域连续轨迹问题,基于安全飞行通道提出了一种多目标轨迹规划算法.在基于快速拓展随机树(RRT)改进的RRT^*算法生成的初始离散路径点基础上,建立以凸多面体集合表示的安全飞行通道;根据轨迹在安全飞行通道内部的约束建立安全项目标函数,结合飞行平滑性、动力学特性、飞行时间等性能指标,建立加权多目标优化函数;采用基于梯度下降的凸优化算法,对离散路径点的位置、速度、加速度及轨迹的时间分配进行优化,生成分段多项式表示的时域连续轨迹.基于煤矿井下等复杂环境对算法的有效性及相关性能进行试验及对比验证,结果表明:相比现有算法,本文算法在综合性能上有一定的提升.

关键词: 无人机, 轨迹规划, 安全飞行通道, 凸优化

Abstract:

Aimed at the problem of generating a smooth, safe, and dynamically feasible continuous-time trajectory for unmanned aerial vehicles (UAV) in complex environments, a trajectory planning algorithm is proposed to minimize a multi-objective function based on safe flight corridors. The safe flight corridor represented by a collection of convex polyhedra is built based on the initial discrete waypoints generated by the improved rapidly-exploring random tree(RRT), namely the RRT^* algorithm. The safety objective function is established according to the constraints of limiting the trajectory inside safe flight corridors. In combination with the flight smoothness, dynamic characteristics, and time performance, a multi-objective function is built. The gradient-based convex optimization algorithm is used to derive the continuous-time trajectory expressed as a piece-wise polynomial by optimizing the position, velocity, acceleration of waypoints, and time allocation. The effectiveness and performance of the proposed algorithm is tested and compared under complex environments such as the coal mine. The test results demonstrate that the proposed algorithm has a better comprehensive performance in comparison with existing algorithms.

Key words: unmanned aerial vehicle (UAV), trajectory planning, safe flight corridor, convex optimization

中图分类号:

TP242.6

黄宇昊, 韩超, 赵明辉, 杜乾坤, 王石刚. 考虑安全飞行通道约束的无人机飞行轨迹多目标优化策略[J]. 上海交通大学学报, 2022, 56(8): 1024-1033.

HUANG Yuhao, HAN Chao, ZHAO Minghui, DU Qiankun, WANG Shigang. Multi-Objective Optimization Strategy of Trajectory Planning for Unmanned Aerial Vehicles Considering Constraints of Safe Flight Corridors[J]. Journal of Shanghai Jiao Tong University, 2022, 56(8): 1024-1033.

图/表 10

图1

图2

图3

图4

图5

图6

表1

不同障碍物密度下SFC与所提算法的性能对比

ρ	本文算法				SFC算法
ρ	$o -$ /m	o_min/m	$o m i n o -$	t_u/s	$o -$ /m	o_min/m	$o m i n o -$	t_u/s
0.02	1.286	0.510	0.397	1.137	1.302	0.473	0.363	1.190
0.04	1.048	0.555	0.530	1.214	1.069	0.454	0.425	1.320
0.06	1.059	0.528	0.499	1.540	1.085	0.446	0.411	1.756
0.08	0.837	0.461	0.551	1.912	0.867	0.342	0.394	2.665
0.10	0.781	0.462	0.592	1.994	0.802	0.359	0.448	2.355
平均值	1.002	0.503	0.513	1.559	1.025	0.415	0.408	1.857

表1

表2

图7

表3

轨迹规划算法性能对比

地图类型	本文算法				GTOP算法
地图类型	J_s/(m²·s^-5)	$o -$ /m	t_w/s	l_w/m	J_s/(m²·s^-5)	$o -$ /m	t_w/s	l_w/m
随机地图	69.106	1.063	21.514	23.690	108.871	1.011	29.714	23.714
走廊地图	368.889	0.775	76.659	69.231	624.186	0.738	86.244	69.432
矿井地图	309.090	2.157	59.893	56.435	337.129	2.139	70.404	56.948

表3

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p^T/m	安全飞行通道计算结果		栅格地图计算结果
p^T/m	o_min/m	g	o_min/m	g
(-7.34,-6.02, 2.00)	1.15	(-0.22,-0.98, 0.00)	1.16	(-0.18,-0.98, 0.00)
(-3.20,-1.51, 1.65)	0.54	(0.08, 0.99,-0.04)	0.63	(-0.31, 0.95, 0.00)
(1.02,-1.29, 1.18)	0.93	(0.98,-0.22, 0.05)	0.94	(0.97,-0.21, 0.01)
(4.28, 1.19, 1.09)	0.90	(-0.51, 0.86, 0.03)	0.92	(-0.49, 0.87, 0.01)