Multi-Objective Optimization Strategy of Trajectory Planning for Unmanned Aerial Vehicles Considering Constraints of Safe Flight Corridors

doi:10.16183/j.cnki.jsjtu.2021.154

Abstract

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

CLC Number:

TP242.6

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.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Tab.1

Comparison of SFC and proposed algorithms at different obstacle densities

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

Tab.1

Tab.2

Fig.7

Tab.3

Comparison of trajectory planning algorithms

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

Tab.3

References 18

[1]	ZHOU Y, RUI T, LI Y R, et al. A UAV patrol system using panoramic stitching and object detection[J]. Computers & Electrical Engineering, 2019, 80: 106473.
[2]	MANSOURI S S, KANELLAKIS C, KOMINIAK D, et al. Deploying MAVs for autonomous navigation in dark underground mine environments[J]. Robotics and Autonomous Systems, 2020, 126: 103472. doi: 10.1016/j.robot.2020.103472 URL
[3]	赵建霞, 段海滨, 赵彦杰, 等. 基于鸽群层级交互的有人/无人机集群一致性控制[J]. 上海交通大学学报, 2020, 54(9): 973-980.
	ZHAO Jianxia, DUAN Haibin, ZHAO Yanjie, et al. Consensus control of manned-unmanned aerial vehicle swarm based on hierarchy interaction of pigeons[J]. Journal of Shanghai Jiao Tong University, 2020, 54(9): 973-980.
[4]	CHANDLER B, GOODRICH M A. Online RRT and online FMT: Rapid replanning with dynamic cost[C]// 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, Canada: IEEE, 2017: 6313-6318.
[5]	CAI Y Z, XI Q B, XING X J, et al. Path planning for UAV tracking target based on improved A-star algorithm[C]// 2019 1st International Conference on Industrial Artificial Intelligence. Shenyang, China: IEEE, 2019: 1-6.
[6]	郝钏钏, 方舟, 李平. 基于Q学习的无人机三维航迹规划算法[J]. 上海交通大学学报, 2012, 46(12): 1931-1935.
	HAO Chuanchuan, FANG Zhou, LI Ping. A 3-D route planning algorithm for unmanned aerial vehicle based on Q-learning[J]. Journal of Shanghai Jiao Tong University, 2012, 46(12): 1931-1935.
[7]	CHEN J, LIU T B, SHEN S J. Online generation of collision-free trajectories for quadrotor flight in unknown cluttered environments[C]// 2016 IEEE International Conference on Robotics and Automation. Stockholm, Sweden: IEEE, 2016: 1476-1483.
[8]	LIU S K, WATTERSON M, MOHTA K, et al. Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments[J]. IEEE Robotics and Automation Letters, 2017, 2(3): 1688-1695. doi: 10.1109/LRA.2017.2663526 URL
[9]	STOICAN F, PRODAN I, POPESCU D, et al. Constrained trajectory generation for UAV systems using a B-spline parametrization[C]// 2017 25th Mediterranean Conference on Control and Automation. Valletta, Malta: IEEE, 2017: 613-618.
[10]	SATAI H A, ZAHRA M M A, RASOOL Z I, et al. Bézier curves-based optimal trajectory design for multirotor UAVs with any-angle pathfinding algorithms[J]. Sensors, 2021, 21(7): 2460. doi: 10.3390/s21072460 URL
[11]	RICHTER C, BRY A, ROY N. Polynomial trajectory planning for aggressive quadrotor flight in dense indoor environments[M]. New York, NY, USA: Springer, 2016: 649-666.
[12]	INGERSOLL B T, INGERSOLL J K, DEFRANCO P, et al. UAV path-planning using bezier curves and a receding horizon approach[C]// AIAA Modeling and Simulation Technologies Conference. Reston, Virginia: AIAA, 2016: 3675.
[13]	LOPEZ B T, HOW J P. Aggressive 3-D collision avoidance for high-speed navigation[C]// 2017 IEEE International Conference on Robotics and Automation. Singapore: IEEE, 2017: 5759-5765.
[14]	MELLINGER D, KUMAR V. Minimum snap trajectory generation and control for quadrotors[C]// 2011 IEEE International Conference on Robotics and Automation. Shanghai, China: IEEE, 2011: 2520-2525.
[15]	OLEYNIKOVA H, BURRI M, TAYLOR Z, et al. Continuous-time trajectory optimization for online UAV replanning[C]// 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems. Daejeon, South Korea: IEEE, 2016: 5332-5339.
[16]	GAO F, LIN Y, SHEN S J. Gradient-based online safe trajectory generation for quadrotor flight in complex environments[C]// 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems. Vancouver, Canda: IEEE, 2017: 3681-3688.
[17]	HORNUNG A, WURM K M, BENNEWITZ M, et al. OctoMap: An efficient probabilistic 3D mapping framework based on octrees[J]. Autonomous Robots, 2013, 34(3): 189-206. doi: 10.1007/s10514-012-9321-0 URL
[18]	SVANBERG K. A class of globally convergent optimization methods based on conservative convex separable approximations[J]. SIAM Journal on Optimization, 2002, 12(2): 555-573. doi: 10.1137/S1052623499362822 URL

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)