Planning a safe and dynamic flyable trajectory for unmanned aerial vehicle (UAV) is the precondition for autonomous flight. Considering environmental complexity and flight safety under limited motor speed, we proposed a new time-segmented trajectory planning method, which can ensure motor speed within the allowable range and achieve safe flight. Firstly, the candidate trajectory for quadrotor UAV is expressed by high-order polynomials. Herein, polynomial is used as the expression of trajectory, and nonlinear programming (NLP) is used to calculate polynomial coefficient. Then, two strategies and an initial value calculation method are proposed to improve the efficiency of NLP. Furthermore, aiming at the problem that the initial trajectory of UAV motor cannot meet the current speed limit constraint after the motor speed drops, an adjustment strategy based on feasible trajectory is designed. The method presented in this paper not only enhances flexibility of the flight process, improves solving efficiency of the algorithm but also enables UAV to continue flying when the motor speed is limited. Finally, simulation analysis and comparison illustrate the effectiveness and superiority of the proposed method.
AlcántaraACapitánJCunhaR, et al.Optimal trajectory planning for cinematography with multiple unmanned aerial vehicles. Robotics and Autonomous Systems2021; 140: 103778.
2.
ZhengYLuoJQiaoY, et al.UAV-assisted traffic speed prediction via gray relational analysis and deep learning. Drones2023; 7(6): 372.
3.
ZhangZWuJDaiJ, et al.Optimal path planning with modified A-Star algorithm for stealth unmanned aerial vehicles in 3D network radar environment. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering2022; 236(1): 72–81.
4.
YanFLiuYSXiaoJZ. Path planning in complex 3D environments using a probabilistic roadmap method. International Journal of Automation and Computing2013; 10(6): 525–533.
5.
ShaoSPengYHeC, et al.Efficient path planning for UAV formation via comprehensively improved particle swarm optimization. ISA Transactions2020; 97: 415–430.
ChenYLiWQiR. Research and smulation of UAV three-dimensional path replanning in complex environment. In: IEEE asia-pacific conference on image processing. Ghaziabad Delhi: Electronics and Computers (IPEC), 2021, pp. 746–751.
8.
LinYSaripalliS. Sampling-based path planning for UAV collision avoidance. IEEE Transactions on Intelligent Transportation Systems2017; 18(11): 3179–3192.
9.
XiaQLiuSGuoM, et al.Multi-UAV trajectory planning using gradient-based sequence minimal optimization. Robotics and Autonomous Systems2021; 137: 103728.
10.
LinSLiFLiX, et al.Improved artificial bee colony algorithm based on multi-strategy synthesis for UAV path planning. IEEE Access2022; 10: 119269–119282.
11.
RousseauGManiuCSTebbaniS, et al.Minimum-time B-spline trajectories with corridor constraints. Application to cinematographic quadrotor flight plans. Control Engineering Practice2019; 89: 190–203.
12.
LiuSWattersonMMohtaK, et al.Planning dynamically feasible trajectories for quadrotors using safe flight corridors in 3-D complex environments. IEEE Robotics and Automation Letters2017; 2(3): 1688–1695.
13.
ElhosenyMTharwatAHassanienAE. Bezier curve based path planning in a dynamic field using modified genetic algorithm. Journal of Computational Science2018; 25: 339–350.
14.
ChamseddineAZhangYRabbathCA, et al.Flatness-based trajectory planning/replanning for a quadrotor unmanned aerial vehicle. IEEE Transactions on Aerospace and Electronic Systems2012; 48(4): 2832–2848.
15.
ElikerKZhangGGrouniS, et al.An optimization problem for quadcopter reference flight trajectory generation. Journal of Advanced Transportation2018; 2018(PT.4): 1–15.
16.
ChamseddineAZhangYRabbathCA. Trajectory planning and re-planning for fault tolerant formation flight control of quadrotor unmanned aerial vehicles. In: American Control Conference 2012, Montreal, QC, 27–29 June 2012.
17.
CichellaVKaminerIWaltonC, et al.Optimal motion planning for differentially flat systems using Bernstein approximation. IEEE Control Systems Letters2017; 2(1): 181–186.
18.
MellingerDKumarV. Minimum snap trajectory generation and control for quadrotors. In: IEEE International Conference on Robotics and Automation 2011, Shanghai, China, 9–13 May 2011.
19.
BryARichterCBachrachA, et al.Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments. The International Journal of Robotics Research2015; 34(7): 969–1002.
20.
LiBAcarmanTZhangY, et al.Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach. IEEE Transactions on Intelligent Vehicles2019; 5(3): 414–425.
21.
DhadekarDDTaloleSE. Robust fault-tolerant flight control of quadrotor against faults in multiple motors. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering2021; 235(16): 2504–2516.
22.
KhaliliMZhangXCaoY, et al.Distributed adaptive fault‐tolerant leader‐following formation control of nonlinear uncertain second‐order multi‐agent systems. International Journal of Robust and Nonlinear Control2018; 28(15): 4287–4308.
23.
YuZQuYZhangY. Distributed fault-tolerant cooperative control for multi-UAVs under actuator fault and input saturation. IEEE Transactions on Control Systems Technology2018; 27(6): 2417–2429.
24.
AbdelghanyMBMoustafaAMMonessM. Benchmarking tracking autopilots for quadrotor aerial robotic system using heuristic nonlinear controllers. Drones2022; 6(12): 379.
25.
KamelMAYuXZhangY. Fault-tolerant cooperative control design of multiple wheeled mobile robots. IEEE Transactions on Control Systems Technology2017; 26(2): 756–764.
26.
NguyenNTProdanIStoicanF, et al.Reliable nonlinear control for quadcopter trajectory tracking through differential flatness. IFAC-PapersOnLine2017; 50(1): 6971–6976.
27.
ShifatTAJang-WookH. Remaining useful life estimation of BLDC motor considering voltage degradation and attention-based neural network. IEEE Access2020; 8: 168414–168428.
