Cuckoo search-based method for trajectory planning of quadrotor in an urban environment

Abstract

Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.

Keywords

Urban environment quadrotor delivery task trajectory planning Cuckoo search

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References

Yang N, San K and Chang Y. A novel approach for real time monitoring system to manage UAV delivery. In: IEEE 5th IIAI international congress on advanced applied informatics (IIAI-AAI), 2016, pp.1054–1057.

Zanenga

Leonello

Bottasso

. Feasibility study of rotorcraft fire fighting for high-rise buildings. J Aerosp Eng 2009; 23: 166–175.

D’Andrea

. Guest editorial can drones deliver? IEEE Transact Automat Sci Eng 2014; 11: 647–648.

Wang

, et al. Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm. Aerosp Sci Technol 2017; 70: 497–510.

Murray

Chu

. The flying sidekick traveling salesman problem: optimization of drone-assisted parcel delivery. Transport Res Part C: Emerg Technol 2015; 54: 86–109.

Paduano J, Wissler J, Drozeski G, et al. TALOS: an unmanned cargo delivery system for rotorcraft landing to unprepared sites. In: 71st American Helicopter Society International Annual Forum 2015. Virginia Beach, Virginia, USA, 5–7 May 2015; 71: 1–17.

Ramana

Varma

Kothari

. Motion planning for a fixed-wing UAV in urban environments. IFAC-PapersOnLine 2016; 49: 419–424.

Zarovy

Costello

Mehta

. Experimental method for studying gust effects on micro rotorcraft. Proc IMechE, Part G: J Aerospace Engineering 2013; 227: 703–713.

Miao H and Wang Y. Optical flow based obstacle avoidance and path planning for quadrotor flight. In: Chinese intelligent automation conference, Singapore, 2–4 June 2017, pp.631–638.

10.

Liu

Wang

, et al. A novel real-time moving target tracking and path planning system for a quadrotor UAV in unknown unstructured outdoor scenes. IEEE Transact Syst Man Cybernetics Systems. Epub ahead of print 07 March 2018, pp. 1–11. DOI: 10.1109/TSMC.2018.2808471.

11.

Chamseddine

Zhang

Rabbath

, et al. Flatness-based trajectory planning/replanning for a quadrotor unmanned aerial vehicle. IEEE Transact Aerosp Electronic Syst 2012; 48: 2832–2848.

12.

Chamseddine

Zhang

Rabbath

, et al. Trajectory planning and replanning strategies applied to a quadrotor unmanned aerial vehicle. J Guidance Control Dynam 2012; 35: 1667–1671.

13.

Bouktir Y, Haddad M and Chettibi T. Trajectory planning for a quadrotor helicopter. In: IEEE 16th Mediterranean conference on control and automation, Ajaccio, France, 25–27 June 2008: 1258–1263.

14.

Iswanto

Wahyunggoro

Cahyadi

. Quadrotor path planning based on modified fuzzy cell decomposition algorithm. Telecommun Comput Electron Control 2016; 14: 655–664.

15.

Murray

Anderson

. A CFD-based procedure for airspace integration of small unmanned aircraft within congested areas. Int J Micro Air Vehicle 2017; 9: 235–252.

16.

Wei B, Zhang Q, Xu Z, et al. Study on real-time safe route planning method for quad-rotor UAV among buildings. In: IEEE guidance, navigation and control conference, Nanjing, China, 12–14 August 2016: 1067–1071.

17.

Tong

. Flight control of a quadrotor under model uncertainties. Int J Micro Air Vehicle 2015; 7: 1–19.

18.

Guerrero JA, Escareño JA and Bestaoui Y. Quad-rotor MAV trajectory planning in wind fields. In: IEEE international conference on robotics and automation Karlsruhe, Germany, 6–10 May 2013, pp.778–783.

19.

Yang X and Deb S. Cuckoo search via Lévy flights. In: IEEE World congress on nature & biologically inspired computing, Coimbatore, India, 9–11 December, 2009, pp.210–214.

20.

Wang

Sun

, et al. A new cuckoo search algorithm with hybrid strategies for flow shop scheduling problems. Soft Comput 2017; 21: 1–11.

21.

Jiang

Luo

Jiang

, et al. A cuckoo search-support vector machine model for predicting dynamic measurement errors of sensors. IEEE Access 2017; 4: 5030–5037.

22.

Xiao

Shao

, et al. Research and application of a hybrid wavelet neural network model with the improved cuckoo search algorithm for electrical power system forecasting. Appl Energy 2017; 198: 203–222.

23.

Lyu

Bao

Lai

, et al. A dynamic model parameter identification method for quadrotors using flight data. Proc IMechE, Part G: J Aerospace Engineering 2018, pp. 0954410018767754–0954410018767754.

24.

Yuan

. Numerical simulation of wind field characteristics around two adjacent high-rise buildings. J Appl Math Phys 2014; 2: 264–264.

25.

Golubev

Visbal

. Modeling MAV response in gusty urban environment. Int J Micro Air Vehicle 2012; 4: 79–92.

26.

Mehta SS, Ton C, McCourt MJ, et al. Human-assisted RRT for path planning in urban environments. In: IEEE international conference on systems, man, and cybernetics, Kowloon, China, 9–12 October 2015, pp.941–946.

27.

Shastry AK, Kothari M and Abhishek A. Development of flight dynamics model of quadrotor. In: AIAA guidance, navigation, and control conference, Kissimmee, Florida, USA, 8–12 January 2018, p.1850.

28.

Payne

Sorensen

. The cuckoos, Oxford: Oxford University Press, 2005.

29.

. A general trajectory optimization method for aircraft taxiing on flight deck of carrier. Proc IMechE, Part G: J Aerospace Engineering 2018, pp. 0954410017752224–0954410017752224.

30.

Shi Y and Eberhart RC. Parameter selection in particle swarm optimization. In: International conference on evolutionary programming. Berlin, Heidelberg: Springer, San Diego, California, USA, 25–27 March 1998, pp.591–600.

31.

Eberhart, Russell C and Shi Y. Comparison between genetic algorithms and particle swarm optimization. In: International conference on evolutionary programming. Berlin, Heidelberg: Springer, San Diego, California, USA, 25–27 March 1998, pp.611–616.

32.

Socha

Blum

. An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training. Neural Comput Appl 2007; 16: 235–247.