Sage Journals: Discover world-class research

Abstract

Tractor-trailers are commonly used to load large cargoes onto roll-on and roll-off (Ro-Ro) ships. To safely load cargoes to the target position on the deck, the path of tractor-trailer needs to be designed for avoiding collision with obstacles. During the loading process, tractor-trailer has to through the inclined ramp which connects the horizontal dock with deck, and the path must be designed in three-dimensional (3D) space. Based on the dynamic relationship between tractor and trailer, the collision free path generation for tractor-trailer on two-dimensional (2D) plane could be realized. However, the proposed methods cannot appropriate for analyzing the motion of tractor-trailer in 3D space. This research investigated a method to generate the safest loading path of tractor-trailer in 3D space. First, the loading scenario was divided into seven sections and all the possible tractor paths were generated based on the Bezier curves. Next, the conversion of tractor-trailer dynamic relationship between 2D plane and 3D space was realized by using geodesic theory, and the trailer paths were calculated according to tractor paths. Then, all paths are sorted based on transportation efficiency and safety during the transportation process. Finally, collision detection is carried out on the generated paths in sequence to determine the safest loading path for the tractor-trailer. Simulation and experimental results verified that the proposed method could be used to design the loading path of tractor-trailer in 3D space under multiple scenarios.

Keywords

Tractor-trailer path generation 3D space Bezier curve geodesic theory

Get full access to this article

View all access options for this article.

References

Chandra

Christiansen

Fagerholt

. Combined fleet deployment and inventory management in roll-on/roll-off shipping. Transp Res E Logist Transp Rev 2016; 92: 43–45.

Fischer

Nokhart

Olsen

, et al. Robust planning and disruption management in roll-on roll-off liner shipping. Transp Res E Logist Transp Rev 2016; 91: 51–67.

Hemmati

Stålhane

Hvattum

, et al. An effective heuristic for solving a combined cargo and inventory routing problem in tramp shipping. Comput Oper Res 2015; 64: 274–282.

Tomas

Wesley

. An algorithm for planning collision-free paths among polyhedral obstacles. Commun ACM 1979; 22: 560–570.

Lamini

Benhlima

Elbekri

. Genetic algorithm based approach for autonomous mobile robot path planning. Procedia Comput Sci 2018; 127: 180–189.

Mac

Copot

Tran

, et al. A hierarchical global path planning approach for mobile robots based on multi-objective particle swarm optimization. Appl Soft Comput 2017; 59: 68–76.

Xiang

Lin

Ouyan

, et al. Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot. Sci Rep 2022; 12: 13273.

Zhang

Lin

Wang

, et al. Rapidly-exploring random trees multi-robot map exploration under optimization framework. Rob Auton Syst 2020; 131: 103565.

Nasir

Islam

Malik , et al. RRT*-SMART: a rapid convergence implementation of RRT*. Int J Adv Robot Syst 2013; 10: 1–12.

10.

Jia

. Path planning and smoothing of mobile robot based on improved artificial fish swarm algorithm. Sci Rep 2022; 12: 659.

11.

Martinez-Melchor

Jimenez-Fernandez

Vazquez-Leal

, et al. Optimization of collision-free paths in a differential-drive robot by a smoothing piecewise-linear approach. Comput Appl Math 2018; 37: 4944–4965.

12.

Chen

Yan

Wang

, et al. Dynamic path planning and path following control for autonomous vehicle based on the piecewise affine tire model. Proc IMechE, Part D: J Automobile Engineering 2021; 235: 881–893.

13.

Wang

, et al. Velocity obstacle-based collision avoidance and motion planning framework for connected and automated vehicles. Transport Res Rec 2022; 2676(5): 748–766.

14.

Berglund

Brodnik

Jonsson

, et al. Planning smooth and obstacle-avoiding B-spline paths for autonomous mining vehicles. IEEE Trans Autom Sci Eng 2010; 7: 167–172.

15.

Zhou

Song

Tian

. Bezier curve based smooth path planning for mobile robot. J Inf Comput Sci 2011; 8: 2441–2450.

16.

Zheng

Zeng

Yang

, et al. Bézier curve-based trajectory planning for autonomous vehicles with collision avoidance. IET Intell Transp Syst 2021; 14(13): 1882–1891.

17.

Chen

Cai

Chen

, et al. Trajectory and velocity planning method of emergency rescue vehicle based on segmented three-dimensional quartic Bezier curve. IEEE Trans Intell Transp Syst 2023: 24(3): 3461–3475.

18.

Elhoseny

Tharwat

Hassanien

, et al. Bezier curve based path planning in a dynamic field using modified genetic algorithm. J Comput Sci 2018; 25: 339–350.

19.

Liu

, et al. Decision-making models on perceptual uncertainty with distributional reinforcement learning. Green Energy Intell Transp 2023; 2(2): 100062.

20.

Zhang

Luan

, et al. Collision avoidance of autonomous vehicles with E-bike at un-signalized occluded intersections based on reinforcement learning. In: 2023 IEEE international conference on systems, man, and cybernetics, Honolulu, HI, USA, 1–4 October 2023.

21.

Zhang

, et al. An integrated longitudinal and lateral control of vehicle platoons. Proc IMechE, Part D: J Automobile Engineering. Epub ahead of print 25 November 2024. DOI: 10.1177/09544070241287245.

22.

Song

Wei

, et al. Longitudinal and lateral control methods from single vehicle to autonomous platoon. Green Energy Intell Transp 2023; 2(2): 100066.

23.

Liu

. Path planning for mobile articulated robots based on the improved A* algorithm. Int J Adv Robot Syst 2017; 14: 1–10.

24.

Jin

Liang

Wang

, et al. Adaptive backstepping sliding mode control of tractor-trailer system with input delay based on RBF neural network. Int J Control Autom Syst 2021; 19: 76–87.

25.

Acarman

Zhang

, et al. Tractor-trailer vehicle trajectory planning in narrow environments with a progressively constrained optimal control approach. IEEE Trans Intell Veh 2020; 5(3): 414–425.

26.

Cariou

Lenain

Thuilot

, et al. Path following of a vehicle-trailer system in presence of sliding: application to automatic guidance of a towed agricultural implement. In: The 2010 IEEE/RSJ international conference on intelligent robots and systems, Taipei, Taiwan, 18–22 October 2010, pp.4976–4981. New York: IEEE.

27.

Rimmer

Cebon

. Theory and practice of reversing control on multiply-articulated vehicles. Proc IMechE, Part D: J Automobile Engineering 2016; 230: 899–913.

28.

Khalaji

. PID-based target tracking control of a tractor-trailer mobile robot. Proc IMechE, Part C: J Mechanical Engineering Science 2019; 233(13): 4776–4787.

29.

Shojaei

Taghavifar

. Input-output feedback linearization control of a tractor with n-trailers mechanism considering the path curvature. Proc IMechE, Part C: J Mechanical Engineering Science 2022; 236(17): 9700–9715.

30.

Yue

Shangguan

, et al. Multi-source motion constrained model predictive control for tractor-trailer trucks with coupled dynamics. Proc IMechE, Part D: J Automobile Engineering 2024; 238(12): 3760–3778.

31.

Lee

. Principles of CAD/CAM/CAE systems. East Rutherford: Prentice Hall PTR, 1999.

32.

Polthier

Schmies

. Straightest geodesics on polyhedral surfaces. In: Hege

Polthier

(eds) Mathematical visualization: algorithms, applications and numerics. Berlin, Heidelberg: Springer, 1998, pp.135–150.

33.

Sun

. VC++ in-depth explanation. Beijing: Electronic Industry Press, 2012.

34.

Shreiner

. OpenGL programming guide. 7th ed. Beijing: China Machine Press, 2010.

Tractor-trailer path generation in three-dimensional space based on Bezier curve and geodesic theory

Abstract

Keywords

Get full access to this article

References