As science and technology advance, the need for autonomous navigation mobile robots and work vehicles rises. Traditional path planning has shortcomings such as excessive reliance on prior environmental information, weak real-time performance of algorithm-based path planning, difficulties in adapting to complex environments, and a lack of consideration for the compatibility with the robot’s own underlying motion information. In this paper, the Dynamic Window Approach (DWA) is used to address these issues by combining it with the work vehicles’ own kinematic model. Firstly, constraints are applied to the speed space of the DWA algorithm to ensure the requirements of dynamic models and obstacle avoidance. At the same time, a function for trajectory prediction selection, weight evaluation of velocity functions, and an added target navigation function are designed to improve the algorithm’s guiding capability toward the target point. Finally, MATLAB is used for simulation and field experiments to verify that the work vehicle can quickly and safely reach the destination. The simulation experiment highlights that the DWA algorithm, fused with the vehicle’s motion data, excels in local path planning in complex environments, enabling efficient obstacle avoidance. The field test further validates these findings, confirming the algorithm’s capabilities for path planning and obstacle avoidance. Therefore, the algorithm proposed in this paper can better address the problems of the traditional DWA algorithm, such as getting stuck in local optima and its inability to effectively handle complex obstacle environments, thereby significantly improving the path planning capability and real-time performance of the algorithm.
NahavandiSAlizadehsaniRNahavandiD, et al. A comprehensive review on autonomous navigation. ACM Comput Surv2025; 57(9): 1–67.
2.
WangJYuZZhouD, et al. Vision-based deep reinforcement learning of unmanned aerial vehicle (UAV) autonomous navigation using privileged information. Drones2024; 8(12): 782.
3.
SariffNBuniyaminN. An overview of autonomous mobile robot path planning algorithms. In: 2006 4th student conference on research and development, Shah Alam, Malaysia, 27–28 June 2006, pp. 183–188. New York: IEEE.
4.
O’ReillyJJRyanNK.Introduction to slam II and slamsystem. In: SwainJJGoldsmanDCrainRC, et al. (eds) Proceedings of the 24th conference on winter simulation. IEEE Computer Society, 1992, pp. 352–356.
5.
MaZYuanZ.Real-time navigation and obstacle avoidance of mobile robot based on raster method. Robot1996; 18(6): 25–29.
6.
MaRGuanZ.A review of the current situation and development of path planning technology. Mod Machinery2008; 45(3): 22–24+27.
7.
SumikuraSShibuyaMSakuradaK.OpenVSLAM: a versatile visual SLAM framework. In: Proceedings of the 27th ACM international conference on multimedia, Nice, France, 21–25 October 2019, pp. 2292–2295. New York: ACM.
8.
TeedZDengJ.DROID-SLAM: deep visual SLAM for monocular, stereo, and RGB-D cameras. Adv Neural Inf Process Syst2021; 34: 16558–16569.
9.
SarlinPEDeToneDMalisiewiczT, et al. SuperGlue: learning feature matching with graph neural networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, Seattle, WA, USA, 13–19 June 2020, pp. 4938–4947. New York: IEEE.
10.
LiJLiaoCZhangW, et al. UAV path planning model based on R5DOS model improved A-star algorithm. Appl Sci2022; 12(22): 11338.
11.
GuoYLiuXLiuX, et al. FC-RRT*: an improved path planning algorithm for UAV in 3D complex environment. ISPRS Int J Geoinf2022; 11(2): 112.
12.
ZhaoYLiuKLuG, et al. Path planning of UAV delivery based on improved APF-RRT algorithm. J Phys Conf Ser2020; 1624(4): 042004.
13.
Ait-SaadiAMeraihiYSoukaneA, et al. A novel hybrid chaotic aquila optimization algorithm with simulated annealing for unmanned aerial vehicles path planning. Comput Electr Eng2022; 104: 108461.
14.
HuangQShengZFangY, et al. A simulated annealing-particle swarm optimization algorithm for UAV multi-target path planning. In: 2022 2nd international conference on consumer electronics and computer engineering (ICCECE), Guangzhou, China, 14–16 January 2022, pp. 906–910. New York: IEEE.
15.
WangQLiuMRenW, et al. Review of common algorithms for UAV track planning. J Jilin Univ (Sci Ed)2019; 37(1): 58–67.
16.
ZhuS.Cooperative task planning method of multi-UAV in dynamic environment. Master’s Thesis, Nanjing University of Posts and Telecommunications, China, 2019.
17.
MaoCWuP.UAV path planning obstacle avoidance algorithm based on artificial potential field method. Electron Sci Technol2019; 32(7): 65–70.
18.
WuQZengQLuoJ, et al. Application research on improved artificial potential field method in UAV path planning. J Chongqing Univ Technol (Nat Sci)2022; 36(3): 144–151.
19.
KebriaPMKhosraviASalakenSM, et al. Deep imitation learning for autonomous vehicles based on convolutional neural networks. IEEE CAA J Autom Sin2019; 7(1): 82–95.
20.
YuZSuXSunG.Fractional order meta-reinforcement learning for space non-cooperative object active visual tracking. IEEE Trans Aerosp Electron Syst2025; 61(2): 4284–4295.
21.
ZhangZZhouDSunG, et al. DFTI: dual-branch fusion network based on transformer and inception for space noncooperative objects. IEEE Trans Instrum Meas2024; 73: 1–11.
22.
ZhouDSunGZhangZ, et al. On deep recurrent reinforcement learning for active visual tracking of space noncooperative objects. IEEE Robot Autom Lett2023; 8(8): 4418–4425.
23.
YuZ.Meta-reinforcement learning for active visual tracking about space non-cooperative object. Multimed Tools Appl2025; 84(22): 25365–25384.
24.
ChiHCaiPFuD, et al. Spatiotemporal-restricted A* algorithm as a support for lane-free traffic at intersections with mixed flows. Green Energy Intell Transp2024; 3(2): 100159.
25.
LiTRuanJZhangK.The investigation of reinforcement learning-based end-to-end decision-making algorithms for autonomous driving on the road with consecutive sharp turns. Green Energy Intell Transp2025; 5(2): 100288.
26.
ChenCChenXHangP.Personalized longitudinal motion planning based on a combination of reinforcement learning and imitation learning. Green Energy Intell Transp2025; 5(1): 100321.
27.
AgrawalMDoermannD. Voronoi++: a dynamic page segmentation approach based on Voronoi and Docstrum features. In: 2009 10th international conference on document analysis and recognition, Barcelona, Spain, 26–29 July 2009, pp. 1011–1015. New York: IEEE.
28.
TangYSuY.New generalized contractions in complete cone metric spaces. In: 2011 international symposium on computer science and society, Kota Kinabalu, Malaysia, 16–17 July 2011, pp. 284–286. New York: IEEE.
29.
ZhangLSunKCsorbaG, et al. Complete mitochondrial genomes reveal robust phylogenetic signals and evidence of positive selection in horseshoe bats. BMC Ecol Evol2021; 21(1): 199.
30.
KhatibO.Real-time obstacle avoidance for manipulators and mobile robots. Int J Robot Res1986; 5(1): 90–98.
31.
LiGYamashitaAAsamaH, et al. An efficient improved artificial potential field based regression search method for robot path planning. In: 2012 IEEE international conference on mechatronics and automation, Chengdu, China, 5–8 August 2012, pp. 1227–1232. New York: IEEE.
32.
LaValleSMKuffnerJJJr.Randomized kinodynamic planning. Int J Robot Res2001; 20(5): 378–400.
33.
KuffnerJLaValleSL.An efficient approach to single-query path planning. In: IEEE international conference on robotics and automation, San Francisco, CA, USA, 24–28 April 2000, pp. 473–479. New York: IEEE.
34.
KaramanSWalterMRPerezA, et al. Anytime motion planning using the RRT. In: 2011 IEEE international conference on robotics and automation, Shanghai, China, 9–13 May 2011, pp. 1478–1483. New York: IEEE.
35.
KuwataYTeoJFioreG, et al. Real-time motion planning with applications to autonomous urban driving. IEEE Trans Control Syst Technol2009; 17(5): 1105–1118.
36.
YershovaAJailletLSiméonT, et al. Dynamic-domain RRTs: efficient exploration by controlling the sampling domain. In: Proceedings of the 2005 IEEE international conference on robotics and automation, Barcelona, Spain, 18–22 April 2005, pp. 3856–3861. New York: IEEE.
37.
ChangXHuWJiS, et al. Obstacle avoidance of mobile robot based on improved dynamic window method. Modular Mach Tool Autom Manuf Tech2021; 37(7): 33–36+39.
38.
LiuJLiuWZhangY, et al. Automatic collision avoidance algorithm of unmanned surface vessels based on improved dynamic window approach. J Shanghai Marit Univ2021; 42(2): 1–7.
SimmonsR. The curvature-velocity method for local obstacle avoidance. In: Proceedings of IEEE international conference on robotics and automation, Minneapolis, MN, USA, 22–28 April 1996, vol. 4, pp.3375–3382. New York: IEEE.
41.
SaranrittichaiPNiparnanNSudsangA. Robust local obstacle avoidance for mobile robot based on dynamic window approach. In: 2013 10th international conference on electrical engineering/electronics, computer, telecommunications and information technology, Krabi, Thailand, 15–17 May 2013, pp. 1–4. New York: IEEE.
42.
SederMPetrovicI. Dynamic window based approach to mobile robot motion control in the presence of moving obstacles. In: Proceedings 2007 IEEE international conference on robotics and automation, Rome, Italy, 10–14 April 2007, pp. 1986–1991. New York: IEEE.
