In order to solve the problem that the traditional DWA algorithm cannot have both safety and speed because of the fixed parameters in the complex environment with many obstacles, a parameter adaptive DWA algorithm (PA-DWA) is proposed to improve the robot running speed on the premise of ensuring safety. Firstly, the velocity sampling space is optimized by the current pose of the mobile robot, and a criterion of environment complexity is proposed. Secondly, a parameter-adaptive method is presented to optimize the trajectory evaluation function. When the environment complexity is greater than a certain threshold, the minimum distance between the mobile robot and the obstacle is taken as the input, and the weight of the velocity parameter is adjusted according to the real-time obstacle information dynamically. The current velocity of the mobile robot is used as input to dynamically adjust the weight of the direction angle parameter. In the Matlab simulation, the total time consumption of PA-DWA is reduced by 47.08% in the static obstacle environment and 39.09% in the dynamic obstacle environment. In Gazebo physical simulation experiment, the total time of PA-DWA was reduced by 26.63% in the case of dynamic obstacles. The experimental results show that PA-DWA can significantly reduce the total time of the robot under the premise of ensuring safety.
AttoyibiM.M., FikrisaF.E. and HandayaniA.N., The implementation of a star algorithm (A*) in the game education about numbers introduction. Proceedings of the 2nd International Conference on Vocational Education and Training (ICOVET 2018) (2019), 234–238.
2.
KalaR., ShuklaA. and TiwariR., Fusion of probabilistic A algorithm and fuzzy inference system for robotic path planning, Artificial Intelligence Review33 (2010), 307–327. https://doi.org/10.1007/s10462-010-9157-y
3.
SantosoL.W., SetiawanA. and PrajogoA.K., Performance Analysis of Dijkstra, A* and Ant Algorithm for Finding Optimal Path Case Study: Surabaya City Map. Doctoral dissertation. Petra Christian University, (2010).
4.
KothariM., PostlethwaiteI. and GuD.W., A suboptimal path planning algorithm using rapidly-exploring random trees, International Journal of Aerospace Innovations2(1–2) (2010), 93–104.
5.
ÖzdemirA. and SezerV., Follow the gap with dynamic windowapproach, International Journal of Semantic Computing12(1) (2018), 43–57. https://doi.org/10.1142/S1793351X18400032
6.
MoonJ., LeeB. and TahkM., A Hybrid Dynamic Window Approach for Collision Avoidance of VTOL UAVs, International Journal of Aeronautical and Space Sciences19 (2018), 889–903. https://doi.org/10.1007/s42405-018-0061-z
7.
TangL., DianS., GuG., ZhouK., WangS. and FengX., A novel potential field method for obstacle avoidance and path planning of mobile robot. 2010 3rd International Conference on Computer Science and Information Technology9 (2010), 633–637. DOI: 10.1109/ICCSIT.2010.5565069
RaoT.S., An Ant Colony TSP to Evaluate the Performance of Supply Chain Network, Materials Today: Proceedings5(5) (2018), 13177–13180.
10.
LiuF., LiangS. and XianX., Optimal Robot Path Planning for Multiple Goals Visiting Based on Tailored Genetic Algorithm, International Journal of Computational Intelligence Systems7(6) (2014), 1109–1122.
11.
ChuK., LeeM. and SunwooM., Local Path Planning for Off-Road Autonomous Driving With Avoidance of Static Obstacles, IEEE Transactions on Intelligent Transportation Systems13(4) (2012), 1599–1616.
12.
KhatibO., Real-time obstacle avoidance for manipulators and mobile robots. Proceedings. 1985 IEEE International Conference on Robotics and Automation. St. Louis, MO, USA, IEEE2 (1985), 500–505. DOI: 10.1109/ROBOT.1985.1087247
13.
ChenY.B., LuoG.C., MeiY.S., YuJ.Q. and SuX.L., UAV path planning using artificial potential field method updated by optimal control theory, International Journal of Systems Science47(6) (2016), 1407–1420. https://doi.org/10.1080/00207721.2014.929191
14.
SunJ., TangJ. and LaoS., Collision avoidance for cooperative UAVs with optimized artificial potential field algorithm, IEEE Access5 (2017), 18382–18390. DOI: 10.1109/ACCESS.2017.2746752
15.
SimmonsR., The curvature-velocity method for local obstacle avoidance. Proceedings of IEEE International Conference on Robotics and Automation. Minneapolis, MN, USA, IEEE4 (1996), 3375–3382. DOI: 10.1109/ROBOT.1996.511023
16.
WangZ., LiangY., GongC., ZhouY., ZengC. and ZhuS., Improved dynamic window approach for Unmanned Surface Vehicles, Local path planning considering the impact of environmental factors, Sensors22(14) (2022), 5181. https://doi.org/10.3390/s22145181
17.
EriksenB.O.H., BreivikM., PettersenK.Y. and WiigM.S., A modified dynamic window algorithm for horizontal collision avoidance for AUVs. 2016 IEEE Conference on Control Applications (CCA). Buenos Aires, Argentina, IEEE (2016), 499–506. DOI: 10.1109/CCA.2016.7587879
18.
WangH., DuanJ., WangM., ZhaoJ. and DongZ., Research on Robot Path Planning Based on Fuzzy Neural Network Algorithm. 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). Chongqing, China, (2018), 1800–1803. DOI: 10.1109/IAEAC.2018.8577599
19.
MuniM.K., ParhiD.R., KumarP.B., SahuC. and KumarS., Towards motion planning of humanoids using a fuzzy embedded neural network approach, Applied Soft Computing119 (2022), 108588. https://doi.org/10.1016/j.asoc.2022.108588
20.
Martınez-AlfaroH. and Gomez-GarcıaS., Mobile robot pathplanning and tracking using simulated annealing and fuzzy logiccontrol, Expert Systems with Applications15(3–4) (1998), 421–429. https://doi.org/10.1016/S0957-4174(98)00055-4
21.
ChenZ.Y. and XuB., AGV Path Planning Based on Improved Artificial Potential Field Method. IEEE International Conference on Power Electronics, Computer Applications (ICPECA). Shenyang, China, IEEE (2021), 32–37. DOI: 10.1109/ICPECA51329.2021.9362519
22.
Orozco-RosasU., PicosK. and MontielO., Hybrid path planning algorithm based on membrane pseudo-bacterial potential field for autonomous mobile robots, IEEE Access7 (2019), 156787–156803. DOI: 10.1109/ACCESS.2019.2949835
23.
Orozco-RosasU., MontielO. and SepúlvedaR., Mobile robot pathplanning using membrane evolutionary artificial potential field, Applied Soft Computing77 (2019), 236–251. https://doi.org/10.1016/j.asoc.2019.01.036
24.
FakoorM., KosariA. and JafarzadehM., Humanoid Robot Path Planning with Fuzzy Markov Decision Processes, Journal of Applied Research and Technology14(5) (2016), 300–310. https://doi.org/10.1016/j.jart.2016.06.006
25.
GuoJ., LiC. and GuoS., A novel step optimal path planning algorithm for the spherical mobile robot based on fuzzy control, IEEE Access8 (2019), 1394–1405. DOI: 10.1109/ACCESS.2019.2962074
26.
KretzschmarH., SpiesM., SprunkC. and BurgardW., Socially compliant mobile robot navigation via inverse reinforcement learning, The International Journal of Robotics Research35(11) (2016), 1307https://doi.org/10.1177/0278364915619772
27.
ChenC., ChenX.Q., MaF., ZengX.J. and WangJ., A knowledge-free path planning approach for smart ships based on reinforcement learning, Ocean Engineering189 (2019), 106299. https://doi.org/10.1016/j.oceaneng.2019.106299
28.
Orozco-RosasU., PicosK., PantrigoJ.J., MontemayorA.S. and Cuesta-InfanteA., Mobile robot path planning using a QAPF learning algorithm for known and unknown environments, IEEE Access10 (2022), 84648–84663. DOI: 10.1109/ACCESS.2022.3197628
29.
LópezJ., OteroC., SanzR., PazE., MolinosE. and BareaR., A new approach to local navigation for autonomous driving vehicles based on the curvature velocity method. 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, IEEE (2019), 1751–1757. DOI: 10.1109/ICRA.2019.8794380
30.
KoN.Y. and SimmonsR.G., The lane-curvature method for local obstacle avoidance. Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No. 98CH0), Victoria, BC, Canada, IEEE3 (1998), 1615–1621. DOI: 10.1109/IROS.1998.724829
31.
GarridoS., MorenoL., AbderrahimM. and MartinF., Path Planning for Mobile Robot Navigation using Voronoi Diagram and Fast Marching. 2006 IEEE/RSJ International Conference on Intelligent Robots & Systems, Beijing, China, (2006), 2376–2381. DOI: 10.1109/IROS.2006.282649
32.
OgrenP. and LeonardN.E., A convergent dynamic window approach to obstacle avoidance, IEEE Transactions on Robotics21(2) (2005), 188–195. DOI: 10.1109/TRO.2004.838008
33.
SinghN.H. and ThongamK., Mobile robot navigation using fuzzy logic in static environments, Procedia Computer Science125 (2018), 11–17. https://doi.org/10.1016/j.procs.2017.12.004
34.
LiX., LiuF., LiuJ. and LiangS., Obstacle avoidance for mobile robot based on improved dynamic window approach, Turkish Journal of Electrical Engineering & Computer Sciences25(2) (2017), 666–676. DOI: 10.3906/elk-1504-194
35.
BianY.M., JiP.C., ZhouY.H. and YangM., Obstacle avoidance path planning of mobile robot based on improved DWA, Chinese Journal of Construction Machinery19(1) (2011), 44–49. DOI: 10.15999/j.cnki.311926.2021.01.008
36.
SaranrittichaiP., NiparnanN. and SudsangA., Robust local obstacle avoidance for mobile robot based on Dynamic Window approach. 2013 10th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand, (2013), 1–4. DOI: 10.1109/ECTICon.2013.6559615
37.
ZhangY., SongJ.Z. and ZhangQ.Q., Local Path Planning of Outdoor Cleaning Robot Based on an Improved DWA, Robot42(5) (2020), 617–625. DOI: 10.13973/j.cnki.robot.190649
38.
LiuT.Y., YanR.X., WeiG.R. and SunL., Local Path Planning Algorithm for Blind-guiding Robot Based on Improved DWA Algorithm. 2019 Chinese Control and Decision Conference (CCDC), Nanchang, China, (2019), 6169–6173. DOI: 10.1109/CCDC.2019.8833259
39.
HuJ., ChengC., WangC., ZhaoC., PanQ. and LiuZ., An Improved Artificial Potential Field Method Based on DWA and Path Optimization. 2021 IEEE International Conference on Unmanned Systems (ICUS), Beijing, China, (2019), 809–814. DOI: 10.1109/ICUS48101.2019.8996014
40.
KobayashiM. and MotoiN., Local Path Planning Method Based on Virtual Manipulators and Dynamic Window Approach for a Wheeled Mobile Robot. 2021 IEEE/SICE International Symposium on System Integration (SII), Iwaki, Fukushima, Japan, (2021), 499–504. DOI: 10.1109/IEEECONF49454.2021.9382692
41.
JinQ., TangC. and CaiW., Research on Dynamic Path Planning Based on the Fusion Algorithm of Improved Ant Colony Optimization and Dynamic Window Method. IEEE Access, 2021. DOI: 10.1109/ACCESS.2021.3064831
42.
YangX., LiH. and ZouJ., An End-to-end Obstacle Avoidance Algorithm for Autonomous Vehicles Based on Global Path Constraint. 2020 Asia-Pacific Conference on Image Processing, Electronics and Computers (IPEC), Dalian, China, (2020), 475–478. DOI: 10.1109/IPEC49694.2020.9115181
43.
Yeong-HoL., Yeong-JunK., Da-UnJ. and Ihn-SikW., Development of an integrated path planning algorithm for autonomous driving of unmanned surface vessel. 2020 20th International Conference on Control, Automation and Systems (ICCAS), Busan, Korea (South), (2020), 27–32. DOI: 10.23919/ICCAS50221.2020.9268353
44.
YoonH.S. and ParkT.H., Path Planning for Autonomous Mobile Robots by Modified Global DWA, The Transactions of The Korean Institute of Electrical Engineers60(2) (2011), 389–397. https://doi.org/10.5370/KIEE.2011.60.2.389
45.
ZhangF., LiN., XueT., ZhuY., YuanR. and FuY., An Improved Dynamic Window Approach Integrated Global Path Planning. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), Dali, China, (2019), 2873–2878. DOI: 10.1109/ROBIO49542.2019.896168
