Research on obstacle avoidance algorithm for unmanned ground vehicle based on multi-sensor information fusion

Jiliang Lv; Chenxi Qu; Shaofeng Du; Xinyu Zhao; Peng Yin; Ning Zhao; Shengguan Qu; Jiliang Lv; Chenxi Qu; Shaofeng Du; Xinyu Zhao; Peng Yin; Ning Zhao; Shengguan Qu

doi:10.3934/mbe.2021055

Mathematical Biosciences and Engineering

2021, Volume 18, Issue 2: 1022-1039. doi: 10.3934/mbe.2021055

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Research article

Research on obstacle avoidance algorithm for unmanned ground vehicle based on multi-sensor information fusion

1.
School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China
2.
State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Inner Mongolia First Machinery Group Co., Ltd., Baotou 014032, China
3.
School of Mechanical, Aerospace and Civil Engineering, The University of Manchester, Manchester, M13 9PL, UK

Received: 17 November 2020 Accepted: 23 December 2020 Published: 05 January 2021

With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.
- multi-sensor information fusion,
- unmanned ground vehicle,
- robotics control,
- obstacle avoidance,
- fuzzy neural network
Citation: Jiliang Lv, Chenxi Qu, Shaofeng Du, Xinyu Zhao, Peng Yin, Ning Zhao, Shengguan Qu. Research on obstacle avoidance algorithm for unmanned ground vehicle based on multi-sensor information fusion[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1022-1039. doi: 10.3934/mbe.2021055

Related Papers:

Abstract

With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.

References

[1]	M. Al-Sagban, R. Dhaouadi, Neural based autonomous navigation of wheeled mobile robots, J. Autom. Mob. Robot. Intell. Syst., 10 (2016), 64–72.
[2]	K. H. Anabi, R. Nordin, N. F. Abdullah, Database-assisted television white space technology: challenges, trends and future research directions, IEEE Access, 4 (2016), 8162–8183. doi: 10.1109/ACCESS.2016.2621178
[3]	D. Chwa, Fuzzy adaptive tracking control of wheeled mobile robots with state-dependent kinematic and dynamic disturbances, IEEE Trans. Fuzzy Syst., 20 (2012), 587–593. doi: 10.1109/TFUZZ.2011.2176738
[4]	K. Goldberg, One Robot is Robotics, Ten Robots is Automation, IEEE Trans. Autom. Sci. Eng., 13 (2016), 1418–1419. doi: 10.1109/TASE.2016.2606859
[5]	C. M. Luo, J. Y. Gao, X. D. Li, H. W. Mo, Q. M Jiang, Sensor-based autonomous robot navigation under unknown environments with grid map representation. 2014 IEEE Symposium on Swarm Intelligence, (2014), pp. 1–7.
[6]	A. Mukhtar, L. Xia, T. B. Tang, Vehicle detection techniques for collision avoidance systems: A review, IEEE Trans. Intell. Transp. Syst., 16 (2015), 2318–2338. doi: 10.1109/TITS.2015.2409109
[7]	P. Subbash, K. T. Chong, Adaptive network fuzzy inference system based navigation controller for mobile robot, Front. Inform. Technol. Elect. Eng., 20 (2019), 141–151. doi: 10.1631/FITEE.1700206
[8]	C. Treesatayapun, Discrete-time direct adaptive control for robotic systems based on model-free and if–then rules operation, Int. J. Adv. Manuf. Technol., 68 (2013), 575–590. doi: 10.1007/s00170-013-4779-2
[9]	C.-C. Tsai, H.-L. Wu, F.-C. Tai, Y.-S. Chen, Distributed consensus formation control with collision and obstacle avoidance for uncertain networked omnidirectional multi-robot systems using fuzzy wavelet neural networks, Int. J. Fuzzy Syst., 19 (2016), 1375–1391.
[10]	J. Savage, S. Muñoz, M. Matamoros, R. Osorio, Obstacle avoidance behaviors for mobile robots using genetic algorithms and recurrent neural networks, IFAC Proceed. Vol., 46 (2013), 141–146.
[11]	A. Pandey, S. Kumar, K. K. Pandey, D. R. Parhi, Mobile robot navigation in unknown static environments using ANFIS controller, Perspect. Sci., 8 (2016), 421–423. doi: 10.1016/j.pisc.2016.04.094
[12]	C.-J. Kim, D. Chwa, Obstacle avoidance method for wheeled mobile robots using interval type-2 fuzzy neural network, IEEE Trans. Fuzzy Syst., 23 (2015), 677–687. doi: 10.1109/TFUZZ.2014.2321771
[13]	S. Goudarzi, N. Kama, M. H. Anisi, S. Zeadally, S. Mumtaz, Data collection using unmanned aerial vehicles for Internet of Things platforms, Comput. Electr. Eng., 75 (2019), 1–15. doi: 10.1016/j.compeleceng.2019.01.028
[14]	D. Z. Wan, C. S. Chin, Simulation and prototype testing of a low-cost ultrasonic distance measurement device in underwater, J. Mar. Sci. Technol., 20 (2014), 142–154.
[15]	H. Yang, X. Fan, P. Shi, C. Hua, Nonlinear control for tracking and obstacle avoidance of a wheeled mobile robot with nonholonomic constraint, IEEE Trans. Control Syst. Technol., (2015), 1.
[16]	A. M. Alajlan, M. M. Almasri, K. M. Elleithy, Multi-sensor based collision avoidance algorithm for mobile robot. 2015 Long Island Systems, Applications and Technology, (2015), pp. 1–6.
[17]	Z. H. Duan, T. H. Wu, S. W. Guo, T. Shao, R. Malekian, Z. Li, Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: A review, Int. J. Adv. Manuf. Technol., 96 (2018), 803–819. doi: 10.1007/s00170-017-1474-8
[18]	S. W. Yoon, S.-B. Park, J. S. Kim, Kalman filter sensor fusion for mecanum wheeled automated guided vehicle localization, J. Sens., 2015 (2015), 1–7.
[19]	B. Khaleghi, A. Khamis, F. O. Karray, S. N. Razavi, Multisensor data fusion: A review of the state-of-the-art, Inf. Fusion, 14 (2013), 28–44. doi: 10.1016/j.inffus.2011.08.001
[20]	M. Alatise, G. Hancke, Pose estimation of a mobile robot based on fusion of IMU data and vision data using an extended kalman filter, Sensors, 17 (2017).
[21]	B. F. Ji, Y. Q. Li, D. Cao, C. G. Li, S. Mumtaz, D. Wang, Secrecy performance analysis of UAV assisted relay transmission for cognitive network with energy harvesting, IEEE Trans. Veh. Technol., 69 (2020), 7404–7415. doi: 10.1109/TVT.2020.2989297
[22]	T. Tang, T. Hong, H. H. Hong, S. Y. Ji, S. Mumtaz, M. Cheriet, An improved UAV-PHD filter-based trajectory tracking algorithm for multi-UAVs in future 5G IoT scenarios, Electronics, 8 (2019).
[23]	Z. Y. Lin, L. L. Wang, Z. M. Han, M. Y. Fu, Distributed formation control of multi-agent systems using complex laplacian, IEEE Trans. Autom. Control, 59 (2014), 1765–1777. doi: 10.1109/TAC.2014.2309031
[24]	C. G. Zong, Z. J. Ji, Y. Yu, H. Shi, Research on obstacle avoidance method for mobile robot based on multisensor information fusion, Sens. Mater., 32 (2020).
[25]	T. Tian, S. L. Sun, N. Li, Multi-sensor information fusion estimators for stochastic uncertain systems with correlated noises, Inf. Fusion, 27 (2016), 126–137. doi: 10.1016/j.inffus.2015.06.001
[26]	M. Almasri, K. Elleithy, A. Alajlan, Sensor fusion based model for collision free mobile robot navigation, Sensors (Basel), 16 (2015).
[27]	A. Al-Mayyahi, W. Wang, P. Birch, Adaptive neuro-fuzzy technique for autonomous ground vehicle navigation, Robotics, 3 (2014), 349–370. doi: 10.3390/robotics3040349
[28]	D. Y. Qu, Y. H. Hu, Y. T. Zhang, The investigation of the obstacle avoidance for mobile robot based on the multi sensor information fusion technology, Int. J. Mater., Mecha. Manuf. (2013), 366–370.
[29]	I. Aydin, S. B. Celebi, S. Barmada, M. Tucci, Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system, Proc. Inst. Mech. Eng. Part F-J. Rail Rapid Transit, 232 (2016), 159–170.
[30]	H. L. Xiong, Z. Z. Mai, J. Tang, F. Hen, Robust GPS/INS/DVL navigation and positioning method using adaptive federated strong tracking filter based on weighted least square principle, IEEE Access, 7 (2019), 26168–26178. doi: 10.1109/ACCESS.2019.2897222
[31]	F. Xiao, B. Qin, A weighted combination method for conflicting evidence in multi-sensor data fusion, Sensors (Basel), 18 (2018).
[32]	D. H. Li, C. Shen, X. P. Dai, X. Zhu, Z. Liang, Research on data fusion of adaptive weighted multi-source sensor, CMC-Comput. Mat. Contin., 61 (2019), 1217–1231.
[33]	C.-H. Hsu, C.-F. Juang, Evolutionary robot wall-following control using type-2 fuzzy controller with Species-DE-Activated continuous ACO, IEEE Trans. Fuzzy Syst., 21 (2013), 100–112. doi: 10.1109/TFUZZ.2012.2202665
[34]	G.-D. Wu, P.-H. Huang, A vectorization-optimization-method-based type-2 fuzzy neural network for noisy data classification, IEEE Trans. Fuzzy Syst., 21 (2013), 1–15. doi: 10.1109/TFUZZ.2012.2197754
[35]	M. Faisal, R. Hedjar, M. Al Sulaiman, K. Al-Mutib, Fuzzy logic navigation and obstacle avoidance by a mobile robot in an unknown dynamic environment, Int. J. Adv. Robot. Syst., 10 (2013).
[36]	D. Wu, Approaches for reducing the computational cost of interval type-2 fuzzy logic systems: overview and comparisons, IEEE Trans. Fuzzy Syst., 21 (2013), 80–99. doi: 10.1109/TFUZZ.2012.2201728
[37]	H. Boubertakh, M. Tadjine, P. Y. Glorennec, A new mobile robot navigation method using fuzzy logic and a modified Q-learning algorithm, J. Intell. Fuzzy Syst., 21 (2010), 113–119. doi: 10.3233/IFS-2010-0440
[38]	P. Melin, L. Astudillo, O. Castillo, F. Valdez, Optimal design of type-2 and type-1 fuzzy tracking controllers for autonomous mobile robots under perturbed torques using a new chemical optimization paradigm, Expert Syst. Appl., 40 (2013), 3185–3195. doi: 10.1016/j.eswa.2012.12.032
[39]	J. R. Castro, O. Castillo, P. Melin, A. Rodríguez-Díaz, A hybrid learning algorithm for a class of interval type-2 fuzzy neural networks, Inf. Sci., 179 (2009), 2175–2193.

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