Boundary Gap Based Reactive Navigation in Unknown Environments

Zhao Gao; Jiahu Qin; Shuai Wang; Yaonan Wang

doi:10.1109/JAS.2021.1003841

Volume 8 Issue 2

Feb. 2021

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2021 > 8(2): 468-477

Zhao Gao, Jiahu Qin, Shuai Wang and Yaonan Wang, "Boundary Gap Based Reactive Navigation in Unknown Environments," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 468-477, Feb. 2021. doi: 10.1109/JAS.2021.1003841

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Zhao Gao, Jiahu Qin, Shuai Wang and Yaonan Wang, "Boundary Gap Based Reactive Navigation in Unknown Environments," IEEE/CAA J. Autom. Sinica, vol. 8, no. 2, pp. 468-477, Feb. 2021. doi: 10.1109/JAS.2021.1003841

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Boundary Gap Based Reactive Navigation in Unknown Environments

doi: 10.1109/JAS.2021.1003841

Funds: This work was supported in part by the National Natural Science Foundation of China (61922076, 61873252), and in part by the Fok Ying-Tong Education Foundation for Young Teachers in Higher Education Institutions of China (161059)

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Author Bio:
Zhao Gao received the B.E. degree in automation from the University of Electronic Science and Technology of China, Chengdu, China, in 2018. He is currently a Master student in control science and engineering at the University of Science and Technology of China, Hefei, China. His current research interests include path planning and navigation of mobile robots

Jiahu Qin (M’12–SM’18) received the first Ph.D. degree in control science and engineering from the Harbin Institute of Technology, Harbin, China in 2012, and the second Ph.D. degree in systems and control from Australian National University, Canberra, Australia in 2014. He is currently a Professor with the Department of Automation, University of Science and Technology of China, Hefei, China. His current research interests include multiagent systems, cyber-physical systems, and complex dynamical networks

Shuai Wang (S’19) received the B.E. degree in automation from Northeast Forestry University, Harbin, China in 2016. He is currently a Ph.D. candidate in control science and engineering at the University of Science and Technology of China, Hefei, China. His current research interests include perception and control in robotics

Yaonan Wang received the B.S. degree in computer engineering from East China University of Technology, Nanchang, China in 1981, and the M.S. and Ph.D. degrees in electrical engineering from Hunan University, Changsha, China in 1990 and 1994, respectively. From 1994 to 1995, he was a Post-Doctoral Research Fellow with the National University of Defence Technology, China. From 1998 to 2000, he was a Senior Humboldt Fellow in Germany, and from 2001 to 2004, he was a Visiting Professor with the University of Bremen, Bremen, Germany. Since 1995, he has been a Professor with Hunan University, China. He is an Academician of the Chinese Academy of Engineering. His current research interests include intelligent perception and control, robotics, and computer vision systems for industrial applications
Corresponding author: Z. Gao, J. H. Qin and S. Wang are with the Department of Automation, University of Science and Technology of China, Hefei 230027, China (e-mail: gaozhao@mail.ustc.edu.cn; jhqin@ustc.edu.cn; wsustcid@mail.ustc.edu.cn)
Received Date: 2020-08-21
Accepted Date: 2020-10-01

Available Online: 2020-10-22

Abstract

Abstract

Due to the requirements for mobile robots to search or rescue in unknown environments, reactive navigation which plays an essential role in these applications has attracted increasing interest. However, most existing reactive methods are vulnerable to local minima in the absence of prior knowledge about the environment. This paper aims to address the local minimum problem by employing the proposed boundary gap (BG) based reactive navigation method. Specifically, the narrowest gap extraction algorithm (NGEA) is proposed to eliminate the improper gaps. Meanwhile, we present a new concept called boundary gap which enables the robot to follow the obstacle boundary and then get rid of local minima. Moreover, in order to enhance the smoothness of generated trajectories, we take the robot dynamics into consideration by using the modified dynamic window approach (DWA). Simulation and experimental results show the superiority of our method in avoiding local minima and improving the smoothness.
- Boundary gap,
- local minima,
- reactive navigation,
- unknown environments

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References(35)

References

[1]	S. McLeod and J. Xiao, “Navigating dynamically unknown environments leveraging past experience,” in Proc. Int. Conf. on Robotics and Automation (ICRA), IEEE, 2019, pp. 29–35.
[2]	J. Minguez and L. Montano, “Nearness diagram (ND) navigation: collision avoidance in troublesome scenarios,” IEEE Trans. Robotics and Automation, vol. 20, no. 1, pp. 45–59, 2004. doi: 10.1109/TRA.2003.820849
[3]	N. Wen, L. Zhao, X. Su, and P. Ma, “Uav online path planning algorithm in a low altitude dangerous environment,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 2, pp. 173–185, 2015. doi: 10.1109/JAS.2015.7081657
[4]	W. Zheng, F. Zhou, and Z. Wang, “Robust and accurate monocular visual navigation combining imu for a quadrotor,” IEEE/CAA Journal of Automatica Sinica, vol. 2, no. 1, pp. 33–44, 2015. doi: 10.1109/JAS.2015.7032904
[5]	A. Ataka, H. K. Lam, and K. Althoefer, “Reactive magnetic-fieldinspired navigation for non-holonomic mobile robots in unknown environments,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), 2018, pp. 6983–6988.
[6]	O. Khatib, “Real-time obstacle avoidance for manipulators and mobile robots,” in Autonomous Robot Vehicles, Springer, 1986, pp. 396–404.
[7]	Y. Koren and J. Borenstein, “Potential field methods and their inherent limitations for mobile robot navigation,” in Proc. IEEE Int. Conf. on Robotics and Automation, IEEE, 1991, pp. 1398–1404.
[8]	K. P. Valavanis, T. Hebert, R. Kolluru, and N. Tsourveloudis, “Mobile robot navigation in 2D dynamic environments using an electrostatic potential field,” IEEE Trans. Systems,Man,and CyberneticsPart A:Systems and Humans, vol. 30, no. 2, pp. 187–196, 2000. doi: 10.1109/3468.833100
[9]	W. Lu, G. Zhang, and S. Ferrari, “An information potential approach to integrated sensor path planning and control,” IEEE Trans. Robotics, vol. 30, no. 4, pp. 919–934, 2014. doi: 10.1109/TRO.2014.2312812
[10]	A. R. Rafsanzani, R. C. Hidayat, A. I. Cahyadi, and S. Herdjunanto, “Omnidirectional sensing for escaping local minimum on potential field mobile robot path planning in corridors environment,” in Proc. 3rd Int. Seminar on Sensors, Instrumentation, Measurement and Metrology (ISSIMM), IEEE, 2018, pp. 79–83.
[11]	T. Chen and Y. Huang, “Non-trap artificial potential field based on virtual obstacle,” in Proc. IEEE 16th Int. Conf. on Networking, Sensing and Control (ICNSC), IEEE, 2019, pp. 275–280.
[12]	T. Y. Abdalla, A. A. Abed, and A. A. Ahmed, “Mobile robot navigation using pso-optimized fuzzy artificial potential field with fuzzy control,” Journal of Intelligent &Fuzzy Systems, vol. 32, no. 6, pp. 3893–3908, 2017.
[13]	V. Lumelsky and A. Stepanov, “Dynamic path planning for a mobile automaton with limited information on the environment,” IEEE Transactions on Automatic Control, vol. 31, no. 11, pp. 1058–1063, 1986. doi: 10.1109/TAC.1986.1104175
[14]	N. Sharma, S. Thukral, S. Aine, and P. Sujit, “A virtual bug planning technique for 2d robot path planning,” in Proc. Annual American Control Conf. (ACC), IEEE, 2018, pp. 5062–5069.
[15]	A. K. Dutta, S. K. Debnath, and S. K. Das, “Local path planning of mobile robot using critical-pointbug algorithm avoiding static obstacles,” Int. Journal of Robotics and Automation, vol. 5, no. 3, pp. 182–187, 2016.
[16]	Q. L. Xu, T. Yu, and J. Bai, “The mobile robot path planning with motion constraints based on bug algorithm,” in Proc. Chinese Automation Congress (CAC), IEEE, 2017, pp. 2348–2352.
[17]	N. Buniyamin, W. W. Ngah, N. Sariff, and Z. Mohamad, “A simple local path planning algorithm for autonomous mobile robots,” Int. Journal of Systems Applications,Engineering &Development, vol. 5, no. 2, pp. 151–159, 2011.
[18]	J. W. Durham and F. Bullo, “Smooth nearness-diagram navigation,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IEEE, 2008, pp. 690–695.
[19]	M. Mujahad, D. Fischer, B. Mertsching, and H. Jaddu, “Closest gap based (CG) reactive obstacle avoidance navigation for highly cluttered environments,” in Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, IEEE, 2010, pp. 1805–1812.
[20]	M. Mujahed, D. Fischer, and B. Mertsching, “Admissible gap navigation: A new collision avoidance approach,” Robotics and Autonomous Systems, vol. 103, pp. 93–110, 2018. doi: 10.1016/j.robot.2018.02.008
[21]	M. Mujahed and B. Mertsching, “The admissible gap (AG) method for reactive collision avoidance,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA), IEEE, 2017, pp. 1916–1921.
[22]	M. Mujahed, D. Fischer, and B. Mertsching, “Tangential gap flow (TGF) navigation: A new reactive obstacle avoidance approach for highly cluttered environments,” Robotics and Autonomous Systems, vol. 84, pp. 15–30, 2016. doi: 10.1016/j.robot.2016.07.001
[23]	D. Fox, W. Burgard, and S. Thrun, “The dynamic window approach to collision avoidance,” IEEE Robotics &Automation Magazine, vol. 4, no. 1, pp. 23–33, 1997.
[24]	J. Ballesteros, C. Urdiales, A. B. M. Velasco, and G. Ramos-Jiménez, “A biomimetical dynamic window approach to navigation for collaborative control,” IEEE Trans. Human-Machine Systems, vol. 47, no. 6, pp. 1123–1133, 2017. doi: 10.1109/THMS.2017.2700633
[25]	L. Xie, C. Henkel, K. Stol, and W. Xu, “Power-minimization and energyreduction autonomous navigation of an omnidirectional mecanum robot via the dynamic window approach local trajectory planning,” Int. Journal of Advanced Robotic Systems, vol. 15, no. 1, pp. 1–12, 2018.
[26]	D. A. De Lima and G. A. S. Pereira, “Navigation of an autonomous car using vector fields and the dynamic window approach,” Journal of Control,Automation and Electrical Systems, vol. 24, no. 1–2, pp. 106–116, 2013. doi: 10.1007/s40313-013-0006-5
[27]	M. Missura and M. Bennewitz, “Predictive collision avoidance for the dynamic window approach,” in Proc. Int. Conf. on Robotics and Automation (ICRA), IEEE, 2019, pp. 8620–8626.
[28]	P. Fiorini and Z. Shiller, “Motion planning in dynamic environments using velocity obstacles,” The Int. Journal of Robotics Research, vol. 17, no. 7, pp. 760–772, 1998. doi: 10.1177/027836499801700706
[29]	T. Xu, S. Zhang, Z. Jiang, Z. Liu, and H. Cheng, “Collision avoidance of high-speed obstacles for mobile robots via maximum-speed aware velocity obstacle method,” IEEE Access, vol. 8, pp. 138 493–138 507, 2020.
[30]	X. Zhu, J. Yi, H. Ding, and L. He, “Velocity obstacle based on vertical ellipse for multi-robot collision avoidance,” Journal of Intelligent &Robotic Systems, vol. 99, no. 1, pp. 183–208, 2020.
[31]	Z. Liu, Z. Jiang, T. Xu, H. Cheng, Z. Xie, and L. Lin, “Avoidance of high-speed obstacles based on velocity obstacles,” in Proc. IEEE Int. Conf. on Robotics and Automation (ICRA). IEEE, 2018, pp. 7624–7630.
[32]	G. T. Toussaint and B. K. Bhattacharya, “Optimal algorithms for computing the minimum distance between two finite planar sets,” Pattern Recognition Letters, vol. 2, no. 2, pp. 79–82, 1983. doi: 10.1016/0167-8655(83)90041-7
[33]	J. Borenstein and Y. Koren, “Real-time obstacle avoidance for fast mobile robots,” IEEE Trans. systems,Man,and Cybernetics, vol. 19, no. 5, pp. 1179–1187, 1989. doi: 10.1109/21.44033
[34]	D. Calisi and D. Nardi, “Performance evaluation of pure-motion tasks for mobile robots with respect to world models,” Autonomous Robots, vol. 27, no. 4, pp. 465–481, 2009. doi: 10.1007/s10514-009-9150-y
[35]	C. Ye and P. Webb, “A sub goal seeking approach for reactive navigation in complex unknown environments,” Robotics and Autonomous Systems, vol. 57, no. 9, pp. 877–888, 2009. doi: 10.1016/j.robot.2009.06.009

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Highlights

The narrowest gap extraction algorithm (NGEA) is proposed which extracts the narrowest gap to evaluate the accessibility reliably.
We present the boundary gap which is a new concept for reactive navigation, and design a strategy of identifying the boundary gap that enables the robot to move along the obstacle boundary, which can lessen the possibility of ending in local minima. To the best of our knowledge, it is the first time to employ the gap based method to tackle the local minimum problem.
The navigation performance in terms of the trajectory smoothness is improved by applying a modified dynamic window approach (DWA) into our framework.

Boundary Gap Based Reactive Navigation in Unknown Environments

doi: 10.1109/JAS.2021.1003841

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