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Intelligent cooperative collision avoidance via fuzzy potential fields

Published online by Cambridge University Press:  18 October 2021

Daegyun Choi Open the ORCID record for Daegyun Choi [Opens in a new window] ,
Anirudh Chhabra Open the ORCID record for Anirudh Chhabra [Opens in a new window]  and
Donghoon Kim Open the ORCID record for Donghoon Kim [Opens in a new window]
Daegyun Choi
Affiliation:
Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati
Anirudh Chhabra
Affiliation:
Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati
Donghoon Kim*
Affiliation:
Department of Aerospace Engineering and Engineering Mechanics, University of Cincinnati
*
*Corresponding author. E-mail:donghoon.kim@uc.edu
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Summary

This paper proposes an intelligent cooperative collision avoidance approach combining the enhanced potential field (EPF) with a fuzzy inference system (FIS) to resolve local minima and goal non-reachable with obstacles nearby issues and provide a near-optimal collision-free trajectory. A genetic algorithm is utilized to optimize parameters of membership function and rule base of the FISs. This work uses a single scenario containing all issues and interactions among unmanned aerial vehicles (UAVs) for training. For validating the performance, two scenarios containing obstacles with different shapes and several UAVs in small airspace are considered. Multiple simulation results show that the proposed approach outperforms the conventional EPF approach statistically.

Keywords

collision avoidancefuzzy inference systemenhanced potential fieldgenetic algorithm
Type
Research Article
Information
Robotica , Volume 40 , Issue 6 , June 2022 , pp. 1919 - 1938
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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References

Locascio, D., Levy, M., Ravikumar, K., German, B., Briceno, S. I and Mavris, D. N., “Evaluation of Concepts of Operations for sUAS Package Delivery,” In: AIAA Aviation Technology, Integration, and Operations Conference, Washington, D.C. (2016).Google Scholar
Noriega, A. and Anderson, R., “Linear-Optimization-Based Path Planning Algorithm for an Agricultural UAV,” In: AIAA Infotech at Aerospace 2016, San Diego, CA (2016)CrossRefGoogle Scholar
Kim, S., Paes, D., Lee, K., Irizarry, J. and Johnson, E., “UAS-Based Airport Maintenance Inspections: Lessons Learned from Pilot Study Implementation,” In: Computing in Civil Engineering 2019 : Smart Cities, Sustainability, and Resilience (2019).Google Scholar
Bhandari, S., Bettadapura, A., Dadian, O., Patel, N., Dayton, J. and Gan, M., “Search and Rescue using Unmanned Aerial Vehicles,” In: AIAA Infotech at Aerospace 2015, Kissimmee, FL (2015)CrossRefGoogle Scholar
Tsach, S., Peled, A., Penn, D., Keshales, B. and Guedj, R., “Development Trends for Next Generation of UAV Systems,” In: AIAA Infotech at Aerospace 2007 Conference and Exhibit, Rohnert Park, CA (2007) pp. 1–14.Google Scholar
Hoy, M., Matveev, A. and Savkin, A., “Algorithms for collision-free navigation of mobile robots in complex cluttered environments: A survey,” Robotica 33(3), 463–497 (2015).CrossRefGoogle Scholar
Diankov, R. and Kuffner, J., “Randomized Statistical Path Planning,” In: 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, San Diego, CA (2007) pp. 1–6.Google Scholar
Sanchez-Lopez, J. L., Wang, M., Olivares-Mendez, M. A., Molina, M. and Voos, H., “A Real-Time 3D Path Planning Solution for Collision-Free Navigation of Multirotor Aerial Robots in Dynamic Environments,” J. Intell. Robot. Syst. 93, 3–53 (2019).CrossRefGoogle Scholar
Temizer, S., Kochenderfer, M., Kaelbling, L., Lozano-Perez, T. and Kuchar, J., “Collision Avoidance for Unmanned Aircraft using Markov Decision Processes,” In: AIAA Guidance, Navigation, and Control Conference (2012).Google Scholar
Strobel, A. and Schwarzbach, M., “Cooperative Sense and Avoid: Implementation in Simulation and Real World for Small Unmanned Aerial Vehicles,” In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, FL (2014) pp. 1253–1258Google Scholar
Park, J., Oh, H. and Tahk, M., “UAV Collision Avoidance Based on Geometric Approach,” In: 2008 SICE Annual Conference, Tokyo (2008) pp. 2122–2126 Google Scholar
Zhang, X., Liniger, A. and Borrelli, F., “Optimization-Based Collision Avoidance,” IEEE Trans. Cont. Syst. Technol. 29(3), 1–12 (2021).CrossRefGoogle Scholar
Elmokadem, T. and Savkin, A. V., “A Method for Autonomous Collision-Free Navigation of a Quadrotor UAV in Unknown Tunnel-Like Environments,” Robotica, 1–27 (2021).CrossRefGoogle Scholar
Mehdi, S., Choe, R. and Hovakimyan, N., “Piecewise Bézier Curves for Avoiding Collisions during Multivehicle Coordinated Missions,” J. Guid. Cont. Dyn. 40(7), 1567–1578 (2017).CrossRefGoogle Scholar
Khatib, O., “Real-Time Obstacle Avoidance for Manipulators and Mobile Robots,” In: Proceedings 1985 IEEE International Conference on Robotics and Automation, St. Louis, MO, USA (1985) pp. 500–505.Google Scholar
Amiryan, J. and Jamzad, M., “Adaptive Motion Planning with Artificial Potential Fields Using a Prior Path,” In: 2015 3rd RSI International Conference on Robotics and Mechatronics (ICROM), Tehran, Iran (2015) pp. 731–736.Google Scholar
Wang, M., Su, Z., Tu, D. and Lu, X., “A Hybrid Algorithm Based on Artificial Potential Field and BUG for Path Planning of Mobile Robot,” In: 2013 2nd International Conference on Measurement, Information and Control, Harbin, China (2013) pp. 1393–1398.Google Scholar
Park, J., Kwak, H., Kang, Y. and Kim, D., “Advanced Fuzzy Potential Field Method for Mobile Robot Obstacle Avoidance,” Computat. Intell. Neurosci. 2016, 1–13 (2016).Google ScholarPubMed
Azzabi, A. and Nouri, K., “An Advanced Potential Field Method Proposed for Mobile Robot Path Planning,” Trans. Inst. Meas. Cont. 41(11), 3132–3144 (2019).CrossRefGoogle Scholar
Triharminto, H., Wahyunggoro, O., Adji, T., Cahyadi, A. and Ardiyanto, I., “A Novel of Repulsive Function on Artificial Potential Field for Robot Path Planning,” Int. J. Electric. Comput. Eng. 6(6), 3262–3275 (2016).Google Scholar
Weerakoon, T., Ishii, K., Ali, A. and Nassiraei, F., “An Artificial Potential Field Based Mobile Robot Navigation Method to Prevent from Deadlock,” J. Artif. Intell. Soft Comput. Res. 5(3), 189–203 (2015).CrossRefGoogle Scholar
Cho, J., Pae, D., Lim, M. and Kang, T., “A Real-Time Obstacle Avoidance Method for Autonomous Vehicles using an Obstacle-Dependent Gaussian Potential Field,” J. Adv. Transport. 2018, 1–15 (2018).CrossRefGoogle Scholar
Zhang, Y., Liu, Z. and Chang, L., “A New Adaptive Artificial Potential Field and Rolling Window Method for Mobile Robot Path Planning,” In: 2017 29th Chinese Control And Decision Conference (CCDC), Chongqing, China (2017) pp. 7144–7148.Google Scholar
Yang, X., Yang, W., Zhang, H., Chang, H., Chen, C. and Zhang, S., “A New Method for Robot Path Planning Based Artificial Potential Field,” In: 2016 IEEE 11th Conference on Industrial Electronics and Applications (ICIEA), Hefei, China (2016) pp. 1294–1299.Google Scholar
Pan, Z., Li, D., Yang, K. and Deng, H., “Multi-Robot Obstacle Avoidance Based on the Improved Artificial Potential Field and PID Adaptive Tracking Control Algorithm,” Robotica 37(11), 1883–1903 (2019).CrossRefGoogle Scholar
Sabo, C. and Cohen, K., “Fuzzy Logic Unmanned Air Vehicle Motion Planning,” Adv. Fuzzy Syst. 2012, 1–14 (2012).Google Scholar
Vadakkepat, P., Lee, T. and Xin, L., “Application of Evolutionary Artificial Potential Field in Robot Soccer System,” In: Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference, Vancouver, BC, Canada (2001) pp. 2781–2785.Google Scholar
Lee, K., Choi, D. and Kim, D., “Incorporation of Potential Fields and Motion Primitives for the Collision Avoidance of Unmanned Aircraft,” Appl. Sci. 11(7), 1–19 (2021).Google Scholar
Choi, D., Lee, K. and Kim, D., “Enhanced Potential Field-Based Collision Avoidance for Unmanned Aerial Vehicles in a Dynamic Environment,” In: 2020 AIAA Science and Technology Forum and Exposition, Orlando, FL (2020) pp. 1–7.Google Scholar
Sun, J., Liu, G., Tian, G. and Zhang, J., “Smart Obstacle Avoidance using a Danger Index for a Dynamic Environment,” Appl. Sci. 9(8), 1–14 (2019)CrossRefGoogle Scholar
Sun, J., Tang, J. and Lao, S., “Collision Avoidance for Cooperative UAVs with Optimized Artificial Potential Field Algorithm,” IEEE Access 5, 18382–18390 (2017).CrossRefGoogle Scholar
Holland, J., Adaptation in Natural and Artificial Systems (MIT Press, Cambridge, MA and London, England, 1992).CrossRefGoogle Scholar
Goldberg, D., Genetic Algorithms in Search, Optimization and Machine Learning (Addison-Wesley Longman Publishing Co., Inc., USA, 1989).Google Scholar
Zadeh, L., “Fuzzy Sets,” Inform. Cont. 8(3), 338–353 (1965).CrossRefGoogle Scholar
Mamdani, E., “Application of Fuzzy Algorithms for Control of Simple dynamic plant,” Proc. Inst. Electric. Eng. 121(12), 1585–1588 (1974).CrossRefGoogle Scholar
Ross, T., Fuzzy Logic with Engineering Applications (John Wiley & Sons Ltd., Chichester, West Sussex, United Kingdom, 2016).Google Scholar
Ohad, Gal, “fit_ellipse,” MATLAB Central File Exchange, 2020. [Online]. Available: https://www.mathworks.com/matlabcentral/fileexchange/3215-fit_ellipse Google Scholar
DJI, “DJI - The World Leader in Camera Drones/Quadcopters for Aerial Photography,” DJI, 2020. [Online]. Available: https://www.dji.com/ Google Scholar
Barber, C. B., Dobkin, D. P. and Huhdanpaa, H., “The Quickhull Algorithm for Convex Hulls,” ACM Trans. Math. Softw. 22(4), 469–483 (1996).CrossRefGoogle Scholar