From Nature to UAV - A Study on Collision Avoidance in Bee Congregation
Автор: Nahin Hossain Uday, Md. Zahid Hasan, Rejwan Ahmed, Md. Mahmudur Rahman, Abhijit Bhowmik, Debajyoti Karmaker
Журнал: International Journal of Intelligent Systems and Applications @ijisa
Статья в выпуске: 3 vol.16, 2024 года.
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Insects engage in a variety of survival-related activities, including feeding, mating, and communication, which are frequently motivated by innate impulses and environmental signals. Social insects, such as ants and bees, exhibit complex collective behaviors. They carry out well-organized duties, including defense, nursing, and foraging, inside their colonies. For analyzing the behavior of any living entity, we selected honeybees (Apis Mellifera) and worked on a small portion of it. We have captured the video of honeybees flying close to a hive (human-made artificial hive) while the entrance was temporarily sealed which resulted in the” bee cloud”. An exploration of the flight trajectories executed and a 3D view of the” bee cloud” constructed. We analyzed the behaviors of honeybees, especially on their speed and distance. The results showed that the loitering honeybees performed turns that are fully coordinated, and free of sideslips so thus they made no collision between themselves which inspired us to propose a method for avoiding collision in unmanned aerial vehicle. This paper gives the collective behavioral information and analysis report of the small portion of data set (honeybees), that bee maintains a safe distance (35mm) to avoid collision.
Honeybee, Collision Avoidance, Safe Distance, Unmanned Aerial Vehicle
Короткий адрес: https://sciup.org/15019371
IDR: 15019371 | DOI: 10.5815/ijisa.2024.03.06
Список литературы From Nature to UAV - A Study on Collision Avoidance in Bee Congregation
- S.-C. Chu, H.-C. Huang, J. F. Roddick, and J.-S. Pan, “Overview of Algorithms for Swarm Intelligence,” 2011, pp. 28–41. doi: 10.1007/978-3-642-23935-9_3.
- M. Dorigo and M. Birattari, “Ant Colony Optimization,” in Encyclopedia of Machine Learning, Boston, MA: Springer US, 2011, pp. 36–39. doi: 10.1007/978-0-387-30164-8_22.
- “Artificial Immune Systems,” in Introduction to Evolutionary Algorithms, London: Springer London, 2010, pp. 355–379. doi: 10.1007/978-1-84996-129-5_9.
- P. A. Vikhar, “Evolutionary algorithms: A critical review and its future prospects,” in 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), IEEE, Dec. 2016, pp. 261–265. doi: 10.1109/ICGTSPICC.2016.7955308.
- L. Liu, Yuning Song, H. Ma, and X. Zhang, “Physarum optimization: A biology-inspired algorithm for minimal exposure path problem in wireless sensor networks,” in 2012 Proceedings IEEE INFOCOM, IEEE, Mar. 2012, pp. 1296–1304. doi: 10.1109/INFCOM.2012.6195492.
- N. Krasnogor, “Memetic Algorithms,” in Handbook of Natural Computing, Berlin, Heidelberg: Springer Berlin Heidelberg, 2012, pp. 905–935. doi: 10.1007/978-3-540-92910-9_29.
- X.-M. Zeng, M. Ito, and E. Shimizu, “Collision avoidance of moving obstacles for ship with genetic algorithm,” in 6th International Workshop on Advanced Motion Control Proceedings, IEEE, 2000, pp. 513–518. doi: 10.1109/AMC.2000.862927.
- Xiao-ming Zeng, M. Ito, and E. Shimizu, “Building an automatic control system of maneuvering ship in collision situation with genetic algorithms,” in Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148), IEEE, 2001, pp. 2852–2853 vol.4. doi: 10.1109/ACC.2001.946330.
- G. Maimon, A. D. Straw, and M. H. Dickinson, “A Simple Vision-Based Algorithm for Decision Making in Flying Drosophila,” Current Biology, vol. 18, no. 6, pp. 464–470, Mar. 2008, doi: 10.1016/j.cub.2008.02.054.
- F. van Breugel and M. H. Dickinson, “The visual control of landing and obstacle avoidance in the fruit fly Drosophila melanogaster,” Journal of Experimental Biology, vol. 215, no. 11, pp. 1783–1798, Jun. 2012, doi: 10.1242/jeb.066498.
- M. B. Milde, O. J. N. Bertrand, R. Benosmanz, M. Egelhaaf, and E. Chicca, “Bioinspired event-driven collision avoidance algorithm based on optic flow,” in 2015 International Conference on Event-based Control, Communication, and Signal Processing (EBCCSP), IEEE, Jun. 2015, pp. 1–7. doi: 10.1109/EBCCSP.2015.7300673.
- M. Y. Mahadeeswara and M. V. Srinivasan, “Coordinated Turning Behaviour of Loitering Honeybees,” Sci Rep, vol. 8, no. 1, p. 16942, Nov. 2018, doi: 10.1038/s41598-018-35307-5.
- S. Ravi et al., “Bumblebees display characteristics of active vision during robust obstacle avoidance flight,” Journal of Experimental Biology, vol. 225, no. 4, Feb. 2022, doi: 10.1242/jeb.243021.
- P. Goyal, E. Baird, M. V. Srinivasan, and F. T. Muijres, “Visual guidance of honeybees approaching a vertical landing surface,” Journal of Experimental Biology, vol. 226, no. 17, Sep. 2023, doi: 10.1242/jeb.245956.
- H. Duan and P. Li, Bio-inspired Computation in Unmanned Aerial Vehicles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. doi: 10.1007/978-3-642-41196-0.
- Q. Yang and S.-J. Yoo, “Optimal UAV Path Planning: Sensing Data Acquisition Over IoT Sensor Networks Using Multi-Objective Bio-Inspired Algorithms,” IEEE Access, vol. 6, pp. 13671–13684, 2018, doi: 10.1109/ACCESS.2018.2812896.
- Y. ZHOU, Y. SU, A. XIE, and L. KONG, “A newly bio-inspired path planning algorithm for autonomous obstacle avoidance of UAV,” Chinese Journal of Aeronautics, vol. 34, no. 9, pp. 199–209, Sep. 2021, doi: 10.1016/J.CJA.2020.12.018.
- S. Na, H. Niu, B. Lennox, and F. Arvin, “Bio-Inspired Collision Avoidance in Swarm Systems via Deep Reinforcement Learning,” IEEE Trans Veh Technol, vol. 71, no. 3, pp. 2511–2526, Mar. 2022, doi: 10.1109/TVT.2022.3145346.
- Q. D. Hossain, M. N. Uddin, and Md. M. Hasan, “Collision avoidance technique using bio-mimic feedback control,” in 2014 International Conference on Informatics, Electronics & Vision (ICIEV), IEEE, May 2014, pp. 1–6. doi: 10.1109/ICIEV.2014.6850784.
- F. Aljalaud, H. Kurdi, and K. Youcef-Toumi, “Bio-Inspired Multi-UAV Path Planning Heuristics: A Review,” Mathematics, vol. 11, no. 10, p. 2356, May 2023, doi: 10.3390/math11102356.
- Wada Kentaro, “Labelme: Image Polygonal Annotation with Python.”
- A. H. Rorres, Elementary linear algebra : applications version. Wiley, 2000.
- M. U. S. MATLAB and Statistics Toolbox Release R2021a. Natick, “MathWorks, Inc.”