Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 186 tok/s
Gemini 2.5 Pro 55 tok/s Pro
GPT-5 Medium 36 tok/s Pro
GPT-5 High 41 tok/s Pro
GPT-4o 124 tok/s Pro
Kimi K2 184 tok/s Pro
GPT OSS 120B 440 tok/s Pro
Claude Sonnet 4.5 35 tok/s Pro
2000 character limit reached

Visually Guided Swarm Motion Coordination via Insect-inspired Small Target Motion Reactions (2405.04591v1)

Published 7 May 2024 in eess.SY and cs.SY

Abstract: Despite progress developing experimentally-consistent models of insect in-flight sensing and feedback for individual agents, a lack of systematic understanding of the multi-agent and group performance of the resulting bio-inspired sensing and feedback approaches remains a barrier to robotic swarm implementations. This study introduces the small-target motion reactive (STMR) swarming approach by designing a concise engineering model of the small target motion detector (STMD) neurons found in insect lobula complexes. The STMD neuron model identifies the bearing angle at which peak optic flow magnitude occurs, and this angle is used to design an output feedback switched control system. A theoretical stability analysis provides bi-agent stability and state boundedness in group contexts. The approach is simulated and implemented on ground vehicles for validation and behavioral studies. The results indicate despite having the lowest connectivity of contemporary approaches (each agent instantaneously regards only a single neighbor), collective group motion can be achieved. STMR group level metric analysis also highlights continuously varying polarization and decreasing heading variance.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (57)
  1. B. Lingenfelter, A. Nag, and F. van Breugel, “Insect inspired vision-based velocity estimation through spatial pooling of optic flow during linear motion,” Bioinspiration & biomimetics, vol. 16, no. 6, p. 066004, 2021.
  2. Z. M. Bagheri, B. S. Cazzolato, S. Grainger, D. C. O’Carroll, and S. D. Wiederman, “An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments,” Journal of neural engineering, vol. 14, no. 4, p. 046030, 2017.
  3. Z. M. Bagheri, S. D. Wiederman, B. S. Cazzolato, S. Grainger, and D. C. O’Carroll, “Performance of an insect-inspired target tracker in natural conditions,” Bioinspiration & biomimetics, vol. 12, no. 2, p. 025006, 2017.
  4. S. Wiedermann and D. C. O’Carroll, “Biologically inspired feature detection using cascaded correlations of off and on channels,” Journal of Artificial Intelligence and Soft Computing Research, vol. 3, 2013.
  5. H. Wang, H. Wang, J. Zhao, C. Hu, J. Peng, and S. Yue, “A time-delay feedback neural network for discriminating small, fast-moving targets in complex dynamic environments,” IEEE Transactions on Neural Networks and Learning Systems, 2021.
  6. J. S. Humbert, R. M. Murray, and M. H. Dickinson, “Sensorimotor convergence in visual navigation and flight control systems,” IFAC Proceedings Volumes, vol. 38, no. 1, pp. 253–258, 2005.
  7. M. A. Billah and I. A. Faruque, “Bioinspired visuomotor feedback in a multiagent group/swarm context,” IEEE Transactions on Robotics, vol. 37, no. 2, pp. 603–614, 2021.
  8. M. A. Billah and I. Faruque, “The multi-agent group motions generated by models of insect small target detector neurons and feedback,” in AIAA SCITECH 2022 Forum, 2022, p. 0962.
  9. M. A. Billah and I. A. Faruque, “Modeling small-target motion detector neurons as switched systems with dwell time constraints,” in 2022 American Control Conference (ACC).   IEEE, 2022, pp. 3192–3197.
  10. D. O’Carroll, “Feature-detecting neurons in dragonflies,” Nature, vol. 362, no. 6420, pp. 541–543, 1993.
  11. K. Nordström, P. D. Barnett, and D. C. O’Carroll, “Insect detection of small targets moving in visual clutter,” PLoS biology, vol. 4, no. 3, p. e54, 2006.
  12. D. C. O’Carroll and S. D. Wiederman, “Contrast sensitivity and the detection of moving patterns and features,” Philosophical Transactions of the Royal Society B: Biological Sciences, vol. 369, no. 1636, p. 20130043, 2014.
  13. S. D. Wiederman and D. C. O’Carroll, “Discrimination of features in natural scenes by a dragonfly neuron,” Journal of Neuroscience, vol. 31, no. 19, pp. 7141–7144, 2011.
  14. S. D. Wiederman and D. C. O’Carroll, “Selective attention in an insect visual neuron,” Current Biology, vol. 23, no. 2, pp. 156–161, 2013.
  15. S. D. Wiederman, J. M. Fabian, J. R. Dunbier, and D. C. O’Carroll, “A predictive focus of gain modulation encodes target trajectories in insect vision,” Elife, vol. 6, p. e26478, 2017.
  16. B. R. Geurten, K. Nordström, J. D. Sprayberry, D. M. Bolzon, and D. C. O’Carroll, “Neural mechanisms underlying target detection in a dragonfly centrifugal neuron,” Journal of Experimental Biology, vol. 210, no. 18, pp. 3277–3284, 2007.
  17. B. J. E. Evans, D. C. O’Carroll, J. M. Fabian, and S. D. Wiederman, “Dragonfly neurons selectively attend to targets within natural scenes,” Frontiers in Cellular Neuroscience, p. 151, 2022.
  18. J. M. Fabian, J. R. Dunbier, D. C. O’Carroll, and S. D. Wiederman, “Properties of predictive gain modulation in a dragonfly visual neuron,” Journal of Experimental Biology, vol. 222, no. 17, p. jeb207316, 2019.
  19. B. H. Lancer, B. J. Evans, J. M. Fabian, D. C. O’Carroll, and S. D. Wiederman, “Preattentive facilitation of target trajectories in a dragonfly visual neuron,” Communications biology, vol. 5, no. 1, pp. 1–13, 2022.
  20. B. H. Lancer, B. J. Evans, J. M. Fabian, D. C. O’Carroll, and S. D. Wiederman, “A target-detecting visual neuron in the dragonfly locks on to selectively attended targets,” Journal of Neuroscience, vol. 39, no. 43, pp. 8497–8509, 2019.
  21. H. G. Krapp, “Optic flow processing,” in Encyclopedia of Computational Neuroscience.   Springer, 2022, pp. 2539–2558.
  22. S. D. Wiederman, P. A. Shoemaker, and D. C. O’Carroll, “A model for the detection of moving targets in visual clutter inspired by insect physiology,” PloS one, vol. 3, no. 7, p. e2784, 2008.
  23. H. Wang, J. Peng, and S. Yue, “A directionally selective small target motion detecting visual neural network in cluttered backgrounds,” IEEE transactions on cybernetics, vol. 50, no. 4, pp. 1541–1555, 2018.
  24. H. Wang, J. Peng, X. Zheng, and S. Yue, “A robust visual system for small target motion detection against cluttered moving backgrounds,” IEEE transactions on neural networks and learning systems, vol. 31, no. 3, pp. 839–853, 2019.
  25. L. Varennes, H. G. Krapp, and S. Viollet, “Two pursuit strategies for a single sensorimotor control task in blowfly,” Scientific reports, vol. 10, no. 1, pp. 1–13, 2020.
  26. R. Strydom and M. V. Srinivasan, “Uas stealth: target pursuit at constant distance using a bio-inspired motion camouflage guidance law,” Bioinspiration & biomimetics, vol. 12, no. 5, p. 055002, 2017.
  27. M. Mischiati, H.-T. Lin, P. Herold, E. Imler, R. Olberg, and A. Leonardo, “Internal models direct dragonfly interception steering,” Nature, vol. 517, no. 7534, pp. 333–338, 2015.
  28. M. V. Srinivasan, “Visual control of navigation in insects and its relevance for robotics,” Current opinion in neurobiology, vol. 21, no. 4, pp. 535–543, 2011.
  29. E. W. Justh and P. Krishnaprasad, “Steering laws for motion camouflage,” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol. 462, no. 2076, pp. 3629–3643, 2006.
  30. L. Peppas, “D. proportional navigation and command to line of sight of a command guided missile for a point defence system,” Ph.D. dissertation, Ph. D. thesis, Naval postgraduate school Monterey, California, 1992.
  31. V. Raju and P. Krishnaprasad, “Motion camouflage in the presence of sensory noise and delay,” in 2016 IEEE 55th Conference on Decision and Control (CDC).   IEEE, 2016, pp. 2846–2852.
  32. J. V. Huang, Y. Wei, and H. G. Krapp, “A biohybrid fly-robot interface system that performs active collision avoidance,” Bioinspiration & Biomimetics, vol. 14, no. 6, p. 065001, 2019.
  33. F. Colonnier, S. Ramirez-Martinez, S. Viollet, and F. Ruffier, “A bio-inspired sighted robot chases like a hoverfly,” Bioinspiration & biomimetics, vol. 14, no. 3, p. 036002, 2019.
  34. X. Wu, K. Zhang, and M. Cheng, “Optimal control of constrained switched systems and application to electrical vehicle energy management,” Nonlinear Analysis: Hybrid Systems, vol. 30, pp. 171–188, 2018.
  35. O. Namaki-Shoushtari, A. Pedro Aguiar, and A. Khaki-Sedigh, “Target tracking of autonomous robotic vehicles using range-only measurements: a switched logic-based control strategy,” International Journal of Robust and Nonlinear Control, vol. 22, no. 17, pp. 1983–1998, 2012.
  36. L. R. G. Carrillo, G. R. F. Colunga, G. Sanahuja, and R. Lozano, “Quad rotorcraft switching control: An application for the task of path following,” IEEE Transactions on Control Systems Technology, vol. 22, no. 4, pp. 1255–1267, 2013.
  37. K. S. Narendra and J. Balakrishnan, “A common lyapunov function for stable lti systems with commuting a-matrices,” IEEE Transactions on automatic control, vol. 39, no. 12, pp. 2469–2471, 1994.
  38. J. L. Mancilla-Aguilar and H. Haimovich, “Uniform input-to-state stability for switched and time-varying impulsive systems,” IEEE Transactions on Automatic Control, vol. 65, no. 12, pp. 5028–5042, 2020.
  39. J. P. Hespanha and A. S. Morse, “Stability of switched systems with average dwell-time,” in Proceedings of the 38th IEEE conference on decision and control (Cat. No. 99CH36304), vol. 3.   IEEE, 1999, pp. 2655–2660.
  40. L. Vu, D. Chatterjee, and D. Liberzon, “Input-to-state stability of switched systems and switching adaptive control,” Automatica, vol. 43, no. 4, pp. 639–646, 2007.
  41. G. Chen and Y. Yang, “Relaxed conditions for the input-to-state stability of switched nonlinear time-varying systems,” IEEE Transactions on Automatic Control, vol. 62, no. 9, pp. 4706–4712, 2016.
  42. T. Alpcan and T. Basar, “A stability result for switched systems with multiple equilibria,” Dynamics of Continuous, Discrete and Impulsive Systems Series A: Mathematical Analysis, vol. 17, no. 4, pp. 949–958, 2010.
  43. M. Dorothy and S.-J. Chung, “Switched systems with multiple invariant sets,” Systems & Control Letters, vol. 96, pp. 103–109, 2016.
  44. S. Veer and I. Poulakakis, “Switched systems with multiple equilibria under disturbances: Boundedness and practical stability,” IEEE Transactions on Automatic Control, vol. 65, no. 6, pp. 2371–2386, 2019.
  45. J. Humbert, “Bio-inspired visuomotor convergence in navigation and flight control systems,” Doctoral dissertation, California Institute of Technology, 2005.
  46. R. Amsters and P. Slaets, “Turtlebot 3 as a robotics education platform,” in Robotics in Education: Current Research and Innovations 10.   Springer, 2020, pp. 170–181.
  47. M. A. Billah and I. A. Faruque, “Robustness in bio-inspired visually guided multi-agent flight and the gain modulation hypothesis,” International Journal of Robust and Nonlinear Control, vol. 33, no. 2, pp. 1316–1334, 2023.
  48. T. Vicsek, A. Czirok, E. Ben-Jacob, I. Cohen, and O. Schochet, “Novel Type of Phase Transition In a System of Self-Driven Particles,” Physical Review Letters, vol. 75, no. 4, pp. 729–732, 1995.
  49. F. Cucker and S. Smale, “Emergent Behavior in Flocks,” IEEE Transactions on Automatic Control, vol. 52, no. May, pp. 1–20, 2007.
  50. D. Armbruster, S. Martin, and A. Thatcher, “Elastic and inelastic collisions of swarms,” Physica D: Nonlinear Phenomena, vol. 344, pp. 45–57, 2017.
  51. V. Nityananda, “Attention-like processes in insects,” Proceedings of the Royal Society B: Biological Sciences, vol. 283, no. 1842, p. 20161986, 2016.
  52. J. Hindes, V. Edwards, M. A. Hsieh, and I. B. Schwartz, “Critical transition for colliding swarms,” Physical Review E, vol. 103, no. 6, p. 062602, 2021.
  53. K. M. Schultz, K. M. Passino, and T. D. Seeley, “The mechanism of flight guidance in honeybee swarms: subtle guides or streaker bees?” Journal of Experimental Biology, vol. 211, no. 20, pp. 3287–3295, 2008.
  54. K. Nordström and D. C. O’Carroll, “Small object detection neurons in female hoverflies,” Proceedings of the Royal Society B: Biological Sciences, vol. 273, no. 1591, pp. 1211–1216, 2006.
  55. M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini et al., “Interaction ruling animal collective behavior depends on topological rather than metric distance: Evidence from a field study,” Proceedings of the national academy of sciences, vol. 105, no. 4, pp. 1232–1237, 2008.
  56. S. Islam and I. A. Faruque, “Metric, topological, and temporal elements of tracking and deliberative decision-making in flying insect groups,” in review.
  57. S. Koenig, R. Wolf, and M. Heisenberg, “Vision in flies: measuring the attention span,” PLoS One, vol. 11, no. 2, p. e0148208, 2016.
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
Dice Question Streamline Icon: https://streamlinehq.com

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Generate Now
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

User Circle Single Streamline Icon: https://streamlinehq.com Sign Up
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets

This paper has been mentioned in 1 tweet and received 0 likes.

Upgrade to Pro to view all of the tweets about this paper:

Start a free 7-day Pro trial