A Drift Diffusion Model of Biological Source Seeking for Mobile Robots

Ignacio Rano, Mehdi Khamassi, KongFatt Wong-Lin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)
8 Downloads (Pure)

Abstract

Braitenberg vehicles have been used in multiple real world robotic implementations of bio-inspired local navigation. While sensor based control strategies – including existing models of Braitenberg vehicles – typically neglect sensor noise, real robot implementations suffer from different levels of noise, especially in the case of low cost robots and highly stochastic environments. This paper presents a novel drift-diffusion model of Braitenberg vehicle 3a – a bio-inspired source seeking controller for non-holonomic robots – accounting for the sensor noise. The stochastic differential equations obtained provide means to accurately simulate the behaviour of this bio-inspired control mechanism. Although these equations do not have analytic solutions in general, under some simplifying assumptions, we obtain the deterministic equations for the average and dispersion of the vehicles while performing source seeking. Moreover, we found an analytic bound for the distribution of the heading direction of the robots. Simulations illustrate and confirm the theoretical results presented.
Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherIEEE
Pages3525-3531
Number of pages7
ISBN (Electronic)9781509046331
DOIs
Publication statusPublished - 21 Jul 2017
EventIEEE International Conference on Robotics and Automation - Singapore
Duration: 15 Jan 2017 → …

Conference

ConferenceIEEE International Conference on Robotics and Automation
Period15/01/17 → …

Keywords

  • Bio-inspired robotics
  • Drift-Diffusion Model.

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  • Cite this

    Rano, I., Khamassi, M., & Wong-Lin, K. (2017). A Drift Diffusion Model of Biological Source Seeking for Mobile Robots. In ICRA 2017 - IEEE International Conference on Robotics and Automation (pp. 3525-3531). [7989403] IEEE. https://doi.org/10.1109/ICRA.2017.7989403