A discrete dipole approximation approach to underwater active electrosense problems

Weakly electric fish use self-established electric field to sense the underwater environment that may be cluttered and turbid. Previous works on building artificial counterparts are limited to simplest cases, as no analytical solutions exist under complex boundary conditions. Universal numerical approaches like Finite Element Method (FEM) and Boundary Element Method (BEM) suffer from lengthy meshing process and heavily computational burden. In this paper, discrete dipole approximation (DDA), which is widely used in light scattering and absorption problems, was for the first time proposed to be applied for underwater electrosense. This approach is lightweight, flexible and computationally efficient compared with FEM. It was simulated in electric fields excited by parallel-plate electrodes and spherical electrodes of a simplified robotic model. A constrained unscented Kalman filter (CUKF) was further utilized to localize the position and identify the size of an invading cube. Results comparison with FEM indicate the differences of a cuboidal object in two orthogonal positions were 7.10% and 10.46% respectively, and the difference in size was 11.82%. These results were achieved at a cost of less than 1% of the computational effort of the FEM. The proposed approach proved effective from the simulation results and laid a solid foundation for real-time underwater active electrosense in a more general environment.

[1]  Josselin Garnier,et al.  Shape recognition and classification in electro-sensing , 2014, Proceedings of the National Academy of Sciences.

[2]  M. A. Yurkina,et al.  The discrete dipole approximation : An overview and recent developments , 2007 .

[3]  G. von der Emde,et al.  Non-visual environmental imaging and object detection through active electrolocation in weakly electric fish. , 2006, Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology.

[4]  Michael A. Peshkin,et al.  Finding and identifying simple objects underwater with active electrosense , 2015, Int. J. Robotics Res..

[5]  Michael A. Peshkin,et al.  Sensing capacitance of underwater objects in bio-inspired electrosense , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[7]  William R B Lionheart,et al.  Uses and abuses of EIDORS: an extensible software base for EIT , 2006, Physiological measurement.

[8]  D. A. Dunnett Classical Electrodynamics , 2020, Nature.

[9]  Kevin M. Lynch,et al.  Active Electrolocation for Underwater Target Localization , 2008, Int. J. Robotics Res..

[10]  K. E. Machin,et al.  The Mechanism of Object Location in Gymnarchus Niloticus and Similar Fish , 1958 .

[11]  E. Purcell,et al.  Scattering and Absorption of Light by Nonspherical Dielectric Grains , 1973 .

[12]  David S. Holder,et al.  Electrical Impedance Tomography : Methods, History and Applications , 2004 .

[13]  Andy Adler,et al.  FEM electrode refinement for electrical impedance tomography , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  R. Fox,et al.  Classical Electrodynamics, 3rd ed. , 1999 .

[15]  Christopher Assad,et al.  Electric field maps and boundary element simulations of electrolocation in weakly electric fish , 1997 .

[16]  Frédéric Boyer,et al.  Model for a Sensor Inspired by Electric Fish , 2012, IEEE Transactions on Robotics.

[17]  Raul Gonzalez Lima,et al.  Electrical impedance tomography using the extended Kalman filter , 2004, IEEE Transactions on Biomedical Engineering.

[18]  Robert M. Fano,et al.  Electromagnetic Fields, Energy, and Forces , 1968 .

[19]  Christine Chevallereau,et al.  Underwater robot navigation around a sphere using electrolocation sense and Kalman filter , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  D. Simon Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[21]  B. Rasnow,et al.  The effects of simple objects on the electric field of Apteronotus , 1996, Journal of Comparative Physiology A.

[22]  Frédéric Boyer,et al.  Sensor model for the navigation of underwater vehicles by the electric sense , 2010, 2010 IEEE International Conference on Robotics and Biomimetics.

[23]  James Snyder Underwater object tracking using electrosense , 2013 .

[24]  Leslie Greengard,et al.  A fast algorithm for particle simulations , 1987 .

[25]  Frédéric Boyer,et al.  Exploration of Objects by an Underwater Robot with Electric Sense , 2012, Living Machines.

[26]  David Stroud,et al.  The effective medium approximations : Some recent developments , 1998 .