Active sensing animals may inspire the development of new technologies that mimic their sensing behavior.
Electric fish, for instance, orient themselves at night in complete darkness by using their active electro-sensing system. They generate a stable, relatively high-frequency, weak electric field and perceive the transdermal potential modulations caused by nearby targets with different electromagnetic properties than the surrounding water. Since they have an electric sense that allows underwater navigation, target classification and intraspecific communication, they are privileged animals for bio-inspiring man-built autonomous systems.
Bats, on the other hand, process the reflected echoes due to the presence of acoustic inclusions for echolocation. In general, they use acoustic waves for most of the perceptual tasks, that range from hunting to navigating.
This thesis introduces premier algorithms in electro-sensing and echo-sensing.
The weakly electric fish is able to retrieve much more information about the target by approaching it. To mimic this behavior, an innovative (real-time) multi-scale method for target classification in electro-sensing is presented. The method is based on a family of transform-invariant shape descriptors computed from generalized polarization tensors (GPTs) reconstructed at multiple scales. The evidence provided by the different descriptors at each scale is fused using Dempster-Shafer Theory. Numerical simulations show that the recognition algorithm we proposed performs undoubtedly well and yields a robust classification.
For real-world applications, inhomogeneous targets have to be identified. The shape descriptor-based classification algorithm is extended in order to consider inhomogenous material parameters.
The approach is based on new invariants for the contracted generalized polarization tensors associated with inhomogeneous objects. The numerical simulations show that by comparing these invariants with those in a dictionary of precomputed homogeneous and inhomogeneous targets, one can successfully classify the inhomogeneous target.
Another problem concerns intraspecific electro-communication for weakly electric fish. In particular, a description on how the fish circumvent the jamming issue for both electro-communication and active electro-sensing is presented.
The main result is a real-time tracking algorithm, which provides a new approach to the communication problem. It finds a natural application in robotics, where efficient communication strategies are needed to be implemented by bio-inspired underwater robots.
The concept of time-dependent polarization tensors (TDPTs) for the wave equation associated to a diametrically small acoustic inclusion, with constitutive parameters different from those of the background and size smaller than the operating wavelength, is used to mimic the echo-sensing capabilities of a static bat. Firstly, the solution to the Helmholtz equation is considered, and a rigorous systematic derivation of a complete asymptotic expansion of the scattered field due to the presence of the inclusion is presented. Then, by applying the Fourier transform, the corresponding time-domain expansion is readily obtained after truncating the high frequencies. The new concept of TDPTs is shown to be promising for performing imaging. Numerical simulations are presented, showing that the TDPTs reconstructed from noisy measurements allow to image fine shape details of the inclusion
