Weakly electric fish are capable of performing obstacle avoidance in dark and complex aquatic environments efficiently using a navigation technique known as \emph{electrolocation}. That is, electric fish infer relevant information about surrounding obstacles from the perturbations that these obstacles impart to their self-generated electric field. This dissertation draws inspiration from electrolocation to demonstrate unmapped reflexive obstacle avoidance in underwater environments. The perturbation signal, called the \emph{electric image}, contains the spatial information of the perturbing objects regarding their location, size, conductivity etc. Electrostatic equations elucidate the concept of electrolocation and the mechanism of obstacle detection using electric field perturbations. Spatial decomposition of an electric image using Wide-Field Integration processing extracts relative proximity information about the obstacles. The electric field source is changed to an oscillatory one and a quasistatic approach is taken. Simulations were performed in straight tunnel, cluttered corridor and an obstacle field. Experimental validation was conducted with a setup comprising a tank, a computer-controlled gantry system and an electro-sensor. Consistency between the simulations and the experiments was maintained by recreating similar environments.

Simulations using both the electrostatic and the quasistatic approach demonstrate that the algorithm is capable of performing various maneuvers like tunnel centering, wall following and clutter navigation. The experimental results agree with the simulation results and validate the efficacy of the approach in performing obstacle avoidance. The presented approach is computationally lightweight and readily implementable, making underwater autonomous navigation in real-time feasible.