Networks of Place Cells for Representing 3D Environments and Path Planning

Conventional methods for motion control and path planning in robots are nowhere near as reactive and flexible as in nature. Brains solve navigation using place cells - neurons that provide a cognitive representation of a specific environment. Neural techniques for path planning in 2D have been developed for years, however, to allow their application for robotic tasks beyond locomotion, a transmission to 3D is required. We present an implementation for path planning via a propagating wavefront on 3D environments. The algorithm operates on a Spiking Neural Network of excitatory place cells structured as a grid. A wavefront travelling through the network is initiated by activating the goal place cell. The wave strengthens synapses in the direction of the propagation using STDP, as a synaptic learning rule. By interpreting the synaptic weights as a vector field, a path can be derived from any place cell, reached by the wave, to the destination. We demonstrate, using a neural simulator, that our algorithm works well on maps with multiple obstacles. Our method allows fast simulation and query times and we expect to considerably improve the network creation time by using dedicated hardware, allowing massive parallelism. Our algorithm applies bio-inspired techniques and is especially interesting for Human-robot interaction, which requires reactive flexible motion planning.