Brain inspired path planning algorithms for drones

With the development of artificial intelligence and brain science, brain-inspired navigation and path planning has attracted widespread attention.

In this paper, we present a place cell based path planning algorithm that utilizes spiking neural network (SNN) to create efficient routes for drones. First, place cells are characterized by the leaky integrate-and-fire (LIF) neuron model. Then, the connection weights between neurons are trained by spike-timing-dependent plasticity (STDP) learning rules. Afterwards, a synaptic vector field is created to avoid obstacles and to find the shortest path.

Finally, simulation experiments both in a Python simulation environment and in an Unreal Engine environment are conducted to evaluate the validity of the algorithms.

Experiment results demonstrate the validity, its robustness and the computational speed of the proposed model.