A neural network model that calculates dynamic distance transform for path planning and exploration in a changing environment

In this paper, we present a neural network model that realizes a dynamic version of the distance transform algorithm (used for path planning in a stationary domain). The novel version is capable of performing path generation for highly dynamic environments. The neural network has discrete-time dynamics, is locally connected, and, hence, computationally efficient. No preliminary information about the world status is required for the planning process. Path generation is performed via the neural-activity landscape, which forms a dynamically-updating potential field over a distributed representation of the configuration space of a robot. The network dynamics guarantees local adaptations and includes a set of strict rules for determining the next step in the path for a robot. According to these rules, planned paths tend to be optimal in a L₁ metric. Simulation results in a series of experiments for various dynamical situations prove the effectiveness of the proposed model.