Work

Probability propagation for path planning in unknown environments

Year: 2025

Type: article

Abstract: We propose a probability propagation framework for path planning on discrete grids where an agent can navigate in an unknown environment to discover new areas and goals. We introduce a technique in which the probabilistic backward flow provides guidance towards discovering multiple distributed goals and hidden regions. This is achieved using a maximum likelihood path estimation framework in which the hidden areas become constrained goals that “attract” the agent. Simulations on various grids are included in the paper. The results show how this idea, applied to a completely unknown environment and goal position, may provide a unifying and powerful method for distributed dynamic path planning.

Source: Intelligent Systems with Applications

DOI: 10.1016/j.iswa.2025.200527

Authors: Giovanni Di Gennaro, Amedeo Buonanno, Giovanni Fioretti, Francesco Verolla, Francesco A.N. Palmieri, Krishna R. Pattipati

Institutions: National Agency for New Technologies, Energy and Sustainable Economic Development, University of Campania "Luigi Vanvitelli", University of Connecticut

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,790

Funders: Regione Campania, Consorzio Nazionale Interuniversitario per le Telecomunicazioni, Office of Naval Research, U.S. Naval Research Laboratory