Work

UP3: Unsupervised Predictive Path Planner for Mobile Robots in Unknown Environment

Year: 2025

Type: article

Abstract: In this article, UP3, a novel unsupervised learning‐based predictive path planning framework for mobile robot navigation in partially observable environments is proposed. An offline unsupervised learning method is used for planner training to avoid frequent environment interactions or expert demonstration. The network employs attention‐based path optimization to generate the predictive path. Herein, the robot's safety is considered as a hard constraint, and the control barrier function constraint is used to design the collision loss. Additionally, a deep constraint correction module is designed to correct the predictive path, which aims to make the path satisfy the constraints. Herein, the approach through simulation and real‐world experiments is tested to verify the function of each module. In the results, it is indicated that UP3 performs well on dense and maze scenarios and has good generalization.

Source: Advanced Intelligent Systems

DOI: 10.1002/aisy.202400916

Authors: Jianing Luo, Jiwei Zhu, Ruopeng Cui, Gaoxiong Lu, Wei Li

Institution: Fudan University

Language: English

Topic: Robotic Path Planning Algorithms

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,970