Work

Safe Robust Framework for Reinforcement Learning-based Control of Indoor Vehicles

Year: 2025

Type: article

Abstract: The paper presents the design of a safe data-aided steering control for indoor vehicles using the robust supervisory framework. The goal of the method is to achieve the combination of the effective motion with reinforcement learning (RL) based control and the guaranteed safe motion with robust control. The RL-based control through the Proximal Policy Optimization (PPO) method is designed in which actor and critic agents are used. The supervisory robust control is selected in the form with which robust stability against an additional input disturbance can be guaranteed. The effectiveness of the combination through simulations and experimental test scenarios is illustrated. For test purposes, an F1TENTH type small-scaled test vehicle is used, whose lap time is minimized through the proposed control system.

Source: Periodica Polytechnica Transportation Engineering

DOI: 10.3311/pptr.23455

Authors: Attila Lelkó, Balázs Németh

Institutions: Budapest University of Technology and Economics, HUN-REN Institute for Computer Science and Control, Hungarian Research Network

Language: English

Topic: Reinforcement Learning in Robotics

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: European Commission, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap, National Research, Development and Innovation Office