Context-based navigation for ground mobile robot in a semi-structured indoor environment

TL;DR

This paper introduces a safety prediction model for ground mobile robots that adapts navigation configurations in semi-structured indoor environments, enhancing safety and performance through self-adaptation.

Contribution

It presents a novel safety model for local planning that enables real-time adaptation based on environment perception, improving robot navigation safety.

Findings

Safety model accurately predicts navigation safety in retail scenarios

Self-adaptation framework effectively adjusts configurations at run-time

Validated models improve navigation safety and efficiency

Abstract

There is a growing demand for mobile robots to operate in more variable environments, where guaranteeing safe robot navigation is a priority, in addition to time performance. To achieve this, current solutions for local planning use a specific configuration tuned to the characteristics of the application environment. In this paper, we present an approach for developing quality models that can be used by a self-adaptation framework to adapt the local planner configuration at run-time based on the perceived environment. We contribute a definition of a safety model that predicts the safety of a navigation configuration given the perceived environment. Experiments have been performed in a realistic navigation scenario for a retail application to validate the obtained models and demonstrate their integration in a self-adaptation framework.