Koczka, Andre (Graz University of Technology), Wipfler, Hans-Peter (Graz University of Technology), Raunegger, Thomas (Graz University of Technology), Reiner, Markus (Graz University of Technology), Rendel, Jonathan (Halmstad University), Yeh, Puh-Yu (National Taipei University of Technology), Steinbauer-Wagner, Gerald (Graz University of Technology), Eder, Matthias (Graz University of Technology)

Enabling Multimodal Mobility for Urban Robot Navigation Using a Skill-Based Navigation Architecture

Scheduled for presentation during the Poster Session "Operation and navigation of automated vehicles" (FrAT16), Friday, September 27, 2024, 10:30−12:30, Foyer

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 14, 2024

Abstract

The use of different modes of transportation to traverse urban environments is a common technique for humans. For autonomous robots, this poses a variety of challenges, as their navigation architectures are typically designed to handle only one specific navigation task. To overcome this problem in urban navigation scenarios, we present a skill-based navigation architecture that is capable of switching between different navigation modes and utilizes different modes of transportation, including public transportation. By representing the urban environment as a skill graph, it is possible to draw from a set of skills for different navigation challenges, including sidewalk navigation, road crossing, and riding public transportation. Furthermore, this representation can be automatically extracted using geospatial data. With the proposed navigation architecture, the robot can efficiently traverse urban environments by autonomously walking to a public transportation station, waiting for the public transportation vehicle, riding it to the desired stop, and then walking to the desired destination. The proposed architecture is validated in a navigation scenario through the city of Graz, Austria on the legged robot platform ANYmal. By extracting a skill graph from OpenStreetMap data, the robot can autonomously plan and execute a route to its desired destination using public transportation.