Summary Paper: Use Case on Building Collaborative Safe Autonomous Systems-A Robotdog for Guiding Visually Impaired People

TL;DR

This work tackles the safety of robot-assisted guidance for visually impaired people navigating dynamic environments such as smart intersections. It proposes a distributed, collaborative framework with a Robotdog as the decision master and other autonomous agents, organized into a three-layer architecture (Sensing and Networking, Decision, Actuation) that shares environment data and trust signals to support safe crossing decisions. A lightweight, trust-based data aggregation mechanism ranks contributions from different nodes based on sensor quality, enabling timely, reliability-driven decisions without full sensor fusion. The approach aims to improve safety and autonomy for visually impaired users and offers a scalable blueprint for collaborative autonomy in public urban settings.

Abstract

This is a summary paper of a use case of a Robotdog dedicated to guide visually impaired people in complex environment like a smart intersection. In such scenarios, the Robotdog has to autonomously decide whether it is safe to cross the intersection or not in order to further guide the human. We leverage data sharing and collaboration between the Robotdog and other autonomous systems operating in the same environment. We propose a system architecture for autonomous systems through a separation of a collaborative decision layer, to enable collective decision making processes, where data about the environment, relevant to the Robotdog decision, together with evidences for trustworthiness about other systems and the environment are shared.

Figures (1)

• Figure 1: Sketch of the Robotdog operating in a smart intersection. Layers of system architecture for enabling collaborative decision making are depicted. A full demo can be found on the following link: https://tinyurl.com/tudrobotdog