Smart wheelchair navigates safely with limited and faulty user commands
A Multi-Layer Navigation System for Smart Wheelchairs with a Limited Input Interface
Robotics
Summary
Smart wheelchairs help people move around safely but can struggle if the user’s commands are unclear or if the environment is new. The authors created a new navigation system that can understand limited or wrong user commands and safely guide the wheelchair through unknown places. Their system combines sensors, smart planning, and vision techniques to detect obstacles, plan paths, and identify targets. They tested it on a real wheelchair with 10 people and showed it works well even when commands are imperfect.
What this means in practice
- •For assistive device engineers: Build smart wheelchairs that can safely navigate unknown spaces using limited or faulty user input.
- •For robotics product developers: Integrate multi-layer navigation with virtual sensors and vision algorithms to improve robustness of mobility aids.$Commercial implications: Enables commercial development of more reliable smart wheelchairs that better interpret user intent in real settings.
Authors
Piero Simonetto, Anna Polato, Francesco Pasti, Muhammad Rameez ur Rahman, Sebastiano Vascon, Nicola Bellotto, Emanuele Menegatti, Stefano Tortora, Luca Tonin
Abstract
Smart wheelchairs are robotic devices that gather information from the surroundings and allow people to safely drive through various environments. They usually rely on the interpretation of high-level user's intentions, typically decoded through human-robot interface (HRI) or brain-machine interface (BMI). However, navigating in an unknown environment and accurately understanding user commands remain significant challenges. Misinterpretation of commands or the environment can lead to user frustration, distrust of the system, or even unsafe situations. In this paper, we present a novel Multi-Layer Navigation System (MLNS) for a smart wheelchair that enables navigation in unknown environments while interpreting and safely contextualizing user's intentions even in the case of limited and faulty commands. The stack relies on a Virtual Sensors-Driven Potential Fields (ViSPF) method for obstacle detection that accounts for the wheelchair's actual size, a Model Predictive Control (MPC) module for trajectory generation, and an open-vocabulary computer vision algorithm for target identification. Finally, a system based on Priority Queue (PQ) was implemented for target selection. We validated our system on a real smart wheelchair with experiments involving 10 participants without disabilities, including scenarios with faulty input interfaces. The results demonstrate the robustness and effectiveness of our system in real-world environments, proving its potential for practical applications in assistive technologies.