Improving wheelchair route planning through instrumentation and navigation systems

Area of Science:

• Computer Science
• Robotics
• Accessibility Engineering

Background:

• Current route planning tools inadequately address the needs of electric-powered wheelchair (EPW) users.
• Accessibility and battery consumption are critical factors for EPW users during navigation.

Purpose of the Study:

• To develop an accessible route navigation application (eNav) that minimizes EPW battery consumption.
• To introduce a novel robotic system (MEBot) for enhancing route accessibility data.

Main Methods:

• eNav integrates data from OpenStreetMaps (OSM), airborne laser scanner (ALS), Points-of-Interest (POIs), public transportation, and crowdsourcing.
• The Mobility Enhancement roBot (MEBot), a legged-wheeled wheelchair, collects real-world accessibility data.
• MEBot's data serves as a sixth layer for eNav and informs road authorities.

Main Results:

• eNav provides optimized routes considering shortest path, accessibility, comfort, and minimal EPW battery usage.
• MEBot can navigate challenging terrains, overcoming architectural barriers.
• The integration of MEBot data improves route planning accuracy and informs infrastructure improvements.

Conclusions:

• eNav offers a significant advancement in accessible navigation for EPW users by prioritizing battery efficiency and accessibility.
• MEBot provides valuable real-world data to enhance digital accessibility maps and inform urban planning.
• The combined approach promises to improve mobility and independence for individuals using electric-powered wheelchairs.