Honda tests Autonomous Work Vehicle in Toronto
Honda has demonstrated its fully-electric prototype Autonomous Work Vehicle (AWV) at Toronto Pearson International Airport to test its use in airfield operations.
The demonstration tested its use in security perimeter fence inspections and was conducted in collaboration with the Greater Toronto Airports Authority along with technology leaders including Cisco Canada, Genwave Technologies, Illuminex AI and Eagle Aerospace.
For the Toronto demonstration, Honda showcased the AWV’s perimeter inspection capabilities leveraging its mapping and obstacle detection features, allowing it to navigate inspection routes and slow down or stop to avoid colliding with an obstacle.
The proof-of-concept perimeter fence inspection uses technologies from multiple tech companies, with Cisco Ultra Reliable Wireless Backhaul providing the wireless networking backbone for private connectivity to the airfield, Genwave Technologies designing, integrating and supporting wireless networks such as Cisco’s IoT Ultra Reliable Wireless Backhaul platform, Illuminex AI’s Operational AI System providing real-time assessments of airfield conditions and potential hazards, and Eagle Aerospace’s AIROps cloud-based software offering real-time automated logging of discrepancies during the inspection process.
Jason VanBuren, System Engineering Manager at American Honda Motor Co, said: “As we work toward commercialising the Honda AWV, we want to understand the needs of airfield operators and airport authorities to create new value by streamlining operations, enhancing safety performance and helping to meet airfield environmental sustainability goals.”
Honda is looking for other use cases such as hauling and transporting aircraft parts and equipment, mowing for vegetation control, working as a foreign object debris (FOD) tool and towing baggage carts and trailers to and from aircraft.
The AWV uses a camera for real-time monitoring and a suite of sensors to operate autonomously including GPS for location and radar and LiDAR for obstacle detection.