Enhancing LocoMobi Mobi 1: A Comprehensive Study and Optimization of Robot Navigation and Safety – Phase 2

Funder: NSERC
Program: ARD
PI Name: Imran Khan
Faculty/Department: Faculty of Applied Sciences & Technology
Research Area(s): Other

This project aims to revolutionize Mobi, LocoMobi World Inc’s (LocoMobi) existing operational parking robot,
into the cornerstone of a fully automated, secure, and efficient parking operation. As automation continues to
reshape parking infrastructures globally, the demand for autonomous mobile parking enforcement robots has
surged. While Canada currently lacks such technology, the worldwide landscape is witnessing a rapid adoption
of automated solutions to address parking challenges. In an article for the Canadian Parking Association,
Shawn Walker (2016) states that the parking sector will be significantly impacted by robotics, artificial
intelligence, and autonomous vehicles in the future. LocoMobi, a Smart City technology small and mediumsized
enterprise (SME) headquartered in Mississauga, Ontario, is at the forefront of this transformation.
Specializing in diverse and innovative enterprise hardware and cloud-based software offerings for parking,
tolling, transit, storage, asset tracking, and threat management sectors, LocoMobi customizes solutions tailored
to specific Smart City needs. Recently, LocoMobi and Humber completed the first phase small-scale test bench
project of autonomous vehicles with 3D LiDAR mapping made possible by Greenfield Research and Innovation
Funds supported by Humber’s NSERC Mobilize grant. By harnessing advanced technologies, this collaborative
effort promises to elevate the user experience and set new standards in parking automation. The engagement
and cooperation between LocoMobi and Humber College are crucial in realizing a parking solution that
seamlessly integrates innovation, security, and efficiency. Toward the project's culmination, the objective is to
deliver a Minimum Viable Product (MVP) that Locomobi can leverage for commercialization. The MVP should
achieve the following requirements: 1) a Fully Autonomous drive vehicle with manual remote control; 2)
Mapping and navigation by avoiding object features using current edge technologies; 3) Light and sound
systems to show or communicate various scenarios during operation, and 4) Take a picture of the license plate
and process it from a safe distance.