Autonomous Motion Planning for a Safe and Efficient Last Mile Delivery Robot

In recent years, the North American population has become increasingly dependent on food and consumer product delivery. As a result of the current COVID-19 pandemic there have been surges in delivery demand. There are several active driving-based delivery methods, such as Uber Eats, however drivers are required to navigate through traffic, park, turn off their vehicle, exit and walk to the customer doorstep to drop products off. This cumbersome and inefficient final step of the service is known as the last-mile delivery problem. The last mile is time consuming, expensive, and environmentally unfriendly, especially in densely populated cities. Tinymile.ai is a company developing tele-operated wheeled mobile robots (WMR) to address the last-mile delivery problem and perform contact-less delivery amidst the current pandemic. These robots are semi-autonomous as operators control their movements remotely. The research objective is to develop an optimal, controlled motion planning approach to enhance functionality and controllability of WMRs when performing deliveries.

Faculty Supervisor:

Jonathan Kelly

Student:

Ioakeim Norihisa Kaltsidis

Partner:

Tinymile.ai

Discipline:

Engineering - mechanical

Sector:

Transportation and warehousing

University:

University of Toronto