In warehouses, optimally assigning tasks to multiple robots is a critical challenge, as it is an NP-hard problem. This project tackles this Multi-Robot Task Allocation (MRTA) problem using a modified Particle Swarm Optimization (PSO) technique. We optimize task scheduling while considering factors like makespan, charge availability, and load distribution across mobile robots.
Shared mobility technology has evolved significantly, leading to the widespread adoption of various shared transportation services. These include car sharing, bike sharing, scooter sharing, ridesharing, ridehailing, ridesourcing, and demand-responsive transit (DRT). In this project, we focus on tackling the car-sharing service problem using various bio-inspired algorithms.
Understanding urban mobility requires analyzing how traffic flows across a city over time. Using data from Toronto Open Data, this project explores traffic volume distributions in Toronto from 2010 to 2019. Using different geospatial analysis techniques, we aim to uncover patterns in Toronto’s traffic flow, identify congestion hotspots, and gain insights into urban mobility trends.
A complete list of packages required in this project is included in requirements.txt. For detailed setup information, please refer to this excellent repository by Dr Alaa Khamis: SmartMobilityAlgoithms/GettingStarted.
@misc{Khamis2022,
title = {ECE1724H: Bio-inspired Algorithms for Smart Mobility. University of Toronto, Course instructor: Dr. Alaa Khamis},
year = {2022},
howpublished = {\url{https://smartmobilityalgorithms.github.io/book/index.html}}
}