Yang, Yufeng (University of Waterloo), Ning, Minghao (University of Waterloo), HUANG, SHUCHENG (University of Waterloo), Hashemi, Ehsan (University of Alberta), Khajepour, Amir (University of Waterloo)

Intelligent Mobility System with Integrated Motion Planning and Control Utilizing Infrastructure Sensor Nodes

Scheduled for presentation during the Invited Session "Trustworthy Diagnosis and Prognosis in Connected, Cooperative and Automated Mobility" (WeBT10), Wednesday, September 25, 2024, 15:30−15:50, Salon 18

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 3, 2024

Abstract

This paper introduces a framework for an indoor autonomous mobility system that can perform patient transfers and materials handling. Unlike traditional systems that rely on onboard perception sensors, the proposed approach leverages a global perception and localization (PL) through Infrastructure Sensor Nodes (ISNs) and cloud computing technology. Using the global PL, an integrated Model Predictive Control (MPC)-based local planning and tracking controller augmented with Artificial Potential Field (APF) is developed, enabling reliable and efficient motion planning and obstacle avoidance ability while tracking predefined reference motions. Simulation results demonstrate the effectiveness of the proposed MPC controller in smoothly navigating around both static and dynamic obstacles. The proposed system has the potential for extension to intelligent connected autonomous vehicles, such as four-wheel independent drive/steering (4WID-4WIS) configurations for electric and cargo transport vehicles.