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

TL;DR

This paper presents an indoor autonomous mobility system that uses infrastructure sensor nodes and cloud computing for perception and localization, combined with an MPC-based control for obstacle avoidance and motion tracking.

Contribution

It introduces a novel framework integrating infrastructure-based perception with advanced control for indoor autonomous mobility, reducing reliance on onboard sensors.

Findings

Simulation shows effective obstacle avoidance and smooth navigation.

The system demonstrates reliable motion tracking in complex environments.

Potential extension to autonomous vehicles with 4WID-4WIS configurations.

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 to extend to intelligent connected autonomous vehicles, such as electric or cargo transport vehicles with four-wheel independent drive/steering (4WID-4WIS) configurations.