Project Description

In modern industrial manufacturing, integrating multi-robot and multi-human systems that collaborate presents both significant opportunities and challenges, primarily concerning human safety, task efficiency, and effective coordination. Traditional robotic systems, often encumbered by physical barriers for safety, are costly and inflexible, limiting their applicability in dynamic manufacturing environments. Recent research highlights advancements in human-robot collaboration (HRC) through technologies like collaborative robots and artificial intelligence, which enhance interaction and coordination. Despite these advancements, current HRC systems often fail to seamlessly integrate human safety with task efficiency, and both multi-robot and multi-human coordination remain suboptimal, particularly in complex assembly processes. To address these gaps, we propose developing intelligent multi-robot systems that dynamically collaborate with multiple humans in manufacturing assembly tasks. This involves implementing context-aware safety protocols that use real-time data to prevent hazards without compromising efficiency, and developing algorithms for real-time task allocation that adapt to the changing capabilities of both human workers and robots. Additionally, AI-driven coordination mechanisms will enhance the symbiosis between multi-robot systems and human operators, ensuring smoother and more efficient collaboration. Our research aims to create a safer, more efficient manufacturing environment that leverages the strengths of both humans and robots, ultimately improving productivity and flexibility in industrial manufacturing. 

Start Date

Spring 2025

Postdoc Qualifications

• Strong backgrounds in robotics, autonomy, control, optimization, and AI/machine learning
• PhD degree in Engineering or related fields
• Passion and interest to solve challenging research problems using methodologies from different areas
• Good communication and writing skills
• Ability to thrive in a collaborative environment.

Co-advisors

Yu She
Assistant Professor
School of Industrial Engineering
Purdue University
Email: shey@purdue.edu
Website: https://www.purduemars.com/

Shaoshuai Mou
Elmer Bruhn Associate Professor
School of Aeronautics and Astronautics
Purdue University
Email: mous@purdue.edu
Website: https://engineering.purdue.edu/AIMS

Bibliography