Dual-Robot Assisted Additive Manufacturing: Challenges and Solutions for Energy-efficient Path and Trajectory Planning

Robot-assisted additive manufacturing (AM) has been gaining popularity benefiting from its great multi-axis reachability. Moreover, an AM system with dual deposition-heads held by manipulators would significantly shorten the building time, especially for large-scale parts. In this talk, the challenges in automated planning tasks for additive manufacturing (AM) with dual robot manipulators (AM2R) are introduced first. Subsequently, an intelligent planning framework has been established to achieve time efficiency, high printing quality, deposition safety, and sustainability in AM2R. The presentation will focus on the developed solutions/algorithms in the following three interconnected categories: (1) Deposition task assignment (DTA) and end-effector path generation/sequencing, (2) Collision-free temporal coordinating and trajectory planning for the two robot arms, and (3) Robot energy consumption (EC) modelling.

Zhang Yunfeng is an Associate Professor with the Department of Mechanical Engineering, National University of Singapore, Singapore. He received the Ph.D. degree from the University of Bath, U.K., in 1991. He is the recipient of the following major awards:

(1) Kayamori Best Paper Award in 1999 IEEE International Conference on Robotics and Automation (ICRA’99);

(2) IMechE 2012 Thatcher Bros Prize’ for paper entitled “Job rescheduling by exploring the solution space of process planning for machine breakdown/arrival problems’ published in the Journal of Engineering Manufacture;

(3) Best Paper Award from Unmanned Systems (2021-2022) for paper entitled “Danger-Aware Adaptive Composition of DRL Agents for Self-Navigation”.

He is a Senior Member of IEEE. His current research interests include robotics and intelligent systems with a focus on data science and artificial intelligence solutions for industrial applications.