TY - GEN
T1 - Advancing In-Situ Bioprinting Through Ant Colony Optimisation
T2 - 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024
AU - Liu, Keyu
AU - Huang, Long
AU - Feng, Zhiming
AU - Ren, Xianlin
AU - Chen, Yi
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine.
AB - Organ transplantation faces significant challenges due to the limited availability of donor organs, necessitating the exploration of innovative alternatives. Bioprinting, a dynamic form of 3D printing, offers a promising solution by creating complex biological structures layer by layer. This study focuses on addressing the dynamic optimization challenges in in-situ bioprinting through the integration of Ant Colony Optimization (ACO) within the Computational Intelligence Aided Design (CIAD) framework. The dynamic nature of bioprinting environments, characterized by shifting obstacles and changing targets, requires robust algorithms that can adapt to these variations in real-time. ACO, inspired by the foraging behavior of ant colonies, provides effective global optimization with low complexity, making it well-suited for these dynamic conditions. The methodology includes environmental modeling, adaptive path selection, and dynamic dead zone escape through improved state transition rules. Simulations conducted using MATLAB demonstrate that ACO ensures complete area coverage, minimizes track repetition, and significantly reduces the number of turns, thus enhancing the efficiency and success rate of bioprinting. These findings highlight the potential of ACO in advancing dynamic optimization techniques for bioprinting, contributing to the broader fields of evolutionary dynamic optimization and regenerative medicine.
KW - 3D printing
KW - AI-in-the-Loop
KW - Ant colony optimisation
KW - CIAD
KW - Dead zone
KW - Dynamic environments
KW - Evolutionary algorithms
KW - In-situ bioprinting
KW - Path planning
KW - Regenerative medicine
KW - Tissue engineering
UR - http://www.scopus.com/inward/record.url?scp=105002719378&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-3949-6_5
DO - 10.1007/978-981-96-3949-6_5
M3 - Conference Proceeding
AN - SCOPUS:105002719378
SN - 9789819639489
T3 - Lecture Notes in Networks and Systems
SP - 61
EP - 78
BT - Selected Proceedings from the 2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024 - Advances in Intelligent Manufacturing and Robotics
A2 - Chen, Wei
A2 - Ping Tan, Andrew Huey
A2 - Luo, Yang
A2 - Huang, Long
A2 - Zhu, Yuyi
A2 - PP Abdul Majeed, Anwar
A2 - Zhang, Fan
A2 - Yan, Yuyao
A2 - Liu, Chenguang
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2024 through 23 August 2024
ER -