[DGIST Series] AI-Powered Micro/Nanorobots to Revolutionize Medical Field

By July 20, 2023 January 19th, 2024 No Comments

The 1966 sci-fi film Fantastic Voyage showed viewers an unrealistic yet revolutionary concept. The movie tells the story of a submarine crew who shrink themselves and enter a scientist’s body to remove a blood clot. Although the plot appears almost comical, the film provided a glimpse of a future when safe intravenous treatments could replace conventionally risky but unavoidable surgeries.

Figure 1. The poster for the 1966 sci-fi film Fantastic Voyage (a) and magnetic microrobots for 3D cell culture and targeted transportation (b) (Figure 1b image credit: Wiley-VCH)


While the idea of shrinking biological entities remains far-fetched, reductions in the sizes of non-living things are now possible. Advances in micro-electromechanical systems (MEMS) and semiconductor technologies have enabled the micro/nanoscale fabrication of robots and devices, thereby revolutionizing micro/nanorobotics. Today, micro/nanorobots are used in biomedical applications, such as precise drug and cell delivery, non-invasive diagnosis, and targeted therapies.

In the latest article in our series from DGIST faculty which covers slightly different topics from the previous semiconductor-related posts, Professor Hong-soo Choi from the Department of Robotics Engineering explains how the application of AI has helped to advance the use of micro/nanobots in the medical field.

Realizing Untethered Microrobots Through Optical, Acoustic and Magnetic Actuation

Conventional robots require integrated circuits and power to actuate their motors, making fabrication difficult at micro/nanoscales. Actuation by optical, acoustic, or magnetic sources solves both of these issues by allowing wireless actuation of these robots.

Figure 2. Optical (a), acoustic (b), and magnetic (c) actuation schemes for microrobots


A. Optical-based Actuation

Optical-based actuation schemes use changes in the physical properties of a specific material upon exposure to a light source. Liquid crystal elastomers1 (LCE) combine the elasticity of a polymer with the self-organization of the liquid crystalline phase to create flexible structures that undergo reversible deformation. Actuators based on LCE and LCE-based composites can be actuated using a light source, as shown in Figure 2a. Several actuation schemes and fabrication techniques for these materials have been presented, which allow photothermal and photochemical actuation of liquid crystal elastomer-based soft robots. The actuation of soft robots depends on the light source and its interaction with the environment. Considering the progress in liquid crystal elastomers, this actuation had predominantly become a design issue which was solved using biomimetics2. Various bio-inspired designs for liquid crystal elastomers in soft miniature robotics have been presented, such as artificial cilia3 and caterpillar-inspired devices.

1Elastomer: A polymer with viscosity and elasticity such as natural rubber.
2Biomimetics: The study of principles of nature which can be applied to materials, synthetic systems and machines that can imitate the structure and function of native biological systems.
3Cilia: Hair-like structures that extend from the cell body into the fluid surrounding the cell.

Another photothermal mechanism is one which uses a metal layer on the microrobot and propels the device with the heat generated from a laser. Additionally, a control scheme has been presented where optoelectronic tweezers, which use projected optical images to manipulate tiny particles, can actuate the microrobot.

B. Acoustic-based Actuation

Acoustic-based actuation schemes rely on sound waves to propel micro/nanorobots. Similar to optical actuation, acoustic-based actuation depends on the robot design. However, the choice of materials gives it an advantage in terms of allowing diverse bio-compatible designs. Two-photon polymerization and photolithography4 are widely used for the fabrication of such microrobots. Three major acoustic actuation schemes are bubble propulsion, flexible-tailed propulsion, and in situ5 micro-rotors.

4Photolithography: A technique that uses light to produce minutely patterned thin films of suitable materials over a substrate, such as a silicon wafer, to protect the selected areas.
5In situ: A Latin phrase meaning on-site or in its original place.

In a bubble propulsion scheme, a bubble trapped inside an open-ended structure vibrates along with the applied acoustic field as shown in Figure 2b. The two ends of the structure allow the intake and discharge of fluid, and the propulsion increases as the applied frequency approaches the resonance frequency of the bubble. Flexible-tailed propulsion is inspired by single-celled organisms and flagella6. It uses flexible-tailed microrobots to generate counter-rotating eddies7 for propulsion. Meanwhile, in situ micro-rotors with a fixed axis of rotation have a working principle similar to flexible-tailed propulsion.

6Flagella: Microscopic hair-like structures involved in the locomotion of a cell.
7Eddy: The swirling of a fluid and the reverse current created when the fluid flows past an obstacle.

C. Magnetic-based Actuation

Magnetic-based actuation schemes use systems based on electromagnets, permanent magnets, or both. In contrast to acoustic and optical-based actuation, magnetic actuation requires an emphasis on control theory and microrobot structure. As shown in Figure 2c, the three main control strategies for the magnetic actuation of microrobots involve gradient, rotating, or oscillating magnetic fields.

Under gradient magnetic fields, microrobots experience magnetic torque and magnetic force. In this case, field vectors guide orientation and the magnetic field gradient controls motion. Any structure with magnetic properties can be guided in this manner. In contrast, a rotating field is generated by the rotation of the magnetic field vector around an axis. Under a rotating field, helical microrobots propel themselves forward using fluid forces; cylindrical microrobots can use a tumbling motion; and spherical microrobots can roll on a surface. Finally, oscillating fields are generated by the movement of the field vector up and down in a plane. Flexible or tailed microrobots and soft robots are commonly actuated with this strategy.

The Rise of AI and Machine Learning

In recent years, AI has undergone a rapid transformation and is now utilized in a range of sectors including transportation, banking, healthcare, and industrial automation. The rise of AI was triggered by state-of-the-art research, advances in semiconductor technology, and the availability of extensive data.

Figure 3. The processes of supervised learning, unsupervised learning, and reinforcement learning


Machine learning (ML) encompasses a subset of AI processes in which a machine learns to perform a specific task through analysis of a provided dataset. As shown in Figure 3, there are three major ML algorithms: supervised learning, unsupervised learning and reinforcement learning. ML combined with a deep neural network (comprising multiple artificial neural networks layered on each other) is known as deep learning. Deep learning has enabled ML to manage information and make human-level or even better decisions.

Supervised learning algorithms require a labeled dataset from which the machine can learn and then infer based on some input. Considering the extensive data currently available, supervised learning has become a very powerful tool for classification, regression, and forecasting tasks. Object recognition, speech recognition, and stock market prediction are among the practical uses of supervised learning.

Unsupervised learning algorithms are used when a dataset is not labeled. These algorithms are more complex than supervised learning as they must comprehend data without relying on human knowledge in the form of labels. Unsupervised learning is used for tasks such as clustering, detecting similarities and anomalies, visualizing data, and labeling unlabeled data.

Reinforcement learning (RL) is the nearest of the three to the human learning process. The algorithm learns to complete a specific task by exploring a particular scenario or environment and learning from its mistakes. The dataset is generated at runtime while the algorithm performs some action in the environment. The RL algorithm can learn without any human interference and is generally used to enable machines to understand tasks humans are already performing, such as playing computer games, driving cars, and trading stocks. Notably, in 2016 an RL-powered computer program named AlphaGo defeated professional Go world champion Lee Sedol.

Micro/Nanorobots and AI

In the past decade, AI has become increasingly valuable in robotics. It has allowed control of robots, followed by vision and then autonomous control, thus providing a new perspective in robotics. Considering the recent demand for autonomous vehicles and the increase in autonomous robots in everyday life, the idea of humanoids replacing humans appears feasible in the not-too-distant future. In the early 2000s, the Da Vinci surgical system allowed robotics to become a component of conventional surgery. It allows surgeons to perform invasive and complex procedures with relative ease, particularly in small, confined spaces.

Complex tasks for machines become considerably more difficult when they are conducted on a micro- or nanoscale inside the human body. Accordingly, researchers have increasingly incorporated AI in microrobotics over the past decade. Challenges in terms of control/actuation, automation, imaging, and design optimization at the micro/nanoscale are being addressed with AI.

Micro/nanorobot design optimization is important because of its central role in the efficiency of control schemes. ML is widely used as a design optimization technique in areas such as the automotive industry, antenna design, and composite material design. In comparison to conventional optimization techniques, ML can substantially accelerate the optimization problem and provide better designs. A recent example of ML-based optimization at the micro/nanoscale is the optimal adhesive fibril design reported by Donghoon Son et al. in 2021, in which their ML approach outperformed proposed designs by 77%.

Imaging at a micro/nanoscale inside the human body is challenging because optical imaging techniques cannot be used. Magnetic resonance imaging, ultrasound, and X-rays are used to detect the positions of micro/nanostructures both in vivo8 and ex vivo9. In 2022, a study by Karim Botros et al. on autonomous detection and tracking based on ultrasound imaging of a microrobot found they were able to track the robot with an accuracy of more than 90% using deep learning. In the same year, Mehmet Efe Tiryaki et al. presented deep learning-based three-dimensional tracking of a magnetic microrobot using two-dimensional magnetic resonance images and were able to achieve an accuracy of 87.5% in vitro.

8In vivo: A Latin phrase meaning inside a living organism.
9Ex vivo: A Latin phrase meaning outside a living organism.

Control and actuation at a micro/nanoscale inside the human body have greater complexity because of Brownian motion10 interactions between the micro/nanorobot’s structure and the environment, and the effect of the actuation mechanism on the robot. These complexities hinder open-loop control of the robots, making conventional controllers like proportional-integral-derivative (PID)11 operations unreliable. RL has therefore become a reliable ML technique for control actuation schemes because it is less dependent on human input and can generate the data itself.

10Brownian motion: The random movement displayed by small particles that are suspended in fluids.
11Proportional-integral-derivative: A control loop feedback mechanism widely used in industrial control systems which uses feedback to continuously adjust the output of a process or system to match a desired setpoint.

In 2018, Santiago Muiños-Landin et al. published one of the first works regarding the use of RL with a microrobot. They navigated a light-controlled artificial microswimmer, a microscopic device able to move in fluids, under the influence of Brownian motion. They divided the workspace into a grid-like structure and used the RL to identify the optimal path for the microswimmer to reach a target in the grid. For magnetic actuation, in 2021 Michael R. Behrens and Warren C. Ruder presented a smart helical-shaped microrobot with an RL-guided time-varying magnetic field for optimal motion inside a circular fluidic channel. The most recent work regarding an acoustic actuation scheme and RL was published by Matthijs Schrage et al. in 2022 in which they showed programmable control of a swarm of microrobots via ultrasound. Moreover, Lidong Yang et al. showcased fully autonomous navigation of magnetic nanoparticles via RL in 2022. Their RL approach could control the shape and trajectory of a nanoparticle swarm for optimal navigation under time-varying magnetic fields.

Future of AI-Powered Microrobotics

Figure 4. Neural reconstruction via magnetically actuated microrobots on a microelectrode array


Recent works focused on microrobots in biomedical applications, such as neural reconstruction as shown in Figure 4 have led to an increasing need for in vivo and human trials. Current work in microrobotics and AI suggests there is potential for unlimited progress. Combinations of micro/nanorobots in biomedical fields with AI for very complex tasks should soon lead to in vivo testing and clinical trials as AI can eliminate human error and limitations at the micro/nanoscale.

Considering the significant advancements thus far in the performance of microscale imaging, localization, control, and fabrication, AI-powered micro/nanorobots will almost inevitably replace humans in operating theaters in the future. Along with these technologies, the use of semiconductors will also increase as machine learning develops further in the future.

<Other articles from this series>

[DGIST Series] How the Quest for AI Led to Next-Generation Memory & Computing Processors

[DGIST Series] How Broadband Interface Circuits Are Evolving for Optimal Data Transfer

[DGIST Series] The Role of Semiconductor Technologies in Future Robotics

[DGIST Series] The Technologies Handling the Growing Data Demands in Healthcare

[DGIST Series] Silicon Photonics: Revolutionizing Data Transfers by Unleashing the Power of Light

[DGIST Series] Sensor Interfaces and ADC Circuits: Bridging the Physical and Digital Worlds