Anuruddha Bhattacharjee

I am a Postdoctoral Fellow in the IMERSE Lab at the Johns Hopkins Laboratory for Computational Sensing and Robotics (LCSR), advised by Dr. Axel Krieger. I develop wireless millirobots, surgical devices, and intelligent systems for minimally invasive procedures and targeted drug delivery.

At Hopkins, I also lead a robotics team in the AIMD Laboratory, collaborating with materials scientists and engineers from the Hopkins Extreme Materials Institute (HEMI) to automate a high-throughput laser shock platform for advanced materials characterization.

I received my Ph.D. in Mechanical Engineering from Southern Methodist University, where I worked in the BAST Lab under the guidance of Dr. MinJun Kim and Dr. Aaron T. Becker, focusing on the design, dynamics, and motion planning of reconfigurable modular robots. I also developed bioinspired soft millirobots and investigated their locomotion in complex environments.

Prior to graduate school, I served as a Lecturer in the Department of Mechanical Engineering at Sonargaon University in Dhaka. I earned my B.Sc. in Mechanical Engineering from Bangladesh University of Engineering and Technology (BUET), where I conducted research on computational heat transfer, with a focus on optimizing the design of industrial heat exchangers.

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Research

My research lies at the intersection of robotics and healthcare, with a focus on the design, control, and automation of intelligent mesoscale robotic systems that interact with complex physiological environments. I specialize in integrating miniaturized mechanical designs with electromagnetic actuation, computer vision, and real-time control algorithms to enable autonomous operation of wireless surgical tools, millirobots, and soft robotic systems in dynamic, unstructured environments.

I envision a future where medical robotics is transformed by wireless, miniaturized systems capable of navigating the human body with precision—enabling ultra-minimally invasive surgery and diagnostics without the need for large incisions or bulky instruments. My research aims to realize this vision by developing innovative robotic platforms and control strategies that enhance the capabilities of medical professionals and improve patient outcomes.

To advance this vision, I aim to explore the following research directions:

• Untethered Reconfigurable Tools for Intelligent Modular Systems: How can we design miniaturized, externally guided tools that are reconfigurable on demand and capable of task-specific assembly and disassembly inside the human body?


• Bioinspired Soft Robots for Navigation in Complex Environments: How can we mimic biological locomotion and morphology to create scalable, intelligent soft robots capable of navigating through dynamic and constrained anatomical environments?


• Advanced Localization and Image Guidance for Mesoscale Robots: How can we integrate medical imaging modalities (e.g., ultrasound, X-ray, endoscopy) with AI-guided localization algorithms to achieve accurate, real-time tracking and control of mesoscale robots in complex surgical environments?


• Enhanced Autonomy Through Integrated Learning and Planning: How can we integrate AI-based learning with model-driven planning to enable robust and precise control of stochastic, underactuated miniature robots for complex tasks?

Publications

I have contributed to 10 journal articles (6 as first author) and 13 peer-reviewed conference papers (2 as first author). My full publication list is available on Google Scholar.

^† Co-first authors, * Corresponding authors, ^#G Co-mentored graduate students, ^#U Co-mentored undergraduate students
First-author papers are highlighted with a yellow background.

Journal Articles

This work presents wireless modular millirobots with autonomous navigation, tissue penetration, and on-demand docking/undocking for targeted drug delivery in a heart phantom under pulsatile flow.

This work presents magnetically actuated, uniform rod-like soft robots, along with their numerical modeling and propulsion performance in viscous and constrained fluidic environments, including a human aneurysm model.

This work presents magnetically actuated modular millibots capable of autonomous self-assembly, wall climbing, and closed-loop navigation using a vision-based feedback controller that integrates a harmonic traveling salesman problem (HTSP) solver with motion dynamics for time-optimal self-assembly.

This work presents magnetically actuated cuboid robots capable of self-assembly, pivot-walking locomotion, and vision-based closed-loop 2D navigation with path planning in mazes.

This work introduces a novel nested electromagnetic coil system and a singularity-resolving controller for precise closed-loop control of magnetic robots, achieving sub-millimeter precision in trajectory tracking of a PillCam™ and a permanent magnet robot.

This work presents a vision-guided closed-loop control method for self-assembling magnetic modular cubes into user-defined 2D polyomino shapes using uniform magnetic fields and boundary-assisted interactions across multiple length scales.

This work presents a bacteria-inspired, magnetically actuated rod-like soft robot capable of swimming and boundary rolling in highly viscous fluids, with experimentally validated and simulated propulsion dynamics under low Reynolds number conditions.

This work presents a scalable modular robotic platform with programmable assembly–disassembly of magnetic cubes into 2D polyomino shapes using open-loop control and motion planning, demonstrating robust performance across varied length scales.

This work presents open-loop manipulation of snowman-shaped soft microrobots under a uniform rotating magnetic field.

This work presents a low-cost millirobot that is simple in design, easy to fabricate, highly scalable, and can be used as modular sub-units within complex structures for large-scale manipulation.

EndoSRT is a transformer-based imitation learning framework that enables autonomous, goal-conditioned navigation of a soft robotic guidewire for endovascular procedures. Trained on 647 demonstrations across 36 modular neurovascular geometries, the system achieves an 83% success rate in targeting aneurysms in previously unseen anatomies under simulated fluoroscopy.

This work introduces a semi-autonomous control strategy for an untethered magnetic suture needle for robotic suturing tasks in bladder repair surgery, experimentally validated through suturing on tissue-mimicking gel phantoms.

Presents a novel controller for robust waypoint tracking in magnetic robots, avoiding singularities using field-based planning and optimal current control.

Introduces a vision-based automation framework for precise control of flyer targets in high-throughput laser-driven impact experiments.

Enumerates all reachable configurations of magnetic modular cubes for self-assembling polyomino and polycube shapes under uniform magnetic fields.

Introduces an adaptive control strategy to track the motion of a biologically-inspired alginate artificial cell under external magnetic actuation.

Demonstrates programmable 2D motion and collaborative assembly of magnetic cuboid robots under uniform rotating magnetic fields with boundary-assisted control.

Presents a rod-like soft robot actuated magnetically in free space and constrained environments, with numerical modeling and experimental propulsion validation.

Proposes feedback control strategies for heterogeneous Janus particles with controllable 3D magnetic locomotion in fluid environments.

Numerical investigation of heat exchanger performance enhancement by optimizing the helix angle of continuous helical baffles.

Conducts a CFD study on turbulent mixed convection flow in pipes, analyzing thermal and velocity profiles under different boundary conditions.

Investigates thermal performance of finned versus plain tube bundles in shell-and-tube heat exchangers using CFD simulations.

Simulates transient MHD mixed convection in an L-shaped enclosure filled with ferrofluid, highlighting effects on heat transfer and fluid flow.

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