Bi-directional locomotion of a magnetically-actuated jellyfish-inspired soft robot

Biomimetic compliant untethered robots find a plethora of applications in biomedical engineering, microfluidics, soft robotics, and deep-sea exploration. Flexible miniature robots in the form of magnetically actuated compliant swimmers are increasingly used for targeted drug delivery, robotic surgery, laparoscopy, and microfluidic device design. These applications require an in-depth understanding of the nonlinear large deformation structural mechanics, non-invasive remote-control and untethered actuation mechanisms, and associated fluid-structure interactions that arise between a soft smart robot and its surrounding fluid. The present work obtains numerical solutions for the temporal evolution of structural and flow variables using a fictitious domain method that employs a robust multi-physics computational model involving both fluid-structure interaction and magneto-elasto-dynamics. The magnetically-actuated soft robotic swimmer (jellyfishbot) is inspired by the most efficient aquatic swimmer, the jellyfish. The swimming kinematics and bi-directional locomotion are obtained for different waveforms and gradients of the external magnetic actuation. The breaking of temporal symmetry and its relative dominance is discussed as well

Magnetic-field-induced propulsion of jellyfish-inspired soft robotic swimmers

The multifaceted appearance of soft robots in the form of swimmers, catheters, surgical devices, and drug-carrier vehicles in biomedical and microfluidic applications is ubiquitous today. Jellyfish-inspired soft robotic swimmers (jellyfishbots) have been fabricated and experimentally characterized by several researchers that reported their swimming kinematics and multimodal locomotion. However, the underlying physical mechanisms that govern magnetic-field-induced propulsion are not yet fully understood. Here, we use a robust and efficient computational framework to study the jellyfishbot swimming kinematics and the induced flow field dynamics through numerical simulation. We consider a two-dimensional model jellyfishbot that has flexible lappets, which are symmetric about the jellyfishbot center. These lappets exhibit flexural deformation when subjected to external magnetic fields to displace the surrounding fluid, thereby generating the thrust required for propulsion. We perform a parametric sweep to explore the jellyfishbot kinematic performance for different system parameters—structural, fluidic, and magnetic. In jellyfishbots, the soft magnetic composite elastomeric lappets exhibit temporal and spatial asymmetries when subjected to unsteady external magnetic fields. The average speed is observed to be dependent on both these asymmetries, quantified by the glide magnitude and the net area swept by the lappet tips per swimming cycle, respectively. We observe that a judicious choice of the applied magnetic field and remnant magnetization profile in the jellyfishbot lappets enhances both these asymmetries. Furthermore, the dependence of the jellyfishbot swimming speed upon the net area swept (spatial asymmetry) is twice as high as the dependence of speed on the glide ratio (temporal asymmetry). Finally, functional relationships between the swimming speed and different kinematic parameters and nondimensional numbers are developed. Our results provide guidelines for the design of improved jellyfish-inspired magnetic soft robotic swimmers

Efficient Motion and Inspection Planning for Medical Robots with Theoretical Guarantees

Medical robots enable faster and safer patient care. Continuum medical robots (e.g., steerable needles) have great potential to accomplish procedures with less damage to patients compared to conventional instruments (e.g., reducing puncturing and cutting of tissues). Due to their complexity and degrees of freedom, such robots are often harder and less intuitive for physicians to operate directly. Automating robot-assisted medical procedures can enable physicians and patients to harness the full potential of medical robots in terms of safety, efficiency, accuracy, and precision.Motion planning methods compute motions for a robot that satisfy various constraints and accomplish a specific task, e.g., plan motions for a mobile robot to move to a target spot while avoiding obstacles. Inspection planning is the task of planning motions for a robot to inspect a set of points of interest, and it has applications in domains such as industrial, field, and medical robotics. With motion and inspection planning, medical robots would be able to automatically accomplish tasks like biopsy and endoscopy while minimizing safety risks and damage to the patient. Computing a motion or inspection plan can be computationally hard since we have to consider application-specific constraints, which come from the robotic system due to the mechanical properties of the robot or come from the environment, such as the requirement to avoid critical anatomical structures during the procedure.I develop motion and inspection planning algorithms that focus on efficiency and effectiveness. Given the same computing power, higher efficiency would shorten the procedure time, thus reducing costs and improving patient outcomes. Additionally, for the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the algorithms involved in procedure automation. Therefore, I focus on providing theoretical guarantees to certify the performance of planners. More specifically, it is important to certify if a planner is able to find a plan if one exists (i.e., completeness) and if a planner is able to find a globally optimal plan according to a given metric (i.e., optimality).Doctor of Philosoph