Towards Microrobot Swarm Path Planning



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Tiny robots have many promising applications in medical treatment including targeted drug delivery, non-invasive diagnosis, and minimally invasive surgery; and in micro-assembly/ -fabrication for Micro-Electro-Mechanical-Systems (MEMS). Microrobots are often deployed in large populations, and typically steered by uniform driving signals, including magnetic, electromagnetic, electrostatic, optical, gravitational, thermal, and chemical. The homogeneity of the microrobots and the uniformity of the control input make microrobot swarm manipulation difficult in constrained workspaces such as human vascular networks. The control laws and path-planning algorithms designed for macro-size robotics do not scale well to a microrobot swarm, so new methodology must be developed to address more efficient planning with constraints for a multi-agent problem in microscale. This thesis addresses the path-planning problem of a swarm of microrobots using a global control input. It begins with an introduction to state-of-the-art research and applications in microrobots. Chapter 2 gives an analysis of 2D and 3D position control of heterogeneous microrobots in the free space, together with demonstrations in simulations and hardware experiments. Motivated by the need for higher computational efficiency and capability of swarm manipulation with spatial constraints, chapter 3 discusses strategies of planning in 2D vascular networks for a swarm of homogeneous microrobots given a shared, global control input. Multiple path-planning methods and control algorithms are proposed, and their performance is compared in multiple vascular networks with different scale and complexity. The algorithms are validated with simulations and hardware experiments. Chapter 4 investigates reinforcement learning strategies to further improve path-planning efficiency, and to overcome local minima dilemmas in online algorithms. Chapter 5 reports automatic steering methods in multi-bifurcation vessels with flow, and reinforcement learning algorithms are implemented for improvement in microrobot delivery rate.



Swarms, Microrobot, Controls, Path planning, Reinforcement learning, Deep learning


Portions of this document appear in: Huang, Li, Julien Leclerc, and Aaron T. Becker. "Analysis of 3D Position Control for a Multi-Agent System of Self-Propelled Agents Steered by a Shared, Global Control Input." In 2019 International Conference on Robotics and Automation (ICRA), pp. 4465-4471. IEEE, 2019. And in: Huang, Li, Louis Rogowski, Min Jun Kim, and Aaron T. Becker. "Path planning and aggregation for a microrobot swarm in vascular networks using a global input." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 414-420. IEEE, 2017.