Towards Microrobot Swarm Path Planning

dc.contributor.advisorBecker, Aaron T.
dc.contributor.committeeMemberClaydon, Frank J.
dc.contributor.committeeMemberMayerich, David
dc.contributor.committeeMemberNguyen, Hien Van
dc.contributor.committeeMemberLindner, Peggy
dc.creatorHuang, Li 2019 2019
dc.description.abstractTiny 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.
dc.description.departmentElectrical and Computer Engineering, Department of
dc.format.digitalOriginborn digital
dc.identifier.citationPortions 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.
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. UH Libraries has secured permission to reproduce any and all previously published materials contained in the work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectPath planning
dc.subjectReinforcement learning
dc.subjectDeep learning
dc.titleTowards Microrobot Swarm Path Planning
local.embargo.terms2021-08-01 College of Engineering and Computer Engineering Engineering of Houston of Philosophy


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