Automated Manipulation of Magnetic Microswarms for Temporal Logic Cargo Delivery Tasks in Complex Environments

Abstract

Micromanipulation using magnetic microswarms has garnered significant attention in recent years due to their potential in microscale cargo delivery tasks. While existing studies have demonstrated the capabilities of microswarms in basic manipulation tasks, they often lack the autonomy required to handle more complex specifications, particularly temporal logic tasks. In this paper, we propose a novel formal planning strategy for magnetic microswarms that enables cargo delivery in complex environments while satisfying finite linear temporal logic (LTL<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf>) specifications. Our approach consists of two key components. First, we develop a high-level path planner based on a bidirectional temporal logic rapid-explore random tree star (BTL-RRT*) algorithm, which facilitates efficient planning while ensuring compliance with the given task specifications. Second, we employ an automaton to manage the manipulation modes of the microswarm, enabling real-time control over the capture and release of cargoes. In addition, we implement the planning strategy on microswarms actuated by a visual feedback magnetic tweezers system. Extensive simulations and experimental results demonstrate the effectiveness of the proposed planning strategy. The results indicate that, using the proposed approach, microswarms can autonomously select and deliver multiple microbeads to designated regions in both static and dynamic environments, adhering to the LTL<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">f</inf> specifications.