Informed Sampling-Based Motion Planning for Manipulating Multiple Micro Agents Using Global External Electric Fields

• Published in: IEEE transactions on automation science and engineering Vol. 19; no. 3; pp. 1 - 12
• Main Authors: Li, Xilin, Wu, Juan, Song, Jiaxu, Yu, Kaiyan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuation; Algorithms; Convergence; Coupling; Decision trees; Electric contacts; Electric fields; Electrodes; Heuristic algorithms; Markov processes; Micro- and nano-agent manipulation; Motion planning; Multiagent systems; Planning; Robustness (mathematics); Routing; Sampling; Search process; Trajectory; Trajectory optimization; Transmission line matrix methods; Wireless communication; Workspace
• ISSN: 1545-5955; 1558-3783
• DOI: 10.1109/TASE.2022.3151872

Online manipulation of multiple micro- and nanoscale agents is of major interest for various research applications. Among the biggest limitations of wireless external actuation are its global and coupled influences in the workspace, which limit the robust manipulation of multiple agents independently and simultaneously. In this paper, we propose novel motion planning algorithms, Bi-iSST and Ref-iSST, to quickly generate time-optimal trajectories for multiple agents sharing global external fields. Both algorithms are extended by the stable sparse rapidly-exploring random tree kinodynamic motion planning algorithm. The Bi-iSST uses a bidirectional approach to speed up the searching process. A novel connection process is proposed to connect the two trees efficiently by applying an optimization procedure. The Ref-iSST uses the workspace information to quickly generate global-routing trajectories as references, then guides the search process more effectively by getting more accurate heuristics according to the reference global-routing trajectories. A transition matrix similar to that in Markov Decision Processes is used to form the reference trajectory. Compared with the state-of-the-art iSST algorithm, the proposed algorithms quickly update feasible solutions and converge to a near-optimal, minimum-time solution to increase the efficiency of the simultaneous manipulation of multiple micro agents using global external fields. Extensive analysis and physical experiments are presented to confirm the effectiveness and the performance of the motion planning algorithms.