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

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 the bidirectional approach to speed up the searching process. A novel connection process is proposed to connect 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 global-routing reference trajectory. Compared to the state-of-the-art algorithms, 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.