Ensemble control of spatial variance of microbot systems through sequencing of motion primitives from optimal control trajectories

Spatial variance reduction of microbot systems through ensemble control, i.e., using a global control input, is a challenging task. In this paper, we propose to use a sequence of primary motion maneuvers called motion primitives to perform spatial variance reduction. We extract these primitives from the principal directions of the optimal control trajectories. The primitives efficiently discretize the input space and reduce the dimension of the search space significantly. These enable us to exploit lightweight and adaptable search algorithms like \(A^{*}\) for the task of fast sub-optimal input primitive sequence generation. Furthermore, we propose a primitive-based receding horizon motion planner (PB-RHMP) to increase robustness to process noise and model uncertainty. We validate the proposed methods with several simulated case studies.