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This paper introduces a hybrid control architecture for planar task and motion planning under Signal Temporal Logic (STL) constraints. The approach uses a discrete variable to model local constraint satisfaction, enabling integrated planning and control design with feasibility analysis. Control barrier functions are designed on a transformed workspace to avoid deadlocks, demonstrating effective handling of complex spatio-temporal tasks with input saturation in simulations.
Escape deadlocks and choreograph robots through complex tasks with this new hybrid control architecture that merges planning and control.
In this work, a novel method for planar task and motion planning based on hybrid modeling is proposed. By virtue of a discrete variable which models local constraint satisfaction and enables local feasibility analysis, the proposed control architecture unifies planning with control design. Concurrently, control barrier functions are designed on a transformed disk version of the original nonconvex and geometrically complex robotic workspace, thus amending the issue of deadlocks. Simulations of the proposed method indicate effective handling of multiple overlapping spatio-temporal tasks even in the face of input saturation.
