🤖 AI Summary
This work addresses the challenge of deadlock and local infeasibility in robotic task and motion planning under signal temporal logic (STL) specifications within non-convex, complex environments. To overcome these issues, the authors propose a hybrid planning framework that integrates discrete decision variables with continuous dynamics. By constructing control barrier functions in a geometrically transformed, disk-shaped workspace and incorporating local feasibility analysis under input saturation, the approach holistically resolves conflicts among multiple spatiotemporal tasks. The key innovation lies in the co-design of hybrid systems, STL specifications, and geometry-driven barrier functions, which collectively enhance planning feasibility and system robustness. Simulations demonstrate the method’s efficiency and reliability in handling overlapping spatiotemporal tasks.
📝 Abstract
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.