STyLuS*: A Temporal Logic Optimal Control Synthesis Algorithm for Large-Scale Multi-Robot Systems.

摘要: This paper proposes a new highly scalable and asymptotically optimal control synthesis algorithm from linear temporal logic specifications, called $\text{STyLuS}^{*}$ for large-Scale Temporal Logic Synthesis, that is designed to solve complex planning problems in large-scale multi-robot systems. Existing approaches with specifications rely on graph search techniques applied product automaton constructed among the robots. In our previous work, we have proposed more tractable sampling-based builds incrementally trees approximate state-space transitions of synchronous does not require sophisticated techniques. Here, extend work by introducing bias sampling process which guided B$\ddot{\text{u}}$chi belong shortest path accepting states. allows us synthesize motion plans automata hundreds orders magnitude states than those existing methods or off-the-shelf model checkers can manipulate. We show probabilistically complete has exponential convergence rate. first time rate results are provided methods. provide simulation very large systems impossible using state-of-the-art