  We present a novel framework for addressing the challenges of multi-Agent
planning and formation control within intricate and dynamic environments. This
framework transforms the Multi-Agent Path Finding (MAPF) problem into a
Multi-Agent Trajectory Planning (MATP) problem. Unlike traditional MAPF
solutions, our multilayer optimization scheme consists of a global planner
optimization solver, which is dedicated to determining concise global paths for
each individual robot, and a local planner with an embedded optimization solver
aimed at ensuring the feasibility of local robot trajectories. By implementing
a hierarchical prioritization strategy, we enhance robots' efficiency and
approximate the global optimal solution. Specifically, within the global
planner, we employ the Augmented Graph Search (AGS) algorithm, which
significantly improves the speed of solutions. Meanwhile, within the local
planner optimization solver, we utilize Control Barrier functions (CBFs) and
introduced an oblique cylindrical obstacle bounding box based on the time axis
for obstacle avoidance and construct a single-robot locally aware-communication
circle to ensure the simplicity, speed, and accuracy of locally optimized
solutions. Additionally, we integrate the weight and priority of path traces to
prevent deadlocks in limiting scenarios. Compared to the other state-of-the-art
methods, including CBS, ECBS and other derivative algorithms, our proposed
method demonstrates superior performance in terms of capacity, flexible
scalability and overall task optimality in theory, as validated through
simulations and experiments.
