  This paper focuses on automatic guided vehicle (AGV) trajectory planning in
the presence of moving obstacles with known but complicated trajectories. In
order to achieve good solution precision, optimality and unification, the
concerned task should be formulated as an optimal control problem, and then
discretized into a nonlinear programming (NLP) problem, which is numerically
optimized thereafter. Without a near-feasible or near-optimal initial guess,
the NLP-solving process is usually slow. With the purpose of accelerating the
NLP solution, a search-based rough planning stage is added to generate
appropriate initial guesses. Concretely, a continuous state space is
formulated, which consists of Cartesian product of 2D configuration space and a
time dimension. The rough trajectory is generated by a graph-search based
planner, namely the A* algorithm. Herein, the nodes in the graph are
constructed by discretizing the aforementioned continuous spatio-temporal
space. Through this first-search-then-optimization framework, optimal solutions
to unified trajectory planning problems can be obtained fast. Simulations have
been conducted to verify the real-time performance of our proposal.
