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This paper focuses on automatic guided vehicle (AGV) trajectory planning inthe presence of moving obstacles with known but complicated trajectories. Inorder to achieve good solution precision, optimality and unification, theconcerned task should be formulated as an optimal control problem, and thendiscretized into a nonlinear programming (NLP) problem, which is numericallyoptimized thereafter. Without a near-feasible or near-optimal initial guess,the NLP-solving process is usually slow. With the purpose of accelerating theNLP solution, a search-based rough planning stage is added to generateappropriate initial guesses. Concretely, a continuous state space isformulated, which consists of Cartesian product of 2D configuration space and atime dimension. The rough trajectory is generated by a graph-search basedplanner, namely the A* algorithm. Herein, the nodes in the graph areconstructed by discretizing the aforementioned continuous spatio-temporalspace. Through this first-search-then-optimization framework, optimal solutionsto unified trajectory planning problems can be obtained fast. Simulations havebeen conducted to verify the real-time performance of our proposal.

Real-Time Trajectory Planning for AGV in the Presence of Moving Obstacles: A First-Search-Then-Optimization Approach