AMR task allocation based on improved greedy algorithm

Abstract:

When using Autonomous Mobile Robot (AMR) cluster to process airport baggage intelligently and efficiently, in order to solve the allocation and scheduling problem of AMR cluster in the airport environment, a task scheduling strategy with an improved greedy algorithm is proposed. According to the random baggage quantity, the appropriate AMR quantity is allocated to perform the processing task. The proposed algorithm comprehensively considers the arrival rules and AMR characteristics of baggage tasks in an airport environment, and accordingly improves the greedy selection strategy, which making it better than other algorithms to reflect the scheduling and allocation relationship between baggage tasks and AMR. Firstly, the A* algorithm is used to calculate the cost, which can obtain a substitute value that is more in line with the actual environment. Secondly, the type division of AMR and the use of advance departure strategies reduce the task allocation time and system runtime. Simulation results show that the algorithm can obtain at least 8.9% improvement in system runtime compared with the greedy algorithm.