Task Scheduling Algorithm for Maximizing the Benefits of Passenger-Carrying Robots Based on ED-ACO

To address the problem of passenger-carrying services provided by robots in large public scenarios, this paper studies an Elite Dual-Adaptive Ant Colony Optimization (ED-ACO) algorithm for multitask scheduling to maximize benefits. The core idea of this algorithm is based on the ant colony optimization method, enhanced with elite ant sorting and pheromone adjustment mechanisms. It effectively optimizes the selection of task sequences and path planning for robots in dynamic environments. By integrating a dual-adaptive pheromone update matrix, the algorithm dynamically adjusts pheromone concentrations to guide task scheduling and path selection toward global optimization. Considering multi-objective optimization weights, a scheduling model is constructed to maximize benefits based on time, distance, and energy consumption. Simulation results demonstrate that the proposed algorithm features rapid convergence and outperforms traditional scheduling algorithms in multitask scheduling within airport terminals. It significantly enhances task completion efficiency and overall benefits, offering new insights and practical references for optimizing the scheduling of autonomous mobile robots in large-scale public spaces.