Graph Convolutional Network Based Ant Colony Optimization for Robot Task Allocation

The robot task allocation is a crucial problem in logistics and distribution where robots are required to perform an array of tasks, with differing locations and numbers. As a result, optimally allocating tasks to available robots to minimize associated costs has become a challenging but essential optimization problem. This paper presents a Graph Convolutional Network (GCN) based Ant Colony Optimization (ACO) denoted as GCN-ACO, to solve the robot task allocation problems, which are formulated as the Travelling Salesman Problems (TSP) and Vehicle Routing Problems (VRP). The GCN-ACO algorithm comprises two stages. In the first stage, a GCN model is trained to predict a heatmap, which represents the probability of each edge belonging to the optimal route within the graph. In the second stage, we integrate the predicted heatmap into ACO to guide the ant colony to select edges with greater potential during the search process. We evaluate our approach's performance through testing on standard TSP and VRP datasets. The experimental results demonstrate that our proposed method has a faster convergence rate and a higher quality of solutions compared to the baseline approaches.