Integrated Task Allocation and Path Coordination for Large-Scale Robot Networks With Uncertainties

Artificial intelligence-enhanced autonomous unmanned systems, such as large-scale autonomous robot networks, are widely used in logistic and industrial applications. In this article, we address the integrated task assignment, path planning, and coordination problem applied for large-scale robot networks with the existence of uncertainties. In particular, a novel generalized conflict graph is designed which encodes the traveling time cost of the subsequent path planning result of each task-robot assignment and also includes the predicted path conflicts of each two assignments. An integrated optimization problem which aims to minimize the total traveling cost and potential path conflicts simultaneously is first formulated and then transformed into a linear programming instance to obtain the optimal solution. In particular, to satisfy the real-time requirement in large-scale systems, a greedy solution is presented which has the near-optimal performance but can decrease the computational complexity by orders of magnitude. The optimality, scalability, robustness, and efficiency of our approach are demonstrated by comprehensive comparisons with existing state-of-the-art approaches.