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. Note to Practitioners—With the development of artificial intelligence techniques, large-scale autonomous robot networks are increasingly used in the logistic warehouses, unmanned container terminals, and intelligence transportation systems. This article considers the large-scale networks with hundreds or even thousands of unmanned robots which are implemented in lifelong transportation systems with uncertainties existed in practical execution process. Our main concept is to simultaneously minimize the total time cost of all the tasks and the potential motion conflicts among all the robots in the subsequent execution stage, thus alleviating robot congestions, balancing traffic distributions, increasing system efficiency, and improving the robustness and scalability. Lifelong simulations with thousand robots illustrate that our approach can reduce more than 30% of the time steps consumed for coordinating robot motion conflicts, and in the meantime, the throughput, and overall system efficiency are improved. However, simulation results show that the system improvement decreases in the presence of extreme high uncertainties (such as temporary motion and communication failures of the robot), due to the inaccurate conflict prediction in the integrated optimization stage. Our future work includes the deep-learning-based traffic evolution prediction and the online reallocation and planning in highly dynamic scenarios.