Omniveyor: An Assembled Logistics Sorting System Powered by Reinforcement Learning

To improve logistics efficiency, smart logistics sorting is an inevitable trend in logistics development. Existing smart logistics sorting systems suffer from high construction costs or limited scalability. To solve these problems, we design a brand-new two-dimensional conveyor system called Omniveyor, which transports and sorts high-density packages within a limited space. It is assembled from multiple repetitive square conveyor modules, achieving the goal of cost-effectiveness and easy maintenance. To realize automatic sorting, we model the planning problem on Omniveyor and propose a scheduling strategy named MMPPO by reinforcement learning. Unlike traditional path planning, MMPPO assigns actions to modules rather than packages, which reduces scheduling overhead in high-throughput scenarios. Furthermore, we develop a simulation environment to inspect the effectiveness of our method, which solves an intractable package-following problem that has plagued simulation implementation in this field. Experimental results show that MMPPO outperforms baselines in terms of throughput and overall consumption at high densities. Besides, we implement a physical prototype of Omniveyor to validate its feasibility. Note to Practitioners - The motivation of the paper is to solve the sorting problems in the logistics system. Existing logistics sorting systems often have problems such as low sorting efficiency and high costs, making them unsuitable for small-sized warehouses. In this paper, we present a modular 2D desktop logistics system that is both scalable and efficient for sorting. We mathematically describe the platform package transportation process and propose an algorithm that addresses scheduling and planning problems, capable of continuous planning under pipeline input. Preliminary simulation tests indicate that our approach is feasible, and we have also built a small-scale prototype. In future research, we will further expand the scale of the platform and conduct research.