SBIR Phase I: Developing an Automated Outbound Packing System

这项小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力是使包裹能够快速有效地装载到集装箱中，从小型送货车到海运集装箱。该项目将重点展示算法方法和机器人开发的可行性。这项技术是朝着创建一个完全自主的系统迈出的一步，该系统具有扩展的机器人功能，可以进一步提高客户出站运输的效率和速度。每秒钟有超过3000个包裹被运送出去。但是在美国，每4辆卡车中就有1辆是空的，2辆不到50%，只有1辆超过50%。初步预测表明，正在开发的技术可以将卡车运输成本降低20%，将装货成本降低70-80%，将装货时间减少30%，同时满足运输旺季的需求。总体而言，增加包裹运输的密度将减少温室气体排放（每辆卡车每年400吨），减少交通拥堵，并通过降低物流和运输运营成本使小型企业能够与大型企业竞争。这项小企业创新研究（SBIR）第一阶段项目将重点推进一种集装箱包装算法，以最大限度地减少出站集装箱的空隙。三维装箱问题（3D-BPP）是一个经典的非线性规划（NP）难题，已经被研究了几十年。为了解决这个问题，一种有效且易于实现的约束、量子加速、深度强化学习模型正在开发中。蒙特卡罗树搜索是一种无监督的启发式搜索算法技术，其中学习代理学习预测在一系列状态结束时出现的变量的期望值。深度强化学习（DRL）扩展了这种技术，允许学习到的状态值指导随后改变环境状态的行为。概念验证评估表明，学习策略明显优于最先进的方法。这个项目的结果成功指标是90%的利用率，低于24小时的模型训练时间，以及任何给定数据集的2500个包裹/小时。这个基础将通过整合许多独特的盒子尺寸来扩展，探索模型在更广泛的情况下的性能（例如，前瞻性和堆叠参数，通用处理单元（GPU）与量子训练），以及开发机器人抓取器来实现算法输出。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to enable the fast and efficient loading of parcels into shipping containers ranging from small delivery vans to maritime shipping containers. This project will focus on demonstrating the feasibility of an algorithmic approach and robotic development. The technology is a step towards creating a fully autonomous system with expanded robotics capability to further enhance the efficiency and speed of outbound shipping for customers. Over 3,000 parcels are shipped every second. However, in the U.S., one out of every four trucks is empty, two are less than 50% filled, and only one is filled over 50% capacity. Initial projections indicate that the technology under development could decrease trucking costs by 20%, reduce loading costs by 70-80%, and decrease loading time by 30%, all while meeting the demands of peak shipping seasons. Overall, increasing the density of parcel shipping will reduce greenhouse gas emissions (400 tons/per truck/per year), reduce traffic congestion, and enable smaller businesses to compete with large organizations by reducing their logistics and shipping operating costs.This Small Business Innovation Research (SBIR) Phase I project will focus on advancing a bin packing algorithm to minimize void space in outbound shipping containers. The 3-Dimentional Bin Packing Problem (3D-BPP) is a classic Nonlinear Programming (NP)-hard problem that has been studied for decades. To solve the problem, an effective and easy-to-implement constrained, quantum accelerated, deep reinforcement learning model is being developed. Monte Carlo Tree Search is an unsupervised, heuristic search algorithm technique in which the learning agent learns to predict the expected value of a variable occurring at the end of a sequence of states. Deep reinforcement learning (DRL) extends this technique by allowing the learned state-values to guide actions which subsequently change the environment state. A proof-of-concept assessment showed that the learned strategy meaningfully outperforms the state-of-the-art methods. Outcome success metrics for this project are 90% utilization rate, sub 24 hours of model training time, and 2500 parcels/hour for any given data set. This foundation will be expanded by integrating many unique box sizes, exploring model performance in the face of broader circumstances (e.g., lookahead and stacking parameters, General Processing Unit (GPU) vs quantum training), and developing of a robotic gripper to enact algorithmic output.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.