SBIR Phase I: Developing an Automated Outbound Packing System

这个小型企业创新研究(SBIR)第一阶段项目的更广泛的影响/商业潜力是使包裹能够快速高效地装载到从小型送货车到海运集装箱的各种航运集装箱中。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具有扩展的机器人能力的完全自主系统迈出的一步，以进一步提高客户出境运输的效率和速度。每秒钟有超过3,000个包裹被运送。然而，在美国，每四辆卡车中就有一辆是空的，两辆车的载客量低于50%，只有一辆卡车的载客量超过50%。初步预测表明，正在开发的技术可以降低20%的卡车运输成本，降低70%-80%的装载成本，减少30%的装载时间，同时满足运输旺季的需求。总体而言，提高包裹运输密度将减少温室气体排放(每辆卡车400吨/年)，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组织竞争。这个小型企业创新研究(SBIR)第一阶段项目将专注于改进装箱算法，以最大限度地减少出境运输集装箱中的空白空间。三维装箱问题(3D-BPP)是一个经典的非线性规划(NP)难题，已被研究了几十年。为了解决这一问题，人们正在开发一种有效且易于实现的受限、量子加速、深度强化学习模型。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出现在状态序列末尾的变量的期望值。深度强化学习(DRL)通过允许学习的状态值来指导随后改变环境状态的动作来扩展该技术。概念验证评估表明，学习的策略明显优于最先进的方法。该项目的成果成功指标是90%的利用率、不到24小时的模型培训时间，以及任何给定数据集的2500个包裹/小时。该基金会将通过整合许多独特的盒子尺寸，在更广泛的情况下探索模型性能(例如，前瞻和堆叠参数，通用处理单元(GPU)与量子训练)，并开发机器人抓取器来制定算法输出，以扩大该基金会。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.