TS-MAMP：基于退役电动汽车部件和无NMS设备端杂草检测的再制造农业机器人
TS-MAMP: A Remanufactured Agricultural Robot with Second-Life EV Components and NMS-Free On-Device Weed Detection
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中文总结 AI 辅助
该研究基于退役电动汽车部件开发了平价再制造农业机器人TS-MAMP，采用无NMS的YOLOv10n检测器实现设备端杂草检测，为小农户提供了低成本AI农业自动化方案。
中文摘要 AI 辅助
农业4.0机器人系统提升了田间作业效率，但对占全球农业主导地位的分散小农户而言，其资本投入过高。与此同时，越来越多退役的低速电动汽车（LSEV）动力总成仍具备机电功能价值，却被破坏性回收。本文提出TS-MAMP（伸缩套模块化农业移动平台），一款遵循减量化、再利用、再循环（3R）循环经济原则的再制造机器人。退役的48V无刷直流（BLDC）轮毂电机通过反电动势匹配配对，健康状态（SOH）为60%-80%的铅酸电池模块在模块间电压偏差≤100mV的范围内主动均衡。这些复用部件使动力总成与底盘的物料清单（BOM）成本降低约60%，降至450美元以下（不含感知与除草模块）。桁架底盘提供≥200kg的静态负载，轮距可在1200mm至2000mm之间连续调节，模块更换时间≤5分钟。一款无NMS（非极大值抑制）的YOLOv10n检测器，采用一致双分配训练与负样本学习，在皖西作物杂草数据集上达到80.87%的平均精度均值（mAP）@0.5（58.41% mAP@0.5:0.95），并通过FP16精度的TensorRT部署在Jetson Nano上，验证了设备端推理的可行性。TS-MAMP表明，退役电动汽车部件经适度筛选后可被重新设计为具备AI功能的平价农业机器人，为商业自动化未覆盖的小农户田间作业开辟了一条再制造路径。
英文摘要
Agriculture 4.0 robotic systems improve field efficiency yet remain too capital-intensive for the fragmented smallholdings that dominate global agriculture. Meanwhile, a growing number of retired low-speed electric-vehicle (LSEV) powertrains retain functional electromechanical value but are destructively recycled. This paper presents TS-MAMP (Telescopic-Sleeve Modular Agricultural Mobile Platform), a remanufactured robot built under 3R (reduce, reuse, recycle) circular-economy principles. Retired 48 V brushless-DC (BLDC) hub motors are paired via back-EMF matching, and lead-acid battery modules screened at 60%-80% state of health are actively balanced within a 100 mV inter-module voltage deviation. Together, these reused components reduce the powertrain-and-chassis BOM cost by approximately 60%, to below USD 450 (perception and weeding modules excluded). The truss chassis provides at least 200 kg static load, continuously adjustable track width from 1200 mm to 2000 mm, and no more than 5-minute module changeover. An NMS-free (non-maximum-suppression-free) YOLOv10n detector with consistent dual-assignment training and negative-sample learning achieves 80.87% mean average precision (mAP)@0.5 (58.41% mAP@0.5:0.95) on the Wanxi Crop-Weed dataset, and is deployed via FP16 TensorRT on a Jetson Nano, confirming on-device inference feasibility. TS-MAMP demonstrates that retired EV components, under modest screening, can be re-engineered into affordable, AI-enabled agricultural robots--opening a remanufacturing pathway for the smallholder fields that commercial automation leaves unserved.
