用退役电动车零件打造低成本智能除草机器人，让小农户也能用得起农业自动化。
TS-MAMP: A Remanufactured Agricultural Robot Powered by Second-Life EV Components and NMS-Free On-Device Weed Detection
- 回收退役电动车电机与电池，通过匹配和筛选实现低成本再利用
- 自研无NMS的YOLOv10n模型在除草数据集上达到80.87% [email protected]
- 整机成本低于450美元，支持现场部署与快速模块更换
农业4.0机器人虽提升作业效率，但对占全球主导的小规模农场而言仍过于昂贵。与此同时，大量退役低速电动车（LSEV）动力系统仍具功能价值却常被破坏性回收。本文提出TS-MAMP（伸缩套筒模块化农业移动平台），基于减少、再利用、回收的循环经济原则构建。将退役48V无刷直流（BLDC）轮毂电机通过反电动势匹配，并对健康度60%-80%的铅酸电池模块进行主动均衡，电压偏差控制在100mV以内。上述回收组件使动力总成与底盘物料成本降低约60%，降至450美元以下（不含感知与除草模块）。桁架式车架承重≥200kg，履带宽度可在1200-2000mm间连续调节，模块更换时间≤5分钟。采用无非极大值抑制（NMS-free）的YOLOv10n检测器，结合一致双分配训练与负样本学习，在Wanxi Crop-Weed数据集上实现80.87% [email protected]（58.41% [email protected]:0.95），并通过FP16 TensorRT部署于Jetson Nano，验证了设备端推理可行性。结果表明，经适度筛选后退役电动车部件可重构成经济、智能的农业机器人，为商业化自动化未覆盖的小农户提供可行的再制造路径。
原文摘要 · Abstract (English)
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 >=200 kg static load, continuously adjustable track width from 1200 mm to 2000 mm, and <=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% [email protected]: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.
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