In this study, I address the challenges associated with strawberry harvesting, such as high labor intensity and inefficient manual picking, by designing and implementing a novel end effector for a strawberry picking machine. The machine is envisioned as a human-operated辅助采摘 pushing cart that integrates advanced mechanisms for cutting, gripping, transporting, and collecting strawberries without damage. The core innovation lies in the end effector, which combines a blade and synchronous belts to perform seamless operations. This article details the overall design, key component analysis, and experimental validation, with a focus on多次体现 the end effector’s role in enhancing efficiency. I employ finite element analysis, kinematic calculations, and tabular summaries to substantiate the design. The goal is to provide a验证手段 for automated strawberry harvesting that reduces reliance on manual labor.
The strawberry picking cart primarily consists of rubber wheels, a collection mechanism, a采摘机械手臂 with multiple degrees of freedom, and the main body. I adopt a Core-XY mechanism for precise planar movement, supplemented by a ball screw-driven vertical axis. This configuration allows the end effector to定位草莓梗 accurately based on machine vision input. The technical specifications include overall dimensions of 900 mm × 320 mm × 700 mm and a single-stop picking space of 600 mm × 400 mm × 250 mm. The design prioritizes portability for use between strawberry rows, with human pushing enabling easy relocation. The end effector is mounted on a multi-axis system that提供采摘所需的位置和姿态, ensuring adaptability to various strawberry layouts.
The ball screw drive mechanism for the Z-axis movement utilizes a 42-step motor linear slide with a lead of 5 mm and an effective stroke of 400 mm. This choice minimizes energy consumption while providing precise vertical positioning. The motion简图 illustrates the screw-nut assembly, where the displacement $\Delta z$ is related to the motor rotation angle $\theta$ by:
$$\Delta z = \frac{P}{360^\circ} \cdot \theta$$
where $P$ is the lead (5 mm). For a desired vertical movement of 250 mm, the required motor rotation can be calculated accordingly. This mechanism ensures stable support for the end effector during operation.
Key to the machine’s functionality is the conveyor belt system for草莓运输. To prevent damage, I select a GM37-520微型直流减速电机 (DC 12V, reduction ratio 1:50)驱动同步轮, with large and small gears having 32 and 20 teeth, respectively, yielding a transmission ratio of 8:5. The conveyor belts are made of soft材料 to cushion strawberries. The主动轴 diameter is 5 cm, and the distance between主动轴 and从动轴 is 0.5 m. Assuming a负载转速 of 100 rpm, the outer line speed $v$ of the conveyor belt is derived as:
$$v = \frac{100 \times 2\pi}{60} \times \frac{5}{8} \times \frac{5}{2 \times 100} = 2.6 \, \text{cm/s}$$
This slow speed ensures gentle handling, critical for maintaining草莓完整性. The conveyor system integrates with the end effector to form a continuous workflow.
The end effector is the centerpiece of this design, responsible for cutting and gripping草莓茎. It comprises two synchronized belts that rotate in opposite directions to clamp the strawberry stem. A front-mounted blade severs the stem during clamping, after which the belts transport the strawberry backward to a collection point. The end effector has two additional degrees of freedom controlled by舵机: pitch and roll angles. The pitch angle is adjusted manually based on垄坡度, while the roll angle is determined by machine vision for precise alignment. This configuration allows the end effector to approach strawberries from optimal angles, minimizing damage. The stress analysis of the end effector structure is crucial for durability. I apply a non-uniform external load on the front end, with固定面 bolted to the舵机支架. Using finite element analysis, the应变和应力分布 are evaluated to ensure minimal deformation. The maximum stress $\sigma_{\text{max}}$ should satisfy:
$$\sigma_{\text{max}} \leq \frac{S_y}{N}$$
where $S_y$ is the yield strength of the material (e.g., aluminum alloy) and $N$ is the safety factor. The analysis confirms that the end effector can withstand repeated operations without failure.
The collection mechanism involves a柔性斜面 made of smooth fabric that guides strawberries from the end effector onto the conveyor belt. Support rods stabilize the斜面, ensuring a smooth transition. The conveyor belt, powered by the减速组电机, transports strawberries to a collection box system at the rear of the cart. This system features six removable小收集盒 arranged in a vertical stack. When the bottom box is full, it is manually extracted and placed on top, allowing the next box to slide down for continuous collection. This循环往复 process enhances efficiency by minimizing interruptions. The design parameters for the collection boxes are optimized based on草莓大小, with each box holding approximately 20-30 strawberries to prevent overcrowding.
To validate the design, I constructed a prototype and conducted experiments in a simulated垄地 environment. The草莓大小 parameters were measured to inform the end effector’s gripping and cutting mechanisms. The table below summarizes the草莓大小 data used for calibration:
| Parameter | Maximum | Minimum | Average |
|---|---|---|---|
| Weight (g) | 18.48 | 13.76 | 16.12 |
| Transverse Width (mm) | 42.25 | 26.33 | 34.29 |
| Longitudinal Length (mm) | 52.28 | 34.42 | 43.35 |
| Stem Diameter (mm) | 1.88 | 1.26 | 1.57 |
These values guided the adjustment of the end effector’s belt spacing and blade position to accommodate variability. The cutting force required for the草莓茎 can be estimated using the shear stress formula:
$$F_c = \tau \cdot A$$
where $\tau$ is the shear strength of the stem material (assumed to be similar to plant fibers, approximately 10 MPa), and $A$ is the cross-sectional area of the stem. For an average diameter of 1.57 mm:
$$A = \pi \left(\frac{d}{2}\right)^2 = \pi \left(\frac{1.57 \times 10^{-3}}{2}\right)^2 \approx 1.94 \times 10^{-6} \, \text{m}^2$$
Thus, $F_c \approx 10 \times 10^6 \times 1.94 \times 10^{-6} = 19.4 \, \text{N}$. The end effector’s blade and motor are designed to exceed this force to ensure clean cuts.
The experiments involved multiple picking trials to assess the end effector’s performance. The table below presents the results from five groups, where target strawberries were identified and harvested:
| Group | Strawberries Picked | Target Strawberries | Time (s) | Success Rate (%) |
|---|---|---|---|---|
| 1 | 88 | 99 | 432 | 90 |
| 2 | 58 | 66 | 358 | 82 |
| 3 | 90 | 106 | 460 | 88 |
| 4 | 61 | 72 | 360 | 90 |
| 5 | 72 | 79 | 386 | 89 |
The average picking speed is calculated as the total strawberries picked divided by the total time. Let $N_{\text{total}} = 88 + 58 + 90 + 61 + 72 = 369$ strawberries and $T_{\text{total}} = 432 + 358 + 460 + 360 + 386 = 1996$ seconds. The average speed $v_{\text{pick}}$ is:
$$v_{\text{pick}} = \frac{N_{\text{total}}}{T_{\text{total}}} = \frac{369}{1996} \approx 0.185 \, \text{strawberries/s} = 666 \, \text{strawberries/h}$$
However, considering the target-based approach, the effective success rate is the average of the group rates: $(90 + 82 + 88 + 90 + 89)/5 = 87.8\%$. This indicates that the end effector reliably harvests strawberries with minimal damage, meeting the design objectives. The end effector’s integration with the conveyor and collection systems ensures a seamless workflow, as strawberries are immediately transported after cutting, reducing handling time.
Further analysis of the end effector’s dynamics involves the torque required for the belts to grip and transport strawberries. The friction force $F_f$ between the belts and the草莓茎 must克服重力 and inertial forces. For a strawberry of average mass $m = 16.12 \, \text{g} = 0.01612 \, \text{kg}$, the gravitational force $F_g = m \cdot g = 0.01612 \times 9.81 \approx 0.158 \, \text{N}$. Assuming a coefficient of friction $\mu = 0.5$ between the belt material and the stem, the minimum normal force $F_n$ required is:
$$F_n = \frac{F_g}{\mu} = \frac{0.158}{0.5} = 0.316 \, \text{N}$$
The motor torque $\tau_m$ for the synchronous belts can be derived from the belt tension and pulley radius $r = 2.5 \, \text{cm} = 0.025 \, \text{m}$:
$$\tau_m = F_n \cdot r = 0.316 \times 0.025 = 0.0079 \, \text{Nm}$$
This low torque requirement allows the use of微型电机, reducing power consumption. The end effector’s design ensures that the belts apply consistent pressure without crushing the草莓, thanks to the compliant材料 selection.
The collection box system’s efficiency is quantified by the time saved in manual handling. If each小收集盒 holds $n = 25$ strawberries on average, the number of boxes filled per hour $B_{\text{hour}}$ given the picking speed is:
$$B_{\text{hour}} = \frac{v_{\text{pick}} \times 3600}{n} = \frac{666 \times 3600}{25 \times 3600} = 26.64 \, \text{boxes/h}$$
In practice, the manual exchange of boxes takes approximately 10 seconds per cycle, so the overall productivity remains high. This modular collection approach enhances the machine’s usability in extended harvesting sessions.
In terms of structural integrity, the end effector undergoes vibrational analysis during operation. The natural frequency $f_n$ of the end effector assembly should be higher than the excitation frequencies from motor vibrations to avoid resonance. For a simplified model as a cantilever beam, $f_n$ can be approximated as:
$$f_n = \frac{1}{2\pi} \sqrt{\frac{k}{m}}$$
where $k$ is the stiffness of the end effector structure and $m$ is its effective mass. Using finite element results, I ensure that $f_n > 50 \, \text{Hz}$, which is above typical motor operating frequencies, thus maintaining stability.
The machine vision system for guiding the end effector relies on stereo cameras to locate草莓梗 in 3D space. The positioning accuracy $\Delta p$ directly impacts the end effector’s success rate. Assuming a pixel error $\epsilon$ and a working distance $D = 300 \, \text{mm}$, the spatial error can be expressed as:
$$\Delta p = D \cdot \tan(\theta_{\text{pixel}}) \approx D \cdot \frac{\epsilon}{f}$$
where $f$ is the focal length in pixels. With $\epsilon = 2$ pixels and $f = 1000$ pixels, $\Delta p \approx 300 \times \frac{2}{1000} = 0.6 \, \text{mm}$. This sub-millimeter accuracy allows the end effector to engage草莓茎 precisely, minimizing missed cuts or damage.
Energy consumption is another critical factor. The total power $P_{\text{total}}$ of the machine includes motors for the end effector, conveyor, and movement axes. For the end effector motor operating at voltage $V = 12 \, \text{V}$ and current $I = 0.5 \, \text{A}$, the power $P_{\text{ee}} = V \cdot I = 6 \, \text{W}$. Similar calculations for other components yield an estimated $P_{\text{total}} \approx 30 \, \text{W}$. This low power demand makes the machine suitable for battery operation, enhancing field applicability.
To further optimize the end effector, I consider the material properties for the cutting blade. Using stainless steel with hardness HRC 55 ensures longevity and sharpness. The blade angle $\alpha$ is set to 30 degrees to balance cutting efficiency and force. The cutting speed $v_c$ relative to the草莓茎 is synchronized with the belt speed to ensure clean severance. Given the belt speed $v_b = 2.6 \, \text{cm/s} = 0.026 \, \text{m/s}$, the cutting time $t_c$ for a stem diameter $d = 1.57 \, \text{mm}$ is:
$$t_c = \frac{d}{v_b} = \frac{1.57 \times 10^{-3}}{0.026} \approx 0.06 \, \text{s}$$
This rapid cutting action reduces the time the草莓 is subjected to stress, preserving quality.
The end effector’s design also incorporates safety features to prevent operator injury. The blade is enclosed within the belt mechanism, and sensors detect obstructions, halting operation if needed. This aligns with industrial standards for agricultural machinery.
In conclusion, I have presented a comprehensive design and implementation of a novel end effector for a strawberry picking machine. The end effector excels in cutting, gripping, and transporting strawberries with minimal damage, as validated through experiments. Key innovations include the synchronized belt-blade mechanism, modular collection system, and integration with machine vision. The analysis using formulas and tables underscores the technical robustness, with an average picking success rate of 87.8% and efficient handling. This end effector-based machine offers a practical solution to labor-intensive strawberry harvesting, with potential for scalability to other soft fruits. Future work may focus on autonomous navigation and advanced AI for ripeness detection, further enhancing the end effector’s capabilities.