Hybrid Gripper for Tomato Harvesting

• Hybrid gripper for tomato harvesting is a robotic end-effector that combines soft and rigid materials for precise, gentle manipulation in agricultural settings.
• It integrates distributed sensor arrays and adaptive control algorithms to classify grasp states in real-time and minimize damage to delicate tomatoes.
• Modular design with auxetic lattices and variable stiffness components enables scalable, efficient adaptation to varying tomato sizes and field conditions.

A hybrid gripper for tomato harvesting is a robotic end-effector engineered by integrating both soft and rigid materials along with adaptive kinematics, distributed sensing, and intelligent control algorithms to enable gentle, conformal, and robust manipulation of tomatoes, particularly in the variable, cluttered, and unstructured conditions characteristic of agricultural environments. The design, modeling, and operational modalities of hybrid grippers are informed by both the unique biomechanical challenges of tomato handling (fragility, compliance, stem attachment, occlusion by foliage) and the need for efficiency and robustness in practical crop harvesting scenarios.

1. Structural Architectures and Material Integration

Hybrid gripper architectures combine rigid exoskeletons or linkages—typically 3D-printed with polymers such as PLA—to ensure positional accuracy and load support, with integrated soft internal lattices or finger pads made from thermoplastic polyurethanes (TPU), silicone elastomers, or auxetic metamaterials for compliant, shape-conformal grasping (Ansari et al., 11 Oct 2025, Ansari et al., 21 Dec 2024, Zhu et al., 2021, Joseph et al., 2022). Notable structural motifs include:

• Auxetic Lattices: Re-entrant honeycomb or similar negative Poisson’s ratio structures embedded within the fingers, expanding laterally under compression, thereby distributing contact pressure uniformly while providing a caging effect around the fruit (Ansari et al., 11 Oct 2025, Ansari et al., 21 Dec 2024).
• Caging Geometries: Arrangements such as six-finger caging rings achieve whole-object encapsulation, adapting to irregular tomato shapes without excessive point loads.
• Variable Stiffness Components: Use of passive or active elements (pneumatic rings, jamming chambers, or layer-jamming tendon-reinforced fingers) to dynamically tune finger compliance and adapt to fruit firmness and size (Zhu et al., 2021, Liu et al., 2022, Tran et al., 8 Oct 2024).

The rigid-soft integration is actuated using mechanisms such as Scotch-yoke linkages for synchronized motion, multi-DOF pneumatic or tendon-driven joints for abduction/adduction and flexion/extension, and in some cases cam-driven telescoping fingers with independent or coordinated motion (Zhu et al., 2021, Velasquez et al., 13 Aug 2024).

2. Sensing, Perception, and Grasp State Classification

Hybrid grippers for tomato harvesting utilize distributed sensing architectures to monitor and adapt grasp interactions in real-time. Typical sensor arrays include:

• Piezoresistive Tactile Arrays: Embedded along the contact surfaces, these measure local stress distributions at sub-centimeter spatial granularity. Signals are processed using statistical filters (moving mean/variance) to detect contact events, obstacle interference, and instability (Zhou et al., 2021).
• IMUs and Tension Sensing: Inertial Measurement Units (IMUs) on fingers/body detect slip or dynamic perturbations, while force-sensitive resistors or custom tension sensors measure the pull force during detachment, enabling discrimination between successful picks and failures (Walt et al., 15 Aug 2025).
• Proximity and Visual Feedback: Integrated IR reflectance and RGB camera modules (“eye-in-hand” or collocated within the gripper core) provide occlusion-robust validation of fruit presence, size, and pick success, supporting closed-loop visual servoing even in high-clutter environments (Koe et al., 31 Jan 2025).

Grasp-state classification algorithms—Random Forests or LSTM networks—operate on fused sensor data to identify states such as slip, no slip, successful pick, and failed grasp, reaching up to 100% accuracy with minimal viable sensor suites (e.g., IMU plus tension sensor), thereby enabling adaptive corrective control and reducing repeated failures (Walt et al., 15 Aug 2025).

3. Kinematic Analysis, Force Modeling, and Control

Hybrid gripper kinematics are modeled at multiple abstraction levels:

• Rigid Link + Soft Lattice Kinematics: Motion/force transfer from the servo-driven Scotch-yoke mechanism or pneumatic actuation is analytically modeled via geometric and virtual work principles; for example,

$$T = P \cdot l_{DM} \cdot \sin(\xi) \cdot \frac{\delta \xi}{\delta \theta} + P \cdot r \cdot \sin(\theta)$$

where $T$ is actuator torque, $P$ the grasp force, and geometric parameters as defined per gripper specifics (Ansari et al., 21 Dec 2024).

• Auxetic Structure Compliance: The deformation response and local curvature are quantified with DIC and finite element analysis, while curvature is expressed as

$k = \frac{|y''|}{(1 + (y')^2)^{3/2}}$

guiding the choice of lattice orientation for contact uniformity versus maximum deformation (Ansari et al., 11 Oct 2025).

• Actuation Modes and Control: Mode switching—for power grasp, ab/adduction, or holding—is implemented by distributing pressure or tendon tension across fingers, with PID or impedance controllers modulating grasping force to avoid excessive tomato compression. Soft-rigid transmission models (e.g., for cam-driven fingers or suction-finger tandem actuation) inform safe operational envelopes by constraining force ratios and motor torques (Zhu et al., 2021, Velasquez et al., 13 Aug 2024).

4. Perception-Driven Manipulation, Vision, and Grasp Strategy

Advanced vision systems coupled with learning-based grasp planning algorithms are essential for selective picking in cluttered, highly variable environments:

• 3D Fruit Localization: Dual-camera architectures—global RGB-D camera for initial detection, local end-effector camera for closed-loop approach—enable robust localization and tracking despite occlusion or depth noise. Real-time object detectors (YOLOv7, Mask R-CNN) and segmentation networks identify individual tomatoes, estimate their 3D pose, and distinguish ripened from unripe fruit (Ansari et al., 21 Dec 2024, Koe et al., 31 Jan 2025).
• Keypoint Detection and Trajectory Planning: Semantic segmentation and keypoint detection localize critical points (body and pedicel) for precise targeting and stem cutting. Trajectory optimization (using PSO or similar algorithms) ensures collision-free arm and gripper motion, adaptable to intra-row or cluster occlusions (Ansari et al., 21 Dec 2024).
• Grasp Pose Ranking: Utility-based ranking functions

$U(p) = \sum_{i=1}^n w_i f_i(p)$

assign weights $w_i$ to features $f_i(p)$ (e.g., approach clearance, stability, local clutter) with online learning for dynamic scenario adaptation (Bent et al., 2023).

5. Experimental Validation and Performance Benchmarks

Hybrid grippers for tomato harvesting are validated with systematic experimental protocols encompassing bench tests, field trials, and simulation-to-real transfer:

• Force and Compliance: Controlled grasping of artificial and real tomatoes quantifies mean/peak forces (e.g., contact forces of 8.5–9.6 N per finger depending on auxetic orientation; grasp strengths of ~40 N in suction+finger tandem mode), ensuring compliance thresholds avoid bruising (Ansari et al., 11 Oct 2025, Velasquez et al., 13 Aug 2024).
• Throughput and Efficiency: Metrics such as grasping efficiency (e.g., $\eta > 150\%$ for multi-object grasps where hybrid modes reduce the number of actions below the number of objects), pick cycle time (e.g., $\sim$11–24 s per harvest), and success rates (88–100%) in field settings are reported (Liu et al., 2022, Koe et al., 31 Jan 2025, Ansari et al., 21 Dec 2024).
• Deformation and Stability: DIC and FEA analyses validate that auxetic configurations (particularly at 0°, 45°, and 60° orientations) yield specific trade-offs in grasp conformity, deformation capacity (e.g., strains up to 0.11), and energy efficiency (via reduced steady-state torque profiles) (Ansari et al., 11 Oct 2025).
• Robustness in Clutter: Tandem suction-finger and adaptive caging designs achieve $>96\%$ success in occlusion-laden scenarios, reflecting the system's ability to operate reliably in dense vegetation (Velasquez et al., 13 Aug 2024).

6. Modularity, Scalability, and Customization for Agricultural Deployment

Modular design paradigms allow hybrid grippers to adapt to task and crop variability:

• Finger Libraries and Parametric Design: Re-configurable jaws, modular finger geometries, and parameterized auxetic lattices enable rapid customization to accommodate tomato size, shape, and cluster topology (Droukas et al., 2022).
• Sensor Integration and Control Simplification: Studies identify minimal sensor suites (e.g., IMU+tension) that balance classification accuracy with hardware complexity, critical for scaling to multi-row and multi-arm deployment (Walt et al., 15 Aug 2025).
• Environmental Hardening: Considerations for sealing pneumatic lines, ruggedizing soft components, and calibrating vision under varying humidity, temperatures, and lighting, are emphasized to ensure operational robustness in field applications (Zhu et al., 2021, Droukas et al., 2022).

7. Impact, Applications, and Future Directions

Hybrid grippers for tomato harvesting represent a confluence of advances in soft robotics, biomimetic design, sensing, and AI-driven control:

• Selectivity and Precision: Integration of adaptable caging, compliant fingers, and vision-driven planning enables selective, damage-minimized harvesting, supporting higher produce quality and reduced post-harvest losses (Ansari et al., 11 Oct 2025, Ansari et al., 21 Dec 2024).
• Automation Synergy: The combination with high-DOF, obstacle-avoiding robotic arms, visual servoing, and multi-modal sensory fusion provides a path to full-cycle crop automation in high-density plantings and cluttered greenhouses (Chen et al., 29 Jul 2024, Koe et al., 31 Jan 2025).
• Standardization and Benchmarking: Progress toward systematic benchmarking using quantitative performance metrics (force, deformation, efficiency, UPH, success rate) establishes a comparative foundation for next-generation designs (Shoushtari, 2023).

Future research directions include the development of fully integrated soft-rigid grippers with embedded tactile and force sensing at the materials level, the use of auxetic and metamaterial tuning for tailored compliance, the deployment of reinforcement learning for adaptive control policies, and further advances in modularity and environmental resilience (Ansari et al., 11 Oct 2025, Shoushtari, 2023, Liu et al., 2022). These trends will accelerate the deployment of precision harvesting systems capable of addressing labor shortages and quality control demands in modern agriculture.