Search Results (144)

Existing robot end-effectors design methods lack structured domain prior knowledge support and have insufficient interaction with the environment, making it difficult to guarantee the accuracy of the design results. An adaptive design method is proposed that deeply embeds domain knowledge of end effectors into the design process, treats key design parameters as environmental variables, and optimizes them adaptively through reinforcement learning algorithms in perception and feedback. In a simulation environment constructed by combining a knowledge graph, a two-finger translational gripper is used as an example robot end-effector to acquire target data via sensors, and reinforcement learning is used to adaptively optimize the gripper’s key parameters. Experiments are conducted on a simulation platform with three typical tasks, yielding the optimal parameter range. Compared to the proximal policy optimization (PPO) algorithm, which has no prior knowledge input, the knowledge graph embedding proximal policy optimization (KGPPO) algorithm improves the average reward for gripper length and gripper force by 63.96% and 43.09%, respectively, for grasping eggs. The KGPPO algorithm achieves the highest average reward and the best stability compared with other algorithms. Experiments show that this method can significantly improve the efficiency, stability, and accuracy of design parameter optimization. Full article

Fast and universal grasping remains a critical challenge for robotic hands operating in unstructured and industrial environments. Conventional pin-array-based robotic hands exhibit strong adaptability to objects with diverse geometries, yet their grasping speed is often limited by centralized motor-driven actuation. To address this limitation, this paper presents the development of a fast-acting cluster-tube self-adaptive robotic hand (CTSA-FA hand), which transforms traditional active actuation into a passive energy-storage-and-release-driven grasping mechanism. A spring-cam-based structure is introduced to enable rapid energy release during the grasping phase, significantly reducing the gathering time. A theoretical model of the CTSA-FA hand is established, including cam trajectory planning and mechanical analysis, to guide parameter design and performance optimization. A physical prototype is developed and experimentally validated. Experimental results demonstrate that the proposed CTSA-FA hand can complete a grasp-release frequency of approximately 3 Hz, corresponding to a grasping time of about 0.25 s per cycle, while maintaining robust adaptive grasping performance. These characteristics indicate that the proposed design is well suited for applications requiring fast and universal grasping, particularly in intelligent mining equipment and industrial automation scenarios. Full article

In nature, structures such as earwig wings and mimosa leaves exhibit remarkable folding and unfolding capabilities. Inspired by these biological mechanisms, this work investigates soft foldable and torsional actuators based on Kresling crease pattern, fabricated using soft TPE 85A material through 3D printing. These actuators enable both foldable grasping and torsional motions. An analytical geometric model is developed to characterize the relationship between structural parameters and the inscribed circle area of a single-layer soft actuator, thereby elucidating their influence on contraction magnitude and relative deflection angle. Treating the soft actuator as an equivalent spring system, a mechanical model relating vacuum pressure to contraction ratio is further established, revealing an approximately linear relationship. The actuators are subsequently integrated with suction cups to form two end-effectors, a foldable soft gripper and a torsional soft gripper, and mounted onto a UR5 robotic arm via a customized flange. Demonstration experiments show that the foldable gripper achieves gentle, adaptive grasping of diverse objects, while the torsional gripper replicates human-like twisting motion, such as opening a bottle cap. This study highlights the potential of Kresling-based soft grippers for practical deployment in automated production tasks, including precision assembly and fruit harvesting. Full article

Current multi-axis force sensors often rely on complex mechanical structures or arrays of discrete transducers, resulting in larger footprints, higher complexity, and limited scalability for compact applications such as robotic fingertips or wearable tactile interfaces. To address these limitations, this paper introduces a novel optical sensing approach that uses a top-layer patterned color surface and an array of color sensors to decouple and measure normal, shear, and torsional forces within a highly compact 15 × 15 mm footprint. The patterned surface functions as a visual encoding layer, where applied forces induce measurable, direction-dependent shifts in reflected color distribution. By deploying multiple color sensors in an array, each sensor captures localized color variations, enabling spatial reconstruction of both magnitude and direction of applied loads through differential color analysis. The sensor’s performance was validated through robotic gripper integration, where it successfully provided multi-axis force feedback and enabled adaptive gripping force adjustment to achieve robust and stable object manipulation. The experimental results confirm the system’s ability to effectively sensing 3D forces and torsion forces, and support closed-loop control in adaptive robotic grasping. This design presents a scalable, low-profile alternative to conventional multi-axis force sensors, suitable for integration into space-constrained robotic and haptic systems. Full article

This work presents a novel soft gripper concept featuring integrated force feedback and a compact, resource-efficient geometry. The gripper is designed to provide a low-cost, adaptable, and precise solution for manipulating delicate and irregularly shaped objects. By embedding force feedback directly into the structure, the system reliably detects contact and enables controlled, gentle gripping of fragile items. The design was developed for collaborative and assistive robotic applications, where safety and human–robot interaction are prioritized. The prototype is fabricated using consumer-grade 3D-printed components and employs a simple cable-driven actuation system. The hybrid soft–rigid architecture combines compliant fingers with a rigid, sensorized thumb, preserving the adaptive grasping characteristics of soft robotics while simplifying sensing integration and construction. A motor-based control mechanism synchronizes finger motion through cable traction, ensuring reliable and repeatable performance. Experimental evaluations demonstrate secure, damage-free handling across diverse object types, highlighting the gripper’s potential in assistive robotics, cobot environments, biomedical contexts, and other domains requiring safe and delicate manipulation. Full article

To address the challenges of the lack of specialized machinery adapted to traditional agronomic requirements, high labor intensity, and low efficiency in the planting of Ligusticum chuanxiong stalk segments (commonly known as Chuanxiong seed stalk or Lingzhong), a planting mechanism for the cutting of Chuanxiong seed stalk was developed in accordance with traditional agronomic requirements. A kinematic model of the gripping point was established, from which a plant spacing formula was derived. Based on the zero-speed planting principle, a cuttage planting scheme for Chuanxiong seed stalks was proposed, in which the gripper trajectory as well as the forward-tilt x_t and correction x_c were defined, and the decisive role of installation height on planting depth and the influence of driven-sprocket motion parameters on planting uprightness were elucidated. A 3D model and a DEM-MBD coupled simulation model were constructed to analyze planter–soil–seed interaction. A three-factor, three-level Box–Behnken experiment was conducted, and a response surface model was built and optimized using ‘Design-Expert’ software. The optimal parameters were a driven sprocket angular velocity of 0.654 rad/s, a rotation radius of 100.787 mm, and a release angle of 90.647°, yielding an average planting uprightness of 85.264°, with the corresponding x_t and x_c of 5.18 mm and 2.69 mm, respectively; the factor influence ranked as angular velocity > rotation radius > release angle. Seed–soil interaction analysis verified the mechanism’s feasibility and the accuracy of the theoretical models. Field tests showed average qualification rates of 87.13% for plant spacing, 96.01% for planting depth, and 90.41% for uprightness, with corresponding coefficients of variation of 4.37%, 2.95%, and 3.73%, indicating stable and reliable field performance. Full article

Robotic grippers play a crucial role in pick-and-place tasks, as their performance directly affects the robot’s operational efficiency, stability, and safety. In industrial applications, such as coal gangue sorting, the target objects have irregular shapes and sharp surfaces, which pose challenges to the gripper’s grasping ability. To solve these problems, an adaptive under-actuated gripper based on rope control is designed. The gripper is simple to control and combines the excellent features of both rigid and flexible grippers. To analyze the characteristics of the gripper, both mathematical analysis and holding force experiments are conducted. The results show that the gripper can generate a greater holding force when grasping larger objects with a constant input air pressure. Furthermore, irregularly shaped testing objects, including coal lumps and ores, are selected to conduct grasping experiments. The gripper achieves a 100% grasping success rate with a load of up to four times the object’s weight suspended beneath it and shows the ability to reliably grasp irregularly shaped objects in high-speed pick-and-place tasks with a payload of four times the object’s weight. Meanwhile, the gripper has a passive anti-collision ability due to the special outer contour of the distal finger when subjected to unexpected, sudden force. Full article

Soft grippers exhibit excellent adaptability in handling objects of various shapes. However, due to the large deformation and high compliance of their constituent materials, the integration of sensing capabilities has long been a major research challenge. Based on the swallowing-type soft gripper proposed in previous work, this study explores the gripper’s capability to perceive object information by leveraging the characteristic that the sealed cavity undergoes volume change due to compression by the object during swallowing, thereby altering the pressure of the internal fluid medium. By establishing the geometric configuration of the sealed cavity composed of elastic membranes, the volume-pressure variation sensing model during the object swallowing process was derived. The performance of this sensing method was tested, and the application of the fluid pressure sensing strategy in closed-loop control was demonstrated, including the classification of objects by shape and sorting by size. This work provides a solution for the object shape-adaptive swallowing-type soft gripper to achieve sensory grasping functionality. Full article

Intelligent fruit-picking robots have emerged as a promising solution to labor shortages and the increasing costs of manual harvesting. This review provides a systematic and critical overview of recent advances in three core domains: (i) vision-based fruit and peduncle detection, (ii) motion planning and obstacle-aware navigation, and (iii) robotic manipulation technologies for diverse fruit types. We summarize the evolution of deep learning-based perception models, highlighting improvements in occlusion robustness, 3D localization accuracy, and real-time performance. Various planning frameworks—from classical search algorithms to optimization-driven and swarm-intelligent methods—are compared in terms of efficiency and adaptability in unstructured orchard environments. Developments in multi-DOF manipulators, soft and adaptive grippers, and end-effector control strategies are also examined. Despite these advances, critical challenges remain, including heavy dependence on large annotated datasets; sensitivity to illumination and foliage occlusion; limited generalization across fruit varieties; and the difficulty of integrating perception, planning, and manipulation into reliable field-ready systems. Finally, this review outlines emerging research trends such as lightweight multimodal networks, deformable-object manipulation, embodied intelligence, and system-level optimization, offering a forward-looking perspective for autonomous harvesting technologies. Full article

This study presents a Design for Additive Manufacturing (DfAM)–driven redesign of an industrial robot vacuum gripper for Fused Deposition Modeling (FDM), focusing on the systematic transformation of a multi-part, machined aluminum assembly into a lightweight, support-minimized polymer component suitable for continuous industrial operation. Beyond a practical redesign, the work contributes a geometry-centered DfAM methodology that links internal channel topology, overhang control, and functional interfaces to manufacturability, vacuum performance, and cost efficiency. The development follows three iterative design revisions, progressing from a geometry-adapted baseline toward a fully DfAM-optimized solution. A key innovation is the introduction of support-free internal vacuum channels with triangular cross-sections, enabling complete elimination of soluble support material within enclosed cavities. This redesign reduces the internal vacuum volume by 44%, leading to faster vacuum response while maintaining functional suction performance. The optimized overhang angles, filleted load paths, and DfAM-compliant suction cup seats significantly reduce post-processing requirements and improve structural robustness. Experimental validation under industrial operating conditions confirms that the final design achieves reliable vacuum performance and mechanical durability. Compared to the original configuration, the optimized gripper demonstrates a substantial reduction in manufacturing complexity, with printing time reduced by approximately 50% and total part cost decreased by 26%, primarily due to eliminated tooling, reduced support material, and simplified post-processing. The presented results demonstrate that DfAM principles, when applied systematically at both global and internal geometry levels, can yield quantifiable functional and economic benefits. The findings provide transferable design guidelines for support-free internal channels and functional interfaces in FDM-manufactured vacuum components, offering practical reference points for researchers and practitioners developing end-use additive manufacturing solutions in industrial automation. Full article

This comprehensive review synthesizes current advances and persistent challenges in integrating worker safety and food safety through human-robot collaboration (HRC) in poultry processing. Rapid industry expansion and rising consumer demand for ready-to-eat poultry products have heightened occupational risks and foodborne contamination concerns, necessitating holistic safety strategies. The review examines ergonomic, microbiological, and regulatory risks specific to poultry lines, and maps how state-of-the-art collaborative robots (“cobots”)—including power and force-limiting arms, adaptive soft grippers, machine vision, and biosensor integration—can support safer, more hygienic, and more productive operations. The authors analyze technical scientific literature (2018–2025) and real-world case studies, highlighting how automation (e.g., vision-guided deboning and intelligent sanitation) can reduce repetitive strain injuries, lower contamination rates, and improve production consistency. The review also addresses the psychological and sociocultural dimensions that affect workforce acceptance, as well as economic and regulatory barriers to adoption, particularly in small- and mid-sized plants. Key research gaps include gripper adaptability, validation of food safety outcomes in mixed human-cobot workflows, and the need for deeper workforce retraining and feedback mechanisms. The authors propose a multidisciplinary roadmap: harmonizing ergonomic, safety, and hygiene standards; developing adaptive food-grade robotic end-effectors; fostering explainable AI for process transparency; and advancing workforce education programs. Ultimately, successful HRC deployment in poultry processing will depend on continuous collaboration among industry, researchers, and regulatory authorities to ensure both safety and competitiveness in a rapidly evolving global food system. Full article

Soft robotic grippers provide compliant and adaptive manipulation, but most existing designs address actuation speed, adaptability, modularity, or sensing individually rather than in combination. This paper presents an electro-actuated customizable stacked Fin Ray gripper that integrates these capabilities within a single design. The gripper employs a compact solenoid for fast grasping, multiple vertically stacked Fin Ray segments for improved 3D conformity, and interchangeable silicone or TPU fins that can be tuned for task-specific stiffness and geometry. In addition, a light-guided, vision-based sensing approach is introduced to capture deformation without embedded sensors. Experimental studies—including free-fall object capture and optical shape sensing—demonstrate rapid solenoid-driven actuation, adaptive grasping behavior, and clear visual detectability of fin deformation. Complementary simulations using Cosserat-rod modeling and bond-graph analysis characterize the deformation mechanics and force response. Overall, the proposed gripper provides a practical soft-robotic solution that combines speed, adaptability, modular construction, and straightforward sensing for diverse object-handling scenarios. Full article

Banana harvesting relies heavily on manual labor, which is labor-intensive and prone to fruit damage due to insufficient control of contact forces. This paper presents a systematic methodology for the design and optimization of adaptive flexible end-effectors for banana bunch harvesting, focusing on contact behavior and mechanical constraints. By integrating response surface methodology (RSM) with multi-objective genetic algorithm (MOGA) optimization, the relationships between finger geometry parameters and key performance metrics—contact area, contact stress, and radial stiffness—were quantified, and Pareto-optimal structural configurations were identified. Experimental and simulation results demonstrate that the optimized flexible fingers effectively improve handling performance: contact area increased by 13–28%, contact stress reduced by 45–56%, and radial stiffness enhanced by 193%, while the maximum shear stress on the fruit stalk decreased by 90%, ensuring harvesting stability during dynamic loading. The optimization effectively distributes contact pressure, minimizes fruit damage, and enhances grasping reliability. The proposed contact-behavior-constrained design framework enables passive adaptation to fruit morphology without complex sensors, offering a generalizable solution for soft robotic handling of fragile and irregular agricultural products. This work bridges the gap between bio-inspired gripper design and practical agricultural application, providing both theoretical insights and engineering guidance for automated, low-damage fruit harvesting systems. Full article

Modern manufacturing robots must dynamically balance multiple conflicting objectives amid rapidly evolving production demands. Traditional control approaches lack the adaptability required for real-time decision-making in Industry 4.0 environments. This study presents an adaptive multi-objective reinforcement learning (MORL) framework integrating dynamic preference weighting with Pareto-optimal policy discovery for real-time adaptation without manual reconfiguration. Experimental validation employed a UR5 manipulator with RG2 gripper performing quality-aware object sorting in CoppeliaSim with realistic physics (friction μ = 0.4, Bullet engine), manipulating 12 objects across four geometric types on a dynamic conveyor. Thirty independent runs per algorithm (seven baselines, 30,000+ manipulation cycles) demonstrated +24.59% to +34.75% improvements (p < 0.001, d = 0.89–1.52), achieving hypervolume 0.076 ± 0.015 (19.7% coefficient of variation—lowest among all methods) and 95% optimal performance within 180 episodes—five times faster than evolutionary baselines. Four independent verification methods (WFG, PyMOO, Monte Carlo, HSO) confirmed measurement reliability (<0.26% variance). The framework maintains edge computing compatibility (<2 GB RAM, <50 ms latency) and seamless integration with Manufacturing Execution Systems and digital twins. This research establishes new benchmarks for adaptive robotic control in sustainable Industry 4.0/5.0 manufacturing. Full article

Soft robotics represents a rapidly advancing and significant subfield within modern robotics. However, existing soft actuators often face challenges including unwanted deformation modes, limited functional diversity, and a lack of versatility. This paper presents a four-chamber multimodal soft actuator with a centrally symmetric layout and independent pneumatic control. While building on existing multi-chamber concepts, the design incorporates a cruciform constraint layer and inter-chamber gaps to improve directional bending and reduce passive chamber deformation. An empirical model based on the vector superposition of single- and dual-chamber inflations is developed to describe the bending behavior. Experimental results show that the actuator can achieve omnidirectional bending with errors below 5% compared to model predictions. To demonstrate versatility, the actuator is implemented in two distinct applications: a three-finger soft gripper that can grasp objects of various shapes and perform in-hand twisting maneuvers, and a steerable crawling robot that mimics inchworm locomotion. These results highlight the actuator’s potential as a reusable and adaptable driving unit for diverse soft robotic tasks. Full article