Driven by orchard labor shortages and rising demand for intelligent harvesting, automated apple picking requires a balance between conformal enveloping and slip-resistant stability. To reduce damage and slippage caused by fragile skins, variable morphologies, and motion disturbances, this study proposes a multi-joint pneumatic flexible apple-picking hand with adjustable circumferential configuration. Based on structural configuration determining grasping stability, six apple-morphology-based finger-base supports were designed. Parametric analysis of soft gripper cavities identified an isosceles trapezoidal profile as the best configuration. Using the Yeoh constitutive model, an equivalent joint model for conformal gripping was developed, and genetic algorithm (GA) optimization selected the four-joint design as the preferred configuration. Static finite element simulations determined an operating pressure of 20.32 kPa. Grasping stability was quantified by relative slip displacement in rigid–flexible coupled dynamic simulations. Among the tested support configurations within 60–110°, the 90° bracket produced the most stable slip response under vertical and horizontal disturbances. Thin-film pressure tests showed an asymmetric but stable three-finger load-sharing pattern. Field trials in a high-density dwarf spindle orchard achieved an 83.98% harvesting success rate. After 72 h of cold storage, no obvious surface browning, epidermal abrasion, or compression marks were observed during visual inspection. This assessment was limited to visible external damage and did not include quantitative evaluation of internal bruising, firmness degradation, flesh browning, or long-term storage quality. These results demonstrate stable grasping performance and low visible external damage under the tested conditions.

To address the challenges of severe background interference and unstable grasping of irregular maintenance tools in complex railway ballast environments, this paper proposes a robotic system that integrates enhanced visual perception with bio-inspired soft grasping. The core components of the system include a lightweight detection network (RA-YOLO), asymmetric “Fin Ray” soft fingers, and a visual servoing control framework. By embedding the CBAM attention mechanism and incorporating Mosaic data augmentation, RA-YOLO achieves robust feature extraction under complex backgrounds. The fingertip topology is optimized using the Yeoh constitutive model and finite element analysis, thereby improving stiffness under heavy loads and overall adaptability. Experimental results demonstrate that proposed RA-YOLO achieved a mAP@0.5 of 93.6% on the standard test set with an inference speed of 105 FPS. The visual-servo localization experiment an average Euclidean positioning error of 1.03 mm, with the maximum component-wise absolute error remaining below 2.5 mm. In system-level grasping experiments involving five categories of irregular tools, the integrated system achieved an overall grasping success rate of 91.8%, indicating its potential for automated tool recovery in unstructured railway maintenance environments.