FlexiRay 与人手感知模态对比及应用场景示意图 在软体机器人交互领域，长期存在着一对核心矛盾：结构柔顺性与感知精确性难以兼顾。传统视触觉传感器（ VTS ）虽能提供高分辨率反馈，但往往依赖刚性结 构以维持光路稳定，牺牲了软体抓手本质的柔顺适应能力；而现有的柔性传感器设计，在大变形下又面临视野遮挡和感知盲区的难题。 浙江大学 Grasp Lab 研发的视触觉一体化软体夹爪 FlexiRay ，以"光学系统 - 柔性结构 -AI 感知"的深度融合为核心路径，创新性地结合了仿生 Fin Ray 效应与 多反射镜光学阵列，成功复现了人手的五大核心触觉模态。 FlexiRay 不仅保留了软体抓手的大变形自适应能力，更在剧烈形变下实现了超过 87% 的有效感知覆 盖率，为软体机器人的智能化交互开辟了"全域感知、柔顺操作"的新赛道。 FlexiRay 柔顺抓取的纹理效果及感知覆盖示意图 ▍ 结构与光学革新：化 "遮挡"为"视窗"，重构柔性感知视野 机械结构与光学系统的协同设计是 FlexiRay 的硬件基石。 Grasp Lab 团队突破了"限制形变以保视觉"的传统思路，转而利用形变，通过系统级优化解决了软体抓 手大 ...

Core Insights - The article discusses the development of a soft robotic sensor system called PneuGelSight, which integrates visual and tactile sensing capabilities to enhance the performance of soft robots in industrial applications [1][3]. Group 1: PneuGelSight Overview - PneuGelSight is a soft robotic finger that incorporates a camera and lighting system, enabling high-precision proprioception and tactile sensing during grasping tasks [4][6]. - The design features a 3D-printed corrugated structure for easy extension under pneumatic drive, with a thicker silicone layer on the inside to facilitate bending during inflation [4][6]. Group 2: Optical Design and Functionality - The optical sensing structure consists of an embedded camera, a lighting system, and a soft reflective surface, allowing for clear image capture regardless of finger bending [6][10]. - The system uses color variance in reflected light to calculate surface normals and reconstruct the 3D geometry of objects, enhancing the robot's interaction capabilities [10][11]. Group 3: Performance and Testing - The finger measures 110 mm in length and has a semicircular cross-section with a diameter of 55 mm, manufactured using SLA 3D printing technology [7][9]. - Testing showed that the reconstruction accuracy of the 3D point cloud model varied between 2.12 mm and 8.76 mm across different deformation scenarios, with an average of 5.35 mm [16][17]. Group 4: Tactile Sensing Capabilities - Tactile sensing is achieved by detecting changes in the surface normals of the silicone layer when in contact with objects, allowing for high-resolution reconstruction of surface features [19][21]. - The system can detect forces as light as 0.2 N, comparable to lifting a sheet of A4 paper, and can adapt sensitivity by adjusting the silicone hardness [25]. Group 5: Practical Applications - Demonstration experiments involved a soft robotic gripper exploring an avocado, successfully constructing a 3D model of the object through repeated contact and analysis of surface textures [26][27]. - The results indicated a high fidelity in the reconstructed shape and surface texture, showcasing the technology's potential in object recognition and interaction tasks [27].

Core Insights - The article discusses the development of SPARC, a soft robot designed for precise movement in three-dimensional environments, overcoming challenges faced by traditional soft robots [2][6]. Group 1: SPARC Development and Design - SPARC is a soft, proprioceptive, agile robot capable of climbing and exploring in 3D environments, utilizing a unique origami structure for movement on both horizontal and vertical surfaces [2][3]. - The robot integrates three parallel Kresling origami actuators and suction cup structures, allowing for three-dimensional driving and adhesion on various surfaces [5][9]. - SPARC can carry a payload of 500 grams, which is more than twice its own weight of 210 grams, demonstrating significant load-bearing capabilities [5][17]. Group 2: Technical Innovations - The Kresling origami actuators provide a 60% contraction rate and a driving force of 3 kilograms under -80 kPa vacuum conditions, with a lifespan exceeding 20,000 cycles [7][8]. - The robot employs a dual closed-loop control system for precise posture management and global positioning adjustments, enhancing trajectory tracking accuracy [5][14]. - SPARC's tracking control algorithm has shown low tracking errors of approximately 0.5% on horizontal surfaces and under 3% on vertical paths, indicating robust performance [17]. Group 3: Motion Strategies and Testing - The research team conducted systematic tests to evaluate SPARC's performance in various conditions, including carrying a 500-gram load and navigating complex paths [16][19]. - A modular configuration of SPARC was explored, allowing for enhanced bending angles and step lengths, facilitating smoother transitions from ground to vertical surfaces [19][20]. - The robot's climbing strategy was optimized for vertical environments, reducing collision risks and improving safety during operation [20].