https://scholar.dgist.ac.kr/handle/20.500.11750/900 2026-10-27T07:55:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61022 Title: Surface-Conforming Modular Jamming Pads for Enhanced Contact and Friction Author(s): Cho, Jinman; Yang, Junmo; Im, Kihong; Yun, Dongwon Abstract: Granular jamming has been widely explored for adaptive robotic grasping, but conventional designs typically rely on single spherical jamming structures that limit contact distribution and modularity. This paper presents a flexure-based robotic gripper integrating modular square-shaped granular jamming pads as distributed adaptive contact interfaces. The design combines structural compliance from paired-Crossed Flexural Hinges (p-CFH) with material compliance from granular jamming, enabling passive surface conformity while improving frictional coupling and pad maintainability. Systematic experiments analyze the influence of pad geometry, granular material, gap distance, pad height, and vacuum pressure on grasp performance. Results show that the proposed square pad achieves 280% higher grip force than a spherical pad, while providing stable jamming behavior with EPDM granules. Compared with silicone pads, the jamming pad produces 30% higher friction and 197% larger contact area under identical loading conditions. Additional experiments further demonstrate robust grasping under varying contact orientations, positional offsets, and fragile objects. These results demonstrate that modular granular-jamming pads can serve as effective distributed contact interfaces for multi-finger robotic grippers. 2026-07-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61015 Title: 3-DoF Snake Robot Head Stabilization for Visual Navigation Using a Coaxial Spherical Parallel Manipulator Author(s): Lim, Seunghyun; Song, Jinhyeok; Lee, Seongmun; Kim, TaeHwan; Yun, Dongwon Abstract: This letter proposes a three-degree-of-freedom (3-DoF) head stabilizer, defined as a compact camera-orientation stage mounted between the snake robot's head link and the onboard monocular camera, based on a coaxial spherical parallel manipulator (CSPM) to enable visual navigation. Visual navigation suits biologically-inspired robots with tight form-factor constraints because it can leverage datasets collected on other platforms and operate with a single monocular camera. However, locomotion-induced head motion in wheel-less snake robots causes camera-orientation oscillations and in-plane image rotations, making it difficult to obtain stable image sequences. To address this, we formulate a matrix-based camera-stabilization command from the head-mounted IMU and a PCA-based locomotion-direction estimate, and use an idealized 3-DoF active spherical joint to quantify the resulting stabilization demand. We then adopt an established coaxial-SPM model to evaluate the task-specific roll-pitch feasibility and select CSPM geometry with a singularity margin. The resulting CSPM-based head stabilizer is integrated into a snake robot simulation, where it attenuates locomotion-induced camera-orientation oscillations and reduces in-plane image rotation, producing steadier image sequences for monocular visual navigation. When coupled with a topological visual-navigation model trained on heterogeneous data, the stabilized image stream supports zero-shot monocular visual navigation in simulated wheel-less snake-robot scenarios. 2026-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/61014 Title: mono-MORP: Monocular Traversability Representation and Motion Planner for Topological Visual Navigation Author(s): Lim, Seunghyun; Park, Jeyoung; Yang, Junmo; Yun, Dongwon Abstract: Topological visual navigation is attractive for biologically-inspired robots because it requires only a monocular camera and can be transferred to robots unseen during training, without dense maps or expensive sensors. Yet many systems rely on waypoint-only open-loop following: even when velocity commands are published at a higher loop rate, the command values are conditioned only on the low-rate waypoint stream and do not incorporate newly observed near-field obstacles. Moreover, waypoint representations predicted from monocular observations tend to bias the controller toward forward motion, making sharp turns or backward maneuvers difficult near obstacles. In this letter, we propose mono-MORP-RL, a reinforcement-learning-based local motion planner that converts monocular depth maps into a metric-free traversability observation by summarizing depth-gradient cues along virtual lanes. Conditioned on this traversability and the predicted waypoint, mono-MORP-RL updates obstacle-aware local commands at the planning rate while using the same waypoint stream, enabling rapid reactions to near-field obstacles without depth sensors or metric-scale depth. In simulations and real-world experiments on a tortoise-inspired mobile robot, mono-MORP-RL increases the success rate and reduces collisions across dynamic obstacle scenarios compared with waypoint-only open-loop followers, and we further analyze the embedded update-rate trade-off against a recent monocular-depth reactive baseline. 2026-09-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60990 Title: Wire-Driven Robotic Gripper of Sensorized Flexure Joints With Kinematic Contact Detection and Underactuated Feedback Control Author(s): Kim, Youngrae; Kang, Minsuk; Yang, Junmo; Yun, Dongwon Abstract: This paper presents an underactuated robotic gripper with Paired Cross Strip Pivot (PCSP) sensorized joints using embedded strain gauges. Conventional underactuated grippers provide passive adaptability but lack sensing and controllability, limiting grasping precision and dexterity. We develop a sensing-integrated gripper based on sensorized flexure joints and feedback control. The PCSP large-deformation behavior is incorporated into displacement/torque sensing, modeling, and control, and sensing performance is validated with 1.90% static error and 2.02% dynamic error. A wire-driven underactuated finger model is derived to consider workspace constraints and compute actuation. For robust control transition, we propose kinematic contact detection that switches between task-space position control and DOB-based force control regulating scalar contact force. Experiments demonstrate accurate position tracking, stable force regulation for different objects, multi-object grasping of 16 objects, and can-lid opening with 60% success. These results show that sensing and feedback control can be integrated into underactuated grippers without compromising structural simplicity. 2026-08-31T15:00:00Z