Accurate needle steering using bevel-tip needles remains challenging due to nonlinear needle-tissue interactions and structural limitations of conventional robotic insertion systems in imaging-guided environments. This paper presents a cable-driven parallel robot (CDPR)-based needle steering framework that enables curvature-induced steering through coordinated control of the needle base pose. The proposed system provides 6-DoF needle orientation control using eight cables and an additional Bowden cable mechanism for axial rotation. Phantom insertion experiments demonstrate that steering direction can be regulated by bevel-tip orientation and that obstacle-avoidance insertion toward a desired target location is achievable. These results confirm the feasibility of CDPR-based needle steering for imaging-compatible minimally invasive intervention scenarios.
Autonomous Mobile Robots are typically limited to structured environments, as conventional wheeled propulsion often fails on deformable terrains like sand due to excessive wheel slip and sinkage. To address this mobility challenge, this paper introduces a novel locomotion strategy for a high-degree-of-freedom wheel-legged robot. The proposed method is a gait based on an asymmetric "dynamic compact-and-push" cycle, where the robot's limbs perform a paddling-like motion to actively remodel the granular media. This active terrain remodeling allows the robot to generate net forward thrust where conventional wheeled locomotion is ineffective. We systematically designed and experimentally validated four distinct gaits founded on this principle. The results demonstrate that this approach enables sustained forward motion in an environment where wheeled propulsion is verified to fail, with the asynchronous paddling gait proving most effective. This work contributes a new, validated locomotion mechanism for sand terrains and provides a quantitative comparison of different limb coordination strategies.
Novel Anomaly Class Discovery (NACD) has recently gained attention in industrial anomaly detection, aiming not only to recognize defects but also to recognize fine-grained and previously unseen anomaly types. However, existing methods, primarily based on visual images, struggle to handle common but challenging materials such as black rubber products. Such products often exhibit hardness anomalies and visually indistinguishable surface defects during production due to process instability and high material absorbance. To address this, we present T-NACD, the first tactile-friendly framework for NACD, leveraging tactile sensing to detect subtle geometric anomalies that are invisible to vision. We introduce two tactile-specific data augmentation methods and design a lightweight feature adapter to transfer visual pre-trained models to the tactile domain without the need for large-scale training. Moreover, we propose a dual-margin enhanced entropy regularization method to mitigate forgetting of known categories and reduce feature stocking across similar anomaly classes. To support this study, we collect BRD, the first real-world tactile dataset for black rubber anomaly detection, including BRD-A (surface and hardness defects) and BRD-H (diverse hardness anomalies). Experimental results show that T-NACD outperforms visual SOTA methods by 6.9% and generalizes well to other nonindustrial tactile detection tasks.
Containerization and orchestration using cloud-native technologies enable scalable deployment of robotic software. Integrating ROS~2 with Kubernetes offers a flexible infrastructure, but also introduces a complex, multi-layered communication stack - from DDS middleware to container networks and the physical layer. Each layer adds overhead and variability that impact application-level performance. This paper presents a comprehensive analysis of communication performance across the cloudedgerobot continuum, focusing on throughput and one-way latency in scalable ROS~2 deployments. We evaluate communication across intra-robot, edge, and cloud segments using wired and wireless connections, including emerging technologies like Wi-Fi~7 and high-speed LAN. Using a Kubernetes-based testbed, we investigate various ROS~2 middlewares, CNI plugins, QoS configurations, and encryption options. Our experiments reveal the impact of network overlays, routing paths, and middleware choices on latency and bandwidth. Despite the inherent complexity, the results confirm the feasibility of deploying ROS~2 in orchestrated, scalable environments. We summarize key insights as practical takeaways, many of which apply beyond Kubernetes, to guide the design of robust cloud/edge robotic systems.
