Spacecraft control requires globally consistent formulations and robustness against model uncertainties and external disturbances. While a general intrinsic sliding-mode framework on Lie groups is available for first-order reaching, existing higher-order geometric sliding-mode formulations are predominantly developed for specific configuration groups or application models. This paper addresses the complementary problem of formulating a multivariable Super-Twisting reaching mechanism directly within a general Lie-group mechanical framework. The algorithm is formulated on the Lie algebra through covariant derivatives and builds upon the existing state-space Lie group structure. A strict Lyapunov analysis establishes finite-time convergence to the second-order sliding set and provides constructive gain conditions. Under suitable matching and regularity assumptions, exact rejection of matched disturbances is obtained, while the sliding dynamics ensure almost-global asymptotic tracking. The method is specialized to spacecraft attitude dynamics on SO(3) and assessed under nominal, disturbed, uncertain, and sampled-data conditions.
Upper-limb amputation can have a significant impact on a patient’s quality of life, including limited independence. While myoelectric prostheses present the opportunity for more natural and intuitive control when compared to body-powered devices, their effectiveness can be limited by the challenges that come with surface EMG data collection. This study proposes a simplified EMG post-processing approach for converting muscle activation into robotic arm movements. Surface EMG data was collected sequentially from ten locations on the upper limb of a single subject that was instructed to perform four predefined motion sequences designed to cover common upper limb motions. The signals were processed via a custom pipeline that includes filtering and onset detection using NeuroKit2. The muscle activations detected were then mapped to commands for a Baxter robotic arm, which was used as a proxy for a prosthetic device. The resulting robotic motions were compared to the human subject’s via the use of joint angle measurements and the time to complete each sequence. The results show that the proposed pipeline can reproduce the general structure and range of human motion, with the robot achieving joint movements similar to those of the participant. However, the robot suffered from a longer time required to complete each sequence of movements. This study shows that a relatively simple post-process EMG-based control scheme can capture multi-step human motion while remaining transparent. However, improvements in speed and real-time performance will be necessary for practical use in prosthetic devices.
The International Organization for Standardization (ISO) defines safety as the ability of a robotic system to operate within limits that reduce risks to humans to an acceptable level. However, ISO standards (such as ISO/TS 15066 and ISO 10218–2) contain ambiguities that may lead to inconsistent risk assessments or hazard underestimation. This Perspective Article presents an energy-centric viewpoint on safety in physical human-robot interaction (pHRI). Building on our previous critical review of pHRI safety constraints, the article isolates and explicates one central theme emerging from that analysis: the role of mechanical energy in interpreting ISO/TS 15066 transient-contact limits. Specifically, we discuss how pain-related force limits can be mapped to admissible elastic energy stored in tissue deformation, why the instant of maximum deformation is central for transient-contact assessment, how this interpretation changes under free-recoil and potential-clamp conditions, and why energy-based metrics remain insufficient as standalone descriptors of pain perception or sustained-contact risk. We conclude by outlining open challenges and future research directions for leveraging energy-centered metrics toward safe and effective human-robot interaction.
IntroductionVisual perception for robot-assisted minimally invasive surgery is sensitive to low illumination, smoke, blood interference, and imaging blur, which can degrade segmentation performance during continuous video.MethodsWe propose Lightweight Spatiotemporal Test-Time Adaptation (LiteST-TTA), combining a Top-k predictive-entropy uncertainty gate, motion-compensated historical predictions, hybrid temporal supervision, and updates restricted to Batch Normalization affine parameters. We evaluate five methods on EndoVis 2017 under 12 controlled degradation conditions (four types × three severity levels).ResultsTENT yields a macro-average DSC of 0.8881 and LiteST-TTA yields 0.8879. A significant degradation-type-by-severity interaction is observed for DSC (Greenhouse–Geisser corrected p = 0.031) and DSC improvement over Source (p < 0.001). Under an identical 24.2% update schedule, LiteST-TTA and Selective Entropy achieve nearly identical DSC (0.886967 vs. 0.886966). Statistics-only BN adaptation yields a DSC of 0.8864. In sequence-disjoint evaluation, LiteST-TTA improves severe low-light performance (0.8083 vs. 0.3251 for Source), whereas Source is superior in 10 of 12 held-out conditions.DiscussionAdaptation benefits depend strongly on degradation type, severity, normalization adaptation, update dynamics, and computational cost. These factors should be considered jointly when applying online adaptation to surgical video.
