Search Results (6)

A growing challenge in mobile robotics is the reliance on complex graphical interfaces and rigid control pipelines, which limit accessibility for non-expert users. This work introduces a latency-aware benchmarking framework that enables natural-language robot navigation by integrating multiple Large Language Models (LLMs) with the Robot Operating System 2 (ROS 2) Navigation 2 (Nav2) stack. The system allows robots to interpret and act upon free-form text instructions, replacing traditional Human–Machine Interfaces (HMIs) with conversational interaction. Using a simulated TurtleBot4 platform in Gazebo Fortress, we benchmarked a diverse set of contemporary LLMs, including GPT-3.5, GPT-4, GPT-5, Claude 3.7, Gemini 2.5, Mistral-7B Instruct, DeepSeek-R1, and LLaMA-3.3-70B, across three local planners, namely Dynamic Window Approach (DWB), Timed Elastic Band (TEB), and Regulated Pure Pursuit (RPP). The framework measures end-to-end response latency, instruction-parsing accuracy, path quality, and task success rate in standardised indoor scenarios. The results show that there are clear trade-offs between latency and accuracy, where smaller models respond quickly but have less spatial reasoning, while larger models have more consistent navigation intent but take longer to respond. The proposed framework is the first reproducible multi-LLM system with multi-planner evaluations within ROS 2, supporting the development of intuitive and latency-efficient natural-language interfaces for robot navigation. Full article

Background: Health system literacy is crucial for immigrants to navigate health care systems and access necessary services. Little is known about how well immigrants understand and use the healthcare system in Canada. This study aimed to adapt and validate a health system literacy scale for the Canadian context (HSL-CAN). Methods: A cross-sectional online survey was conducted from March 11 to July 19, 2024, among Chinese individuals aged 30 or older who have lived in Canada for at least 6 months. The HSL-CAN was developed through a literature review, patient and provider consultation, and adaptation of the European Health Literacy Population Survey 2019–2021 for navigational health literacy measurement (HLS₁₉–NAV) and was then translated into simplified and traditional Chinese. Content validity was evaluated via stakeholders’ feedback, and structural validity was evaluated via exploratory and confirmatory analyses (EFA/CFA). Convergent and discriminant validity, as well as known-group validity, were tested using correlations with the HLS₁₉-SF12, ANOVA (or t-test), and effect size. Internal consistency was measured with Cronbach’s alpha coefficient and composite reliability. Results: Initially, HSL-CAN contained 25 items developed using a five-point Likert response scale. Some minor revisions were made according to the stakeholders’ feedback (n = 12). Five redundancy items were removed based on the EFA. CFA supported a one-factor model with good fit indices (CFI = 0.960, TLI = 0.955, SRMR = 0.033, RMSEA = 0.025),${\chi }^2$/df = 1.41). The scale showed a solid internal reliability (Cronbach’s alpha = 0.81; composite reliability = 0.812). The HSL-CAN is highly correlated with the “health care” construct but lowly with the “health prevention and promotion” construct of HLS₁₉–SF12. Known-group validity showed large mean differences by education, income, and non-cancer chronic comorbidities and small to moderate mean differences by gender, age groups, employment status, self-rated health, and assistance needed to see a healthcare provider. Conclusions: The HSL-CAN is the first validated instrument to evaluate health system literacy in the Chinese population in Canada. Given strong validity and reliability, the instrument can be useful for research and practice, although further refinement is recommended before using this scale on the general population in Canada. Full article

The aim of our study was to assess the efficacy of a computed tomography (CT)-Navigation™ electromagnetic system compared to conventional CT methods for percutaneous lung biopsies (PLB). In this single-center retrospective study, data of a CT-Navigation™ system guided PLB (NAV-group) and conventional CT PLB (CT-group) performed between January 2017 and February 2020 were reviewed. The primary endpoint was the diagnostic success. Secondary endpoints were technical success, total procedure duration, number of CT acquisitions and the dose length product (DLP) during step ∆1 (from planning to initial needle placement), step ∆2 (progression to target), and the entire intervention (from planning to final control) and complications. Additional parameters were recorded, such as the lesion’s size and trajectory angles. Sixty patients were included in each group. The lesions median size and median values of the two trajectory angles were significantly lower (20 vs. 29.5 mm, p = 0.006) and higher in the NAV-group (15.5° and 10° vs. 6° and 1°; p < 0.01), respectively. Technical and diagnostic success rates were similar in both groups, respectively 95% and 93.3% in the NAV-group, and 93.3% and 91.6% in the CT-group. There was no significant difference in total procedure duration (p = 0.487) and total number of CT acquisitions (p = 0.066), but the DLP was significantly lower in the NAV-group (p < 0.01). There was no significant difference in complication rate. For PLB, CT-Navigation™ system is efficient and safe as compared to the conventional CT method. Full article

A current trend in automotive research is autonomous driving. For the proper testing and validation of automated driving functions a reference vehicle state is required. Global Navigation Satellite Systems (GNSS) are useful in the automation of the vehicles because of their practicality and accuracy. However, there are situations where the satellite signal is absent or unusable. This research work presents a methodology that addresses those situations, thus largely reducing the dependency of Inertial Navigation Systems (INSs) on the SatNav. The proposed methodology includes (1) a standstill recognition based on machine learning, (2) a detailed mathematical description of the horizontation of inertial measurements, (3) sensor fusion by means of statistical filtering, (4) an outlier detection for correction data, (5) a drift detector, and (6) a novel LiDAR-based Positioning Method (LbPM) for indoor navigation. The robustness and accuracy of the methodology are validated with a state-of-the-art INS with Real-Time Kinematic (RTK) correction data. The results obtained show a great improvement in the accuracy of vehicle state estimation under adverse driving conditions, such as when the correction data is corrupted, when there are extended periods with no correction data and in the case of drifting. The proposed LbPM method achieves an accuracy closely resembling that of a system with RTK. Full article

The receiver delay has a significant impact on global navigation satellite system (GNSS) time measurement. This article comprehensively analyzes the difficulty, composition, principle, and calculation of GNSS receiver delay. A universal method, based on clock-steering characterization, is proposed to absolutely calibrate all types of receivers. We use a hardware simulator to design several experiments to test the performance of GNSS receiver delay for different receiver types, radio frequency (RF) signals, operation status and time-to-phase (TtP). At first, through the receivers of Novatel and Septentrio, the channel delay of Septentrio is 2 ns far lower than 65 ns for Novatel, and for the inter-frequency bias of GLONASS L1, Septentrio tends to increase within 10 ns compared with decreasing of Novatel within 5 ns. Secondly, a representative receiver of UniNav-BDS (BeiDou) is chosen to test the influence of Ttp which may be ignored by users. Under continuous operation, the receiver delay shows a monotone reduction of 10 ns as TtP increased by 10 ns. However, under on-off operation, the receiver delay represents periodic variation. Through a zero-baseline comparison, we verifies the relation between receiver delay and TtP. At last, the article analyzes instrument errors and measurement errors in the experiment, and the combined uncertainty of absolute calibration is calculated with 1.36 ns. Full article

The Earth-viewed images acquired by the space probe OSIRIS-REx during its Earth gravity assist flyby maneuver on 22 September 2017 provided an opportunity to radiometrically calibrate the onboard NavCam imagers. Spatially-, temporally-, and angularly-matched radiances from the Earth viewing GOES-15 and DSCOVR-EPIC imagers were used as references for deriving the calibration gain of the NavCam sensors. An optimized all-sky tropical ocean ray-matching (ATO-RM) calibration approach that accounts for the spectral band differences, navigation errors, and angular geometry differences between NavCam and the reference imagers is formulated in this paper. Prior to ray-matching, the GOES-15 and EPIC pixel level radiances were mapped into the NavCam field of view. The NavCam 1 ATO-RM gain is found to be 9.874 × 10⁻² Wm⁻²sr⁻¹µm⁻¹DN⁻¹ with an uncertainty of 3.7%. The ATO-RM approach predicted an offset of 164, which is close to the true space DN of 170. The pre-launch NavCam 1 and 2 gains were compared with the ATO-RM gain and were found to be within 2.1% and 2.8%, respectively, suggesting that sensor performance is stable in space. The ATO-RM calibration was found to be consistent within 3.9% over a factor of ±2 NavCam 2 exposure times. This approach can easily be adapted to inter-calibrate other space probe cameras given the current constellation of geostationary imagers. Full article