Precise Navigation Research Articles

An Over-Actuated Hexacopter Tilt-Rotor UAV Prototype for Agriculture of Precision: Modeling and Control

This paper focuses on the modeling, control, and simulation of an over-actuated htr. This configuration implies that two of the six actuators are independently tilted using servomotors, which provide high maneuverability and reliability. This approach is predicted to maintain zero pitch throughout the trajectory and is expected to improve the aircraft’s steering accuracy. This arrangement is particularly beneficial for pa applications where accurate monitoring and management of crops are critical. The enhanced maneuverability allows for precise navigation in complex vineyard environments, enabling the uav to perform tasks such as aerial imaging and crop health monitoring. The employed control architecture consists of cascaded p-pid controllers using the slc method on the five controlled dof. Simulated results using Gazebo demonstrate that the htr achieves stability and maneuverability throughout the flight path, significantly improving precision agriculture practices. Furthermore, a comparison of the htr with a traditional hexacopter validates the proposed approach.

A robust time synchronization algorithm for GNSS/IMU integrated navigation in urban environments

Abstract Global navigation satellite systems (GNSS) are often integrated with inertial measurement units (IMU) for navigation in urban environments. The ability of these integrated systems to achieve precise positioning and navigation is highly dependent on accurate time synchronization. While the current time synchronization algorithms are capable of achieving millisecond-level synchronization in open environments, their performance deteriorates significantly in complex urban environments due to the impact on GNSS measurements of non-line-of-sight (NLOS) signals and multipath effects. Here we propose a loosely coupled GNSS/IMU integration time synchronization error modeling and compensation algorithm to tackle this problem in the context of urban vehicle navigation. In the algorithm, the time synchronization error is augmented as a state variable in the Robust Extended Kalman Filter (REKF), with the robustness factor threshold being dynamically adjusted based on the time synchronization error. Furthermore, we have designed a system time synchronization detection scheme based on two detection factors, with the aim of achieving high-precision GNSS/IMU time synchronization in complex urban environments. Experimental results show that the proposed algorithm synchronizes data time to the millisecond level in urban positioning environments. This represents an improvement of more than 90% in time synchronization precision compared to the traditional EKF-based time synchronization algorithm, and an improvement of more than 60% compared to the REKF-based time synchronization algorithm. Furthermore, compared to the EKF-based GNSS/IMU fusion algorithm, the proposed algorithm enhances the accuracy of the determination of both position and velocity in the horizontal and 3D directions by more than 50% and heading accuracy by 85%.

Optical molecular imaging technology and its application in precise surgical navigation of liver cancer.

Recent innovations in medical imaging technology have placed molecular imaging techniques at the forefront of diagnostic advancements. The current research trajectory in this field aims to integrate personalized molecular data of patients and diseases with traditional anatomical imaging data, enabling more precise, non-invasive, or minimally invasive diagnostic options for clinical medicine. This article provides an in-depth exploration of the basic principles and system components of optical molecular imaging technology. It also examines commonly used targeting mechanisms of optical probes, focusing especially on indocyanine green-the FDA-approved optical dye widely used in clinical settings-and its specific applications in diagnosing and treating liver cancer. Finally, this review highlights the advantages, limitations, and future challenges facing optical molecular imaging technology, offering a comprehensive overview of recent advances, clinical applications, and potential impacts on liver cancer treatment strategies.

Fusion GNSS/INS/Vision with Path Planning Prior for High Precision Navigation in Complex Environment

Fusion GNSS/INS/Vision with Path Planning Prior for High Precision Navigation in Complex Environment

Enhancing Robot Autonomy: Path Planning via Hand-Drawn Sketches and Computer Vision Integration

This investigation delves into an advanced method for enhancing robot autonomy by integrating hand-drawn sketches with computer vision systems to optimize route planning effectively. The procedure starts with the capture of image data, which is then processed through multiple stages until the sketches are turned into exact coordinates that guide a robot. Canny edge detection, a specific computer vision technique, is employed to detect the edges of the drawn lines, facilitating precise and dependable navigation for autonomous robot operations. Initially, the image is subjected to noise reduction and edge enhancement before converting the sketch lines into Cartesian coordinates. This step is succeeded by point filtering and ordering to ascertain accurate path adherence by the robot, with no coordinates overlooked. Scale adjustments are also made to match digital image coordinates with the real-world setting, thereby improving the robot’s interaction based on the received visual inputs. The program has been tested in Robotic Operating System 2 (ROS) using MoveIt2 and also in RoboDK software, where it was used to verify the results. It is important to note that path planning, which focuses on determining an optimal route from start to finish, is a subset of motion planning, which also considers the dynamics and kinematics of the robot's movement along that route.

GPS/VIO integrated navigation system based on factor graph and fuzzy logic

In today’s technologically advanced landscape, precision in navigation and positioning holds paramount importance across various applications, from robotics to autonomous vehicles. A common predicament in location-based systems is the reliance on Global Positioning System (GPS) signals, which may exhibit diminished accuracy and reliability under certain conditions. Moreover, when integrated with the Inertial Navigation System (INS), the GPS/INS system could not provide a long-term solution for outage problems due to its accumulated errors. This article introduces a novel graph-based method that utilizes a dynamically adjustable fuzzy window to improve navigation and positioning accuracy. This approach effectively integrates GPS data with Visual-Inertial Odometry (VIO). Additionally, it proposes a novel technique for motion estimation and feature extraction, called Adaptive Feature-Flow Fusion, which facilitates robust performance in environments with both high- and low-feature content. The proposed GPS/VIO system is a compelling solution for mitigating GPS-based navigation’s accuracy and reliability concerns. It was implemented and tested on Jetson’s embedded board platform to ensure optimal and real-time system performance. The results from these tests are comprehensively detailed in this article. The system underwent stringent evaluation using the KITTI dataset, demonstrating significant accuracy improvements compared to the Extended Kalman Filter (EKF) system. Specifically, the GPS/VIO system exhibited remarkable accuracy improvements of 84.59% and 88.806% during GPS outages lasting 10 and 27 s, respectively. Furthermore, to enhance precision, data preprocessing techniques were incorporated. These techniques involve optimizing image data by adjusting contrast and brightness levels and applying noise reduction to the Inertial Measurement Unit (IMU) data. This resulted in a substantial 44.7% accuracy enhancement for predefined trajectories.

Event-Based Visual/Inertial Odometry for UAV Indoor Navigation.

Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great potential for indoor navigation due to their high dynamic range and low latency. In this study, an event-based visual-inertial odometry approach is proposed, emphasizing adaptive event accumulation and selective keyframe updates to reduce computational overhead. The proposed approach fuses events, standard frames, and inertial measurements for precise indoor navigation. Features are detected and tracked on the standard images. The events are accumulated into frames and used to track the features between the standard frames. Subsequently, the IMU measurements and the feature tracks are fused to continuously estimate the sensor states. The proposed approach is evaluated using both simulated and real-world datasets. Compared with the state-of-the-art U-SLAM algorithm, our approach achieves a substantial reduction in the mean positional error and RMSE in simulated environments, showing up to 50% and 47% reductions along the x- and y-axes, respectively. The approach achieves 5-10 ms latency per event batch and 10-20 ms for frame updates, demonstrating real-time performance on resource-constrained platforms. These results underscore the potential of our approach as a robust solution for real-world UAV indoor navigation scenarios.

Navigation in GNSS-Denied Environment Based on Observation of RSOs and Stars

Navigating in an environment, where Global Navigation Satellite System (GNSS) signals are unavailable, presents a formidable challenge. A feasible strategy involves integrating stellar observations with inertial information to realize precise navigation. Inertial/Stellar Celestial Navigation enhances position accuracy by addressing gyro drift through stellar observations. However, the position error still continues to grow over time which arises from unobservable error factors. With improved orbit determination accuracy and the resident space object (RSO) observation method, navigating using RSO observation is gaining viability. This study models the RSO navigation problem as a Perspective-n-Point (PnP) problem and proposes that observing more than 6 RSOs can yield complete solution for position and attitude. Additionally, a tightly coupled navigation algorithm integrating inertial, stellar, and RSO observations is introduced. Simulation validation under dynamic condition demonstrates the algorithm’s efficacy, achieving a position accuracy of 30[Formula: see text]m and an attitude accuracy of 5[Formula: see text]arcsec (RMS) without GNSS data.

Localized Microrobotic Delivery of Enzyme-Responsive Hydrogel-Immobilized Therapeutics to Suppress Triple-Negative Breast Cancer.

Triple-negative breast cancer (TNBC), characterized by its aggressive metastatic propensity and lack of effective targeted therapeutic options, poses a major challenge in oncological management. A proof-of-concept neoadjuvant strategy aimed at inhibiting TNBC tumor growth and mitigating metastasis through a localized delivery of chemotherapeutics is reported in this paper. This approach addresses the limitations in payload capacity and stimuli responsiveness commonly associated with microrobotics in oncology. A hydrogel-based system is developed for the immobilization of chemotherapeutic agents, subsequently encapsulated within magnetically responsive microrobots. This design leverages external magnetic fields to facilitate the precise navigation and localization of the therapeutic agents directly to the tumor site. The efficacy of this approach is demonstrated in an animal model, in which a significant 14-fold reduction in tumor size and suppression of metastasis to critical organs such as the liver and lungs are observed. Crucially, the drug release mechanism is engineered to be responsive to the tumor microenvironment and is regulated by the overexpression of the enzymatic activity of matrix metalloproteinases (MMP2 and MMP9) in TNBC tumors, triggering the degradation of the hydrogel matrix, leading to controlled release of the immobilized therapeutic drug. This ensures that the therapeutic action is localized, reducing systemic toxicity and enhancing treatment efficacy. These findings suggest that this neoadjuvant approach holds promise for broader applications in other cancer types.

MOBILE ROBOT NAVIGATION BASED ON LASER RANGE-METRY METHODS

The article is devoted to solving the problem of finding people who are under the debris of destroyed buildings, which requires an urgent solution to ensure an effective rescue operation. The primary focus of this research is the development and refinement of a navigation system for a mobile rescue robot. This paper specifically explores localization techniques and methods for constructing a map of an unknown environment, which are crucial for the robot’s effective operation in complex and dynamic settings. The overarching goal of the study is to identify the most adaptable and precise navigation method that can effectively manage the movement of a mobile robot in environments characterized by unpredictable and shifting obstacles. To solve the tasks, modern methods of laser telemetry and SLAM methods (Simultaneous Localization and Mapping) are used in the work, which are used to accurately create a map of the territory and localize the robot on it. The research proposes an innovative algorithm for the navigation system of a mobile robot, which leverages laser telemetry to meticulously scan the surrounding area. It has been shown that scanning process is essential for real-time data acquisition, enabling the robot to make informed decisions regarding its movement. It is stated that, the proposed lidar-based navigation method is particularly advantageous as it facilitates the creation of a detailed spatial map, even in environments where the layout is not known in advance. This approach allows for the precise determination of the robot’s position within the mapped space, ensuring that the robot can navigate effectively and avoid potential hazards. It is proved that, the integration of these advanced techniques significantly enhances the robot’s ability to perform in search and rescue missions, where the accurate and timely localization of trapped individuals is of paramount importance.

An Elastic Filtering Algorithm with Visual Perception for Vehicle GNSS Navigation and Positioning.

Amidst the backdrop of the profound synergy between navigation and visual perception, there is an urgent demand for accurate real-time vehicle positioning in urban environments. However, the existing global navigation satellite system (GNSS) algorithms based on Kalman filters fall short of precision. In response, we introduce an elastic filtering algorithm with visual perception for vehicle GNSS navigation and positioning. Firstly, the visual perception system captures real-time environmental data around the vehicle. It utilizes the interframe differential optical flow method and vehicle state switching characteristics to assess the current driving status. Secondly, we design an elastic filtering model specifically for various vehicle states. This model enhances the precision of Kalman filter-based GNSS navigation. In urban driving, vehicles often experience frequent stationary parking. To address this, we incorporate a zero-speed constraint to further refine vehicle location data when the vehicle is stationary. This constraint matches the data with the appropriate elastic filtering model. Ultimately, we conduct simulation and real-world vehicle navigation experiments to confirm the validity and rationality of our proposed algorithm. Compared with the conventional algorithm and the existing interactive multi-model algorithm, the proposed algorithm significantly improves the navigation and positioning accuracy of vehicle GNSS in urban environments. Compared to the commonly used constant acceleration (CA) and Constant Velocity (CV) models, there has been a significant improvement in positioning accuracy. Furthermore, when benchmarked against the more advanced interactive multi-model (IMM) model, the method proposed in this paper has enhanced the positioning accuracy enhancements in three dimensions: 21.8%, 20.9%, and 31.3%, respectively.

Intelligent Generic High-Throughput Oscillatory Shear Technology Fabricates Programmable Microrobots for Real-Time Visual Guidance During Embolization.

Microrobots for endovascular embolization face challenges in precise delivery within dynamic blood vessels. Here, an intelligent generic high-throughput oscillatory shear technology (iGHOST) is proposed to fabricate diversely programmable, multifunctional microrobots capable of real-time visual guidance for in vivo endovascular embolization. Leveraging machine learning (ML), key synthesis parameters affecting the success and sphericity of the microrobots are identified. Therefore, the ML-optimized iGHOST enables continuous production of uniform microrobots with programmable sizes (400-1000µm) at an ultrahigh rate exceeding 240mLh-1 by oscillatory segmenting fluid into droplets before ionic cross-linking, and without requiring purification. Particularly, the iGHOST-fabricated magnetically responsive lipiodol-calcium alginate (MagLiCA) microrobots are highly distinguishable under X-ray imaging, which allows for precise navigation in fluid flows of up to 4mLmin-1 and accurate embolization in liver and kidney blood vessels, thus addressing the current issues. Crucially, MagLiCA microrobots possess drug-loading capabilities, enabling simultaneous embolization and site-specific treatment. The iGHOST process is an intelligent, rapid, and green manufacturing method, which can produce size-controllable, multifunctional microrobots with the potential for precise drug delivery and treatment under real-time imaging across various medical applications.

Analysis of the impact of high temporal resolution tropospheric parameters on GNSS station coordinate and troposphere estimation

Abstract Troposphere modelling accuracy can seriously influence the performance of precise global navigation satellite system (GNSS) positioning. The temporal interpolation accuracies of troposphere parameters become important with the improvement in troposphere modelling, especially during the periods of severe weather changes. In this study, a one-month experiment was conducted for 10 EUREF Permanent GNSS Network stations to analyse the temporal interpolation errors of numerical weather model derived troposphere parameters and their influences on GNSS station coordinate and troposphere estimation. The root mean square of the differences between the 1- and 6-hourly European Centre for Medium-Range Weather Forecasts reanalysis v5-derived troposphere parameters are 0.72/11.52 mm for the zenith hydrostatic/zenith wet delay (ZWD), while they are 7.09/91.84 mm for the slant hydrostatic/wet delay at a 3 ∘ elevation angle. The corresponding influences on the estimated upper component and ZWD are 1.28 and 0.79 mm, respectively, in the daily static Precise Point Positioning ambiguity resolution solutions. However, the accuracy improvements are insignificant in the daily static solutions, mainly due to the drop of temporal interpolation errors with increasing elevation angles and the influence of other unmodeled systematic errors. In the 4-hourly static solutions, the accuracies of the up component improve 0.51 mm (5.9% in relative) and 0.29 mm (3.5% in relative) during the severe and stable periods, respectively. These impacts cannot be ignored in some GNSS subdaily applications, for example, monitoring non-tidal ocean loading effects, bedrock and monument thermal elastic effects, etc.

A shape-changing haptic navigation interface for vision impairment

Individuals with visual impairment (VI) require aids such as white canes and guide dogs to navigate their environments. Modern localisation technologies have the capacity to transform the way individuals with VI navigate surroundings, but they have yet to do so. A critical barrier is the inability of human–machine interfaces to communicate precise navigation instructions non-visually. We present a shape changing haptic interface (Shape) that provides spatial guidance in two dimensions via bending of its body. Individuals with VI and sighted individuals were recruited to locate virtual targets in 3D space using Shape and vibration feedback (Vibration), and sighted individuals were also asked to visually locate targets. Throughout, device orientation and position were tracked in real-time using a virtual reality system. Individuals with VI located targets significantly faster and more efficiently using Shape, than with Vibration, and there were no significant differences in time or efficiency between Shape and natural vision. Moreover, participants scored Shape significantly more positively than Vibration in a Likert user experience survey, while no significant differences were observed between Shape and natural vision. Here, we provide compelling evidence for the application of a new shape-changing haptic interface as part of an effective future digital navigation system for individuals with VI.

Design and Development of an Automated Forklift with IOT Weight System

This paper presents the design and development of an automated forklift system with integrated IoT-based weight monitoring and RFID-based pallet identification, aimed at enhancing efficiency and safety in industrial material handling. The forklift autonomously identifies, loads, and transports pallets using RFID technology, ensuring accurate pallet selection and handling. The IOT- based weight system, monitored via the Blynk app, enables real-time load tracking and overload alerts, safe operation. Path-following capabilities are achieved using IR sensors, while ultrasonic sensors provide effective obstacle detection, allowing precise navigation within medium scale industrial environments. By minimizing human intervention, the forklift system optimizes productivity, and offers a scalable solution for medium- scale industries. Results demonstrate the forklift’s effectiveness in automating complex tasks in industrial settings, contributing to improved workflow and operational reliability.

An Integrated Algorithm Fusing UWB Ranging Positioning and Visual–Inertial Information for Unmanned Vehicles

During the execution of autonomous tasks within sheltered space environments, unmanned vehicles demand highly precise and seamless continuous positioning capabilities. While the existing visual–inertial-based positioning methods can provide accurate poses over short distances, they are prone to error accumulation. Conversely, radio-based positioning techniques could offer absolute position information, yet they encountered difficulties in sheltered space scenarios. Usually, three or more base stations were required for localization. To address these issues, a binocular vision/inertia/ultra-wideband (UWB) combined positioning method based on factor graph optimization was proposed. This approach incorporated UWB ranging and positioning information into the visual–inertia system. Based on a sliding window, the joint nonlinear optimization of multi-source data, including IMU measurements, visual features, as well as UWB ranging and positioning information, was accomplished. Relying on visual inertial odometry, this methodology enabled autonomous positioning without the prerequisite for prior scene knowledge. When UWB base stations were available in the environment, their distance measurements or positioning information could be employed to institute global pose constraints in combination with visual–inertial odometry data. Through the joint optimization of UWB distance or positioning measurements and visual–inertial odometry data, the proposed method precisely ascertained the vehicle’s position and effectively mitigated accumulated errors. The experimental results indicated that the positioning error of the proposed method was reduced by 51.4% compared to the traditional method, thereby fulfilling the requirements for the precise autonomous navigation of unmanned vehicles in sheltered space.

Development of surgical robots: A brief history

The surgical robot is a complex integrating a number of modern high technologies. It results from the cross-integration and development of medical knowledge with mechanical engineering, intelligent control, advanced sensing technology, and other disciplines. Surgical robots improve the quality of medical services by providing patients with precise, minimally invasive, and intelligent surgical operations. Throughout the development history of surgical robots, with the improvement of the stability and flexibility of robots and the advancement of precise positioning technology, navigation technology, and automation technology, the current robots can perform more complex surgical operations. It has been widely used in orthopaedics, urology, neurosurgery, gastrointestinal surgery, hepatobiliary surgery, gynecology, and many other departments and has achieved good clinical results. Based on the field of surgical robot application, this paper introduces the development history of the main types of surgical robots in detail, summarizes the advantages and disadvantages of current surgical robots, and looks forward to the main development directions in the future to provide ideas for further research on surgical robots.

Application of Improved Visual SLAM in Intraoperative Navigation of Robotic-Assisted Laparoscopic Surgery

In robot-assisted laparoscopic surgery, precise intraoperative navigation is essential to enhance the accuracy, safety, and efficiency of the procedure. By extracting feature points from the environment, Visual Simultaneous Localization and Mapping (V-SLAM) technology has been widely employed to accomplish self-localization and map generation. In laparoscopic surgery, V-SLAM offers high-precision real-time positional data for surgical robots, thereby improving the accuracy of operative navigation. Nonetheless, the performance of conventional V-SLAM algorithms is significantly compromised by the extremely dynamic characteristics of surgical environments. Widely used visual SLAM systems, such as ORB-SLAM, are susceptible to inaccurate feature point tracking in the presence of dynamic objects and variations in lighting, hence compromising navigation precision and surgical safety. This study assessed the efficacy of visual V-SLAM technology in enhancing intraoperative navigation during robot-assisted laparoscopic surgery, pointing out its contribution to surgical precision, safety, and operational efficiency. The research enhanced the ORB-SLAM2 algorithm to adapt to the dynamically changing conditions encountered during surgical procedures. The experiment validated the efficacy of the enhanced method within a simulated laparoscopic surgery setting and assessed the impact of three variables: light intensity, instrument occlusion, and dynamic tissue deformation, on trajectory error (ATE and RPE) and mapping accuracy. The findings indicated that under situations of low illumination, significant occlusion, and substantial deformation, ATE and RPE mistakes escalated by over 50%, while map accuracy diminished by approximately 40%, leading to heightened potential surgical risks.

Research on Diagnosis and Localization of Moles for Robotic Mole Removal Surgery Based on YOLO

Surgical treatment is often considered the first choice for mole removal. Treatments such as laser mole removal are widely performed, but intraoperative navigation remains a significant challenge, relying heavily on the experience of surgeons. Additionally, when multiple moles are required to be removed, a more repetitive workload can occur in the surgical procedures. To address these issues, this study aims to achieve precise intraoperative navigation and to achieve modes of automatic diagnosis and localization for robotic mole removal surgery. By training the YOLO model on an open-source categorical mole dataset, this study achieves the automatic diagnosis and localization of the moles during surgeries. In the evaluation of the study, a precision of 0.839 and an average precision mAP of 0.862 which are at a high level are observed. This study verifies that the YOLO can play a critical role in enabling the automatic diagnosis and localization of robotic mole removal surgery.

Space 4.0 – a common, democratic European space, part 2

Today we are in a period of explosion of business taking advantage of the new opportunities offered by the new environment of economically open space. The period, sometimes called Space 4.0, is a paradigm shift, with changes in motivation and understanding, actors, and technology. For over a dozen years, under the name NewSpace, a revolution has been taking place in the space sector with the participation of new players, schools and universities, new commercial entrepreneurs and businesses. NewSpace entered the area traditionally occupied by OldSpace – government space agencies and large companies, testing new possibilities. These possibilities include new services, e.g. using data transmission from space, regarding communication, precise navigation, agriculture, surveillance, mapping, geology, climate, space weather, environmental monitoring, and security. The transformation of OldSpace into NewSpace was associated with the business risk of changing old, conservative business models into completely new ones, unknown in this area. The transformation is related to the need to maintain the changes in a reasonable legal system so as not to experience the „Wild West” again, this time in space. It is currently difficult to establish strict rules regarding the still distant colonization of Mars, but undoubtedly establishing rules for the use of the LEO area, i.e. the already crowded low Earth orbits, is becoming an increasingly urgent necessity. In the context of competition and technological cooperation between giga-regions, international cooperation, combating old prejudices and establishing equal opportunities, under the umbrella of social acceptance, we are building in Europe, not without difficulties, a common, democratic space.