Navigation Requirements Research Articles

Adapting a Telehealth Physical Activity and Diet Intervention to a Co-Designed Website for Self-Management After Stroke: Tutorial

People who experience a stroke are at a higher risk of recurrent stroke when compared with people who have not had a stroke. Addressing modifiable risk factors like physical inactivity and poor diet has been shown to improve blood pressure, a leading contributor to stroke. However, survivors of stroke often experience challenges with accessing risk reduction services including long wait lists, difficulty with transportation, fatigue, impaired function, and diminished exercise capacity. Providing health interventions via a website can extend the reach when compared with programs that are only offered face to face or via real-time telehealth. Given global challenges of accessing secondary prevention programs, it is important to consider alternative ways that this information can be made available to survivors of stroke worldwide. Using the “design thinking” framework and drawing on principles of the integrated knowledge translation approach, we adapted 2 co-designed telehealth programs called i-REBOUND – Let’s get moving (physical activity intervention) and i-REBOUND – Eat for health (diet Intervention) to create the i-REBOUND after stroke website. The aim of this paper is to describe the systematic process undertaken to adapt resources from the telehealth delivered i-REBOUND – Let’s get moving and i-REBOUND – Eat for health programs to a website prototype with a focus on navigation requirements and accessibility for survivors of stroke. We engaged a variety of key stakeholders with diverse skills and expertise in areas of stroke recovery, research, and digital health. We established a governance structure, formed a consumer advisory group, appointed a diverse project team, and agreed on scope of the project. Our process of adaptation had the following 3 phases: (1) understand, (2) explore, (3) materialize. Our approach considered the survivor of stroke at the center of all decisions, which helped establish guiding principles related to our prototype design. Careful and iterative engagement with survivors of stroke together with the application of design thinking principles allowed us to establish the functional requirements for our website prototype. Through user testing, we were able to confirm the technical requirements needed to build an accessible and easy-to-navigate website catering to the unique needs of survivors of stroke. We describe the process of adapting existing content and co-creating new digital content in partnership with, and featuring, people who have lived experience of stroke. In this paper, we provide a road map for the steps taken to adapt resources from 2 telehealth-delivered programs to a website format that meets specific navigation and accessibility needs of survivors of stroke.

An Adaptive and Automatic Power Supply Distribution System with Active Landmarks for Autonomous Mobile Robots.

With the development of automation and intelligent technologies, the demand for autonomous mobile robots in the industry has surged to alleviate labor-intensive tasks and mitigate labor shortages. However, conventional industrial mobile robots' route-tracking algorithms typically rely on passive markers, leading to issues such as inflexibility in changing routes and high deployment costs. To address these challenges, this study proposes a novel approach utilizing active landmarks-battery-powered luminous landmarks that enable robots to recognize and adapt to flexible navigation requirements. However, the reliance on batteries necessitates frequent recharging, prompting the development of an automatic power supply system. This system integrates omnidirectional contact electrodes on mobile robots, allowing to recharge active landmarks without precise positional alignment. Despite these advancements, challenges such as the large size of electrodes and non-adaptive battery charging across landmarks persist, affecting system efficiency. To mitigate these issues, this research focuses on miniaturizing active landmarks and optimizing power distribution among landmarks. The experimental results of this study demonstrated the effectiveness of our automatic power supply method and the high accuracy of landmark detection. Our power distribution calculation method can adaptively manage energy distribution, improving the system's persistence by nearly three times. This study aims to enhance the practicality and efficiency of mobile robot remote control systems utilizing active landmarks by simplifying installation processes and extending operational durations with adaptive and automatic power supply distribution.

A study on path-planning algorithm for a multi-section continuum robot in confined multi-obstacle environments

Abstract In confined multi-obstacle environments, generating feasible paths for continuum robots is challenging due to the need to avoid obstacles while considering the kinematic limitations of the robot. This paper deals with the path-planning algorithm for continuum robots in confined multi-obstacle environments to prevent their over-deformation. By modifying the tree expansion process of the Rapidly-exploring Random Tree Star (RRT*) algorithm, a path-planning algorithm called the continuum-RRT* algorithm herein is proposed to achieve fewer iterations and faster convergence as well as generating desired paths that adhere to the kinematic limitations of the continuum robots. Then path planning and path tracking are implemented on a tendon-driven four-section continuum robot to validate the effectiveness of the path-planning algorithm. The path-planning results show that the path generated by the algorithm indeed has fewer transitions, and the path generated by the algorithm is closer to the optimal path that satisfies the kinematic limitations of the continuum robot. Furthermore, path-tracking experiments validate the successful navigation of the continuum robot along the algorithm-generated path, exhibiting an error range of 2.51%–3.91%. This attests to the effectiveness of the proposed algorithm in meeting the navigation requirements of continuum robots.

Multi-AUVs cooperative path planning in 3D underwater terrain and vortex environments based on improved multi-objective particle swarm optimization algorithm

This paper presents an improved multi-objective particle swarm optimization (IMOPSO) algorithm for cooperative path planning of multiple autonomous underwater vehicles (multi-AUVs) in 3D underwater terrain and vortex environment. Traditional PSO algorithms often struggle with local optima in high-dimensional, complex search spaces. To address this, we propose enhancements including Sine double-sequence perturbation interpolation for initial particle population generation, adaptive differential evolution strategies, iterative local search, and traction operations to enhance global search capability and avoid local optima. Additionally, cubic spline interpolation is employed to smooth the planned paths. The objective function is tailored to the ocean currents environment, considering path length, smoothness, deflection angle, and current impact, with a penalty function method to handle multi-AUVs cooperation and environmental constraints optimization for optimal energy consumption. Simulation results demonstrate that the proposed algorithm effectively plans short-range, high-security global paths for multi-AUVs, verifying its effectiveness and practicality in meeting the navigation requirements in ocean currents environments.

SceneFusion: Room-Scale Environmental Fusion for Efficient Traveling Between Separate Virtual Environments.

Traveling between scenes has become a major requirement for navigation in numerous virtual reality (VR) social platforms and game applications, allowing users to efficiently explore multiple virtual environments (VEs). To facilitate scene transition, prevalent techniques such as instant teleportation and virtual portals have been extensively adopted. However, these techniques exhibit limitations when there is a need for frequent travel between separate VEs, particularly within indoor environments, resulting in low efficiency. In this article, we first analyze the design rationale for a novel navigation method supporting efficient travel between virtual indoor scenes. Based on the analysis, we introduce the SceneFusion technique that fuses separate virtual rooms into an integrated environment. SceneFusion enables users to perceive rich visual information from both rooms simultaneously, achieving high visual continuity and spatial awareness. While existing teleportation techniques passively transport users, SceneFusion allows users to actively access the fused environment using short-range locomotion techniques. User experiments confirmed that SceneFusion outperforms instant teleportation and virtual portal techniques in terms of efficiency, workload, and preference for both single-user exploration and multi-user collaboration tasks in separate VEs. Thus, SceneFusion presents an effective solution for seamless traveling between virtual indoor scenes.

Nonuniformity of diffractive splitting and receiver-transmitter alignment for large-field-of-view hundred-beam-scale LiDAR

To meet the spatial perception requirements for autonomous ship navigation in common scenarios such as ocean navigation, port entry and exit, and lock passage, commercial vehicle-mounted LiDAR technology falls short of demands in aspects such as sensing distance, field of view, angular resolution, and spatial sampling density. This paper proposes a hundred-beam-scale LiDAR scheme based on large-field-of-view diffractive beam splitting and a fiber array for echo reception and presents an in-depth investigation of the angular nonuniformity of diffractive beam splitting and the microradian-scale alignment for such hundred-beam-scale large-field-of-view LiDAR. This paper considers a combination of split-beam transmission based on a high-order diffractive optical element (DOE) and echo reception based on a high-precision fiber-optic line array. The nonlinear angular characteristics of the DOE are deduced and analyzed for a large field of view. The units of the receiving fiber-optic array are designed to offset the influence of the angular nonlinearity of the DOE, ensuring high-precision receiver–transmitter alignment of the hundred-beam-scale LiDAR for beams at any order of diffraction and helping to reduce system errors. The above-described LiDAR system has undergone laboratory testing and practical engineering verification, and it provides a new optical solution for LiDAR systems at the hundred-beam scale with a large field of view, a small divergence angle, and high sampling density. The presented system achieves a registration accuracy of 66.5 μrad with 128 beams and a 10-degree field of view, greatly improving signal reception efficiency. Such LiDAR systems have a wide range of applications, including space docking, target identification, lunar and planetary exploration, and ground-based vehicle-mounted LiDAR.

A curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields

Due to the undulating topography, the world contains numerous irregularly shaped small fields. Autonomous navigation along the curved edges of these fields is crucial for achieving complete unmanned operation in these small-field environments. Based on this, we proposed an accurate and robust curved path extraction method using RGB-D multimodal data for single-edge guided navigation in irregularly shaped fields. Leveraging the significant height difference between headland and farmland, we employed an efficient RGB-D multimodal semantic segmentation network to achieve high-precision segmentation of headland area. Meanwhile, a slope-based feature points adaptive selection algorithm and a feature points transformation algorithm fusing multimodal depth data were proposed innovatively for curved navigation path extraction. By utilizing the RGB-D segmentation model ESANet, we achieved a mean intersection over union (mIoU) of 96.94 %, surpassing other RGB segmentation models. The mean deviations between the ground truth path and the extracted path were less than 6.43 pixels on an image with a resolution of 640 × 480. The proposed method can finally achieve a processing efficiency of 23.80 FPS on images in 640 × 480 resolution, and the field experiments tested that the mean deviations of the agricultural vehicle navigating along the extracted curved navigation path were less than 0.188 m, which met the real-time performance requirement and accuracy requirement of autonomous navigation.

Research on four-sided differential localization algorithm for automated guided vehicles based on ultra-wideband

Abstract To meet the high-precision positioning and navigation requirements of automated guided vehicles (AGV) in environments such as factories and workshops, this study utilizes ultra-wideband (UWB) positioning technology and designs a multi-tag differential localization algorithm to enhance the positioning accuracy of AGV. Starting from the current research status of AGV positioning and navigation, the paper provides a detailed introduction to the concept, process, and practical applications of the UWB differential localization algorithm. The maximum static positioning accuracy for UWB is 64 mm, and the maximum dynamic positioning accuracy is 222 mm, thereby meeting the requirements for AGV positioning and navigation.

A Visual-Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System.

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.

Improved A* Navigation Path-Planning Algorithm Based on Hexagonal Grid

Navigation systems are extensively used in everyday life, but the conventional A* algorithm has several limitations in path planning applications within these systems, such as low degrees of freedom in path planning, inadequate consideration of the effects of special regions, and excessive nodes and turns. Addressing these limitations, an enhanced A* algorithm was proposed using regular hexagonal grid mapping. First, the approach to map modeling using hexagonal grids was described. Subsequently, the A* algorithm was refined by optimizing the calculation of movement costs, thus allowing the algorithm to integrate environmental data more effectively and flexibly adjust node costs while ensuring path optimality. A quantitative method was also introduced to assess map complexity and adaptive heuristics that decrease the number of traversed nodes and increase the search speed. Moreover, a turning penalty measure was implemented to minimize unnecessary turns on the planned paths. Simulation results confirmed that the improved A* algorithm exhibits superior performance, which can dynamically adjust movement costs, enhance search efficiency, reduce turns, improve overall path planning quality, and solve critical path planning issues in navigation systems, greatly aiding the development and design of these systems and making them better suited to meet modern navigation requirements.

Local Path Planner for Mobile Robot Considering Future Positions of Obstacles

Local path planning is a necessary ability for mobile robot navigation, but existing planners are not sufficiently effective at dynamic obstacle avoidance. In this article, an improved timed elastic band (TEB) planner based on the requirements of mobile robot navigation in dynamic environments is proposed. The dynamic obstacle velocities and TEB poses are fully integrated through two-dimensional (2D) lidar and multi-obstacle tracking. First, background point filtering and clustering are performed on the lidar points to obtain obstacle clusters. Then, we calculate the data association matrix of the obstacle clusters of the current and previous frame so that the clusters can be matched. Thirdly, a Kalman filter is adopted to track clusters and obtain the optimal estimates of their velocities. Finally, the TEB poses and obstacle velocities are associated: we predict the obstacle position corresponding to the TEB pose through the detected obstacle velocity and add this constraint to the corresponding TEB pose vertex. Then, a pose sequence considering the future positions of obstacles is obtained through a graph optimization algorithm. Compared with the original TEB, our method reduces the total running time by 22.87%, reduces the running distance by 19.23%, and increases the success rate by 21.05%. Simulations and experiments indicate that the improved TEB enables robots to efficiently avoid dynamic obstacles and reach the goal as quickly as possible.

RFG-TVIU: robust factor graph for tightly coupled vision/IMU/UWB integration.

High precision navigation and positioning technology, as a fundamental function, is gradually occupying an indispensable position in the various fields. However, a single sensor cannot meet the navigation requirements in different scenarios. This paper proposes a "plug and play" Vision/IMU/UWB multi-sensor tightly-coupled system based on factor graph. The difference from traditional UWB-based tightly-coupled models is that the Vision/IMU/UWB tightly-coupled model in this study uses UWB base station coordinates as parameters for real-time estimation without pre-calibrating UWB base stations. Aiming at the dynamic change of sensor availability in multi-sensor integrated navigation system and the serious problem of traditional factor graph in the weight distribution of observation information, this study proposes an adaptive robust factor graph model. Based on redundant measurement information, we propose a novel adaptive estimation model for UWB ranging covariance, which does not rely on prior information of the system and can adaptively estimate real-time covariance changes of UWB ranging. The algorithm proposed in this study was extensively tested in real-world scenarios, and the results show that the proposed system is superior to the most advanced combination method in all cases. Compared with the visual-inertial odometer based on the factor graph (FG-VIO), the RMSE is improved by 62.83 and 64.26% in scene 1 and 82.15, 70.32, and 75.29% in scene 2 (non-line-of-sight environment).

Design and Experiment of an Autonomous Navigation System for a Cattle Barn Feed-Pushing Robot Based on UWB Positioning

The autonomous navigation system of feed-pushing robots is one of the key technologies for the intelligent breeding of dairy cows, and its accuracy has a significant influence on the quality of feed-pushing operations. Currently, the navigation methods of feed-pushing robots in the complex environment of cattle barns mainly include visual, LiDAR, and geomagnetic navigation, but there are still problems relating to low navigation accuracy. An autonomous navigation system based on ultra-wideband (UWB) positioning utilizing the dynamic forward-looking distance pure pursuit algorithm is proposed in this paper. First, six anchor nodes were arranged in the corners and central feeding aisle of a 30 × 86 m rectangular standard barn to form a rectangular positioning area. Then, utilizing the 9ITL-650 feed-pushing robot as a platform and integrating UWB wireless positioning technology, a global coordinate system for the cattle barn was established, and the expected path was planned. Finally, the pure pursuit model was improved based on the robot’s two-wheel differential kinematics model, and a dynamic forward-looking distance pure pursuit controller based on PID regulation was designed to construct a comprehensive autonomous navigation control system. Subsequently, field experiments were conducted in the cattle barn. The experimental results show that the static positioning accuracy of the UWB system for the feed-pushing robot was less than 16 cm under no-line-of-sight conditions in the cattle barn. At low speeds, the robot was subjected to linear tracking comparative experiments with forward-looking distances of 50, 100, 150, and 200 cm. The minimum upper-line distance of the dynamic forward-looking distance model was 205.43 cm. In the steady-state phase, the average lateral deviation was 3.31 cm, with an average standard deviation of 2.58 cm and the average root mean square error (RMSE) of 4.22 cm. Compared with the fixed forward-looking distance model, the average lateral deviation, the standard deviation, and the RMSE were reduced by 42.83%, 37.07%, and 42.90%, respectively. The autonomous navigation experiments conducted on the feed-pushing robot at travel speeds of 6, 8, and 10 m/min demonstrated that the maximum average lateral deviation was 7.58 cm, the maximum standard deviation was 8.22 cm, and the maximum RMSE was 11.07 cm, meeting the autonomous navigation requirements for feed-pushing operations in complex barn environments. This study provides support for achieving high-precision autonomous navigation control technology in complex environments.

Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation.

Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.

Target location method of intelligent deicing robot based on nonlinear auto disturbance rejection neural network

A visual navigation scheme for intermittent walking deicing robots was designed to address the issues of multiple iterations, long computation time, and poor real-time performance in the nonlinear optimization of the original SLAM system. A computer image acquisition unit, a computer image processing module, and a visual navigation parameter extraction algorithm were also designed. Implemented a visual navigation system for deicing robots based on image processing. It can meet the navigation requirements of the deicing robot. Simulation and experiments have shown that the method proposed in this paper can quickly and accurately identify abnormal point clouds. The visual servo control scheme constructed in this paper is aimed at robot task operations with unknown system calibration and target depth information. By calibrating the robot vision system, the conversion between camera pixel coordinates and robot base coordinates is achieved, with a transmission frame rate of 53.65 per second; The maximum error in positioning accuracy in space is 10.6 mm. The feature trajectory of visual space is smooth and stable within the camera's field of view, and the end movement of Cartesian space robots is stable without rebound, resulting in high grasping and positioning accuracy.

RL-TEE: Autonomous Probe Guidance for Transesophageal Echocardiography Based on Attention-Augmented Deep Reinforcement Learning

Ultrasound image acquisition in conventional transesophageal echocardiography (TEE) requires complex manual operation of the probe in the esophagus based on the interpretation of ultrasound images and in-depth knowledge of the cardiac anatomy. In this work, we formulate the TEE probe guidance task as a reinforcement learning (RL) problem, and present the first learning-based solution to 3-DOF control of a TEE probe based on the ultrasound image feedback, named RL-TEE, in order to mimic the visual search and navigation strategies of expert echocardiographers. The probe-tissue interaction in TEE is carefully modeled in our framework by considering both the requirements for navigation towards the standard views and compliance in the esophageal environment. Furthermore, we propose a hybrid deep Q-network model that augments a convolutional neural network backbone with self-attention mechanisms to better capture spatial information in ultrasound images to guide navigation decisions. The presented methods are preliminarily validated in a TEE simulation environment built with data from 25 subjects to acquire four standard views of the heart. Our results show that the proposed method can effectively learn to accurately and compliantly guide the probe movement for TEE standard view acquisition tasks and has a good generalization ability to unseen patient data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to realize 3-DOF movement guidance of a TEE probe to acquire the standard views of the heart based on the real-time images, which can be applied to existing robotic control systems or used to assist novice echocardiographers in TEE examination, thereby relieving operator workload and improving ease of use. This paper suggests a novel approach that uses the deep RL technique to achieve automatic interpretation of TEE images and intelligent guidance of the probe movement. The RL framework is designed to take into account both the navigation efficiency and compliance with the esophageal environment for the targeted intracorporeal application. A hybrid deep Q-network model that augments a convolutional neural network with attention mechanisms is designed to better capture spatial information from ultrasound images to predict the probe movement. The effectiveness of the framework is preliminarily validated in extensive experiments in a simulation environment built with real patient data. The proposed method can be applied in clinical use to provide real-time TEE probe guidance for novice echocardiographers, and can be integrated with a robotic system to fully automate the TEE acquisition, thereby relieving the doctors from tedious manual operation to focus on the diagnosis and treatment.

GNSS/5G Joint Position Based on Weighted Robust Iterative Kalman Filter

The Global Navigation Satellite System (GNSS) is widely used for its high accuracy, wide coverage, and strong real-time performance. However, limited by the navigation signal mechanism, satellite signals in urban canyons, bridges, tunnels, and other environments are seriously affected by non-line-of-sight and multipath effects, which greatly reduce positioning accuracy and positioning continuity. In order to meet the positioning requirements of human and vehicle navigation in complex environments, it was necessary to carry out this research on the integration of multiple signal sources. The Fifth Generation (5G) signal possesses key attributes, such as low latency, high bandwidth, and substantial capacity. Simultaneously, 5G Base Stations (BSs), serving as a fundamental mobile communication infrastructure, extend their coverage into areas traditionally challenging for GNSS technology, including indoor environments, tunnels, and urban canyons. Based on the actual needs, this paper proposes a system algorithm based on 5G and GNSS joint positioning, aiming at the situation that the User Equipment (UE) only establishes the connection with the 5G base station with the strongest signal. Considering the inherent nonlinear problem of user position and angle measurements in 5G observation, an angle cosine solution is proposed. Furthermore, enhancements to the Sage–Husa Adaptive Kalman Filter (SHAKF) algorithm are introduced to tackle issues related to observation weight distribution and adaptive updates of observation noise in multi-system joint positioning, particularly when there is a lack of prior information. This paper also introduces dual gross error detection adaptive correction of the forgetting factor based on innovation in the iterative Kalman filter to enhance accuracy and robustness. Finally, a series of simulation experiments and semi-physical experiments were conducted. The numerical results show that compared with the traditional method, the angle cosine method reduces the average number of iterations from 9.17 to 3 with higher accuracy, which greatly improves the efficiency of the algorithm. Meanwhile, compared with the standard Extended Kalman Filter (EKF), the proposed algorithm improved 48.66%, 35.17%, and 38.23% at 1σ/2σ/3σ, respectively.

Research on a Visual/Ultra-Wideband Tightly Coupled Fusion Localization Algorithm.

In the autonomous navigation of mobile robots, precise positioning is crucial. In forest environments with weak satellite signals or in sites disturbed by complex environments, satellite positioning accuracy has difficulty in meeting the requirements of autonomous navigation positioning accuracy for robots. This article proposes a vision SLAM/UWB tightly coupled localization method and designs a UWB non-line-of-sight error identification method using the displacement increment of the visual odometer. It utilizes the displacement increment of visual output and UWB ranging information as measurement values and applies the extended Kalman filtering algorithm for data fusion. This study utilized the constructed experimental platform to collect images and ultra-wideband ranging data in outdoor environments and experimentally validated the combined positioning method. The experimental results show that the algorithm outperforms individual UWB or loosely coupled combination positioning methods in terms of positioning accuracy. It effectively eliminates non-line-of-sight errors in UWB, improving the accuracy and stability of the combined positioning system.

RADAR-BASED DETECTION AND RECOGNITION METHODOLOGY OF AUTONOMOUS SURFACE VEHICLES IN CHALLENGING MARINE ENVIRONMENT

This paper presents a methodology that combines radar polarization selection and recognition techniques for navigating objects in atmospheric formations, with a special focus on unmanned surface vehicles (ASVs). The proposed technique utilizes the concept of an energy dissipation matrix to represent these objects as characteristic “shiny dots”. By strategically changing the polarization of the emitted and received electromagnetic waves, the resulting echo energy dissipation matrix is determined. This approach allows the formation of an intensity-based repository of atmospheric formations, which gives SRPC a complete set of tools to account for atmospheric conditions in radar identification of remote objects, including ASVs. The practical application of this technique extends to the improvement of a distinct class of shipborne radar systems optimized for ASVs and their specific navigation requirements. Ultimately, this technology bridges the gap between advanced radar technology and the emerging field of unmanned ground vehicles, providing safer and more proficient navigation in challenging weather conditions.

Analysis of the Technical Accuracy of a Patient-Specific Stereotaxy Platform for Brain Biopsy.

The use of stereotactic frames is a common practice in neurosurgical interventions such as brain biopsy and deep brain stimulation. However, conventional stereotactic frames have been shown to require modification and adaptation regarding patient and surgeon comfort as well as the increasing demand for individualized medical treatment. To meet these requirements for carrying out state-of-the-art neurosurgery, a 3D print-based, patient-specific stereotactic system was developed and examined for technical accuracy. Sixteen patient-specific frames, each with two target points, were additively manufactured from PA12 using the Multi Jet Fusion process. The 32 target points aim to maximize the variability of biopsy targets and depths for tissue sample retrieval in the brain. Following manufacturing, the frames were measured three-dimensionally using an optical scanner. The frames underwent an autoclave sterilization process prior to rescanning. The scan-generated models were compared with the planned CAD models and the deviation of the planned target points in the XY-plane, Z-direction and in the resulting direction were determined. Significantly lower (p < 0.01) deviations were observed when comparing CAD vs. print and print vs. sterile in the Z-direction (0.17 mm and 0.06 mm, respectively) than in the XY-plane (0.46 mm and 0.16 mm, respectively). The resulting target point deviation (0.51 mm) and the XY-plane (0.46 mm) are significantly higher (p < 0.01) in the CAD vs. print comparison than in the print vs. sterile comparison (0.18 mm and 0.16 mm, respectively). On average, the results from the 32 target positions examined exceeded the clinically required accuracy for a brain biopsy (2 mm) by more than four times. The patient-specific stereotaxic frames meet the requirements of modern neurosurgical navigation and make no compromises when it comes to accuracy. In addition, the material is suitable for autoclave sterilization due to resistance to distortion.