Real-time Navigation Research Articles

Simulation for Foregut and Bariatric Surgery: Current Status and Future Directions.

Simulation offers the opportunity to practice in a safe, controlled, and standardized environment. Surgical simulation, in particular, is very attractive because it avoids learning and practicing surgical skills in the operating room. Many simulators are currently available such as box-lap trainers, virtual-reality platforms, cadavers, live animals, animal-based tissue blocks, and synthetic/artificial models. Endoscopic interventions can be practiced with high-fidelity virtual simulators. Box-lap trainers help practicing basic laparoscopic skills. Cadavers and live animals offer realism to train entire foregut and bariatric procedures. However, limited availability and high expenses often restrict their use. Ex vivo simulators with animal tissue blocks have been recently developed and appear to be a realistic and cost-effective alternative. Three-dimensional printing and real-time navigation systems have also emerged as promising training tools. Overall, further efforts are needed to develop a formal simulation curriculum with validated simulators for foregut and bariatric surgery.

A Dynamic Day-To-Day Departure Time and Route Choice Model for Bounded-Rational Individuals

Accurate prediction of travellers’ day-to-day departure time and route choice is critical in advanced traffic management systems. There have been several related works about route choice with the assumption that the departure time for individual travellers is known beforehand. With real-time traffic state information provided by navigation systems and previous historical experience, travellers will dynamically update their departure time, which is neglected in existing works. In this study, we aim to describe travellers’ spatial-temporary choice behaviour taking navigation information into account and propose a bounded-rational day-to-day dynamic learning and adjustment model. The new model contains three steps. First, the real-time navigation guidance on each discrete day is obtained, and the self-learned experience of travellers’ choices with navigation information is presented; then, the day-to-day revision process of the choices is derived to maximize departure and route choice prospect; next, by aggregating each individual’s behaviour and calculating route choice probability, a bounded-rational continuous day-to-day dynamic model is provided. Numerical experiments suggest that the proposed model converges to a spatial-temporal oscillating equilibrium not a fixed-point stable status, and the final equilibrium trend is different from classical user equilibrium. The findings of the study are helpful to improve the prediction accuracy of traffic state in urban street networks.

Performance evaluation for vehicular ad-hoc networks based routing protocols

VANET is a branch of MANETS, where each vehicle is a node, and a wireless router will run. The vehicles are similar to each other will interact with a wide range of nodes or vehicles and establish a network. VANETs provide us with the infrastructure to build new solutions for improving safety and comfort for drivers and passengers. There are several routing protocols proposed and evaluated for improving VANET's performance. The simulator is preferred over external experience because it is easy, simple, and inexpensive. In this paper, we choose AODV protocol, DSDV protocol, and DSR protocol with five different nodes density. For each protocol, as regards specific parameters like (throughput, packet delivery ratio, and end- to- end delay). On simulators that allow users to build real-time navigation models of simulations using VANET. Tools (SUMO, MOVE, and NS-2) were used for this paper, then graphs were plotted for evaluation using Trace-graph. The results showed the DSR is much higher than AODV and DSDV, In terms of throughput. While DSDV is the best choice because of the low average end to end delay. From the above, we conclude that each strategy has its own negative and positive aspects that make it ideally suited to a particular scenario than other scenarios.

Responding to Illegal Activities Along the Canadian Coastlines Using Reinforcement Learning

This article elaborates on how machine learning (ML) can leverage the solution of a contemporary problem related to the security of maritime domains. The worldwide ``Illegal, Unreported, and Unregulated'' (IUU) fishing incidents have led to serious environmental and economic consequences which involve drastic changes in our ecosystems in addition to financial losses caused by the depletion of natural resources. The Fisheries and Aquatic Department (FAD) of the United Nation's Food and Agriculture Organization (FAO) issued a report which indicated that the annual losses due to IUU fishing reached $25 Billion. This imposes negative impacts on the future-biodiversity of the marine ecosystem and domestic Gross National Product (GNP). Hence, robust interception mechanisms are increasingly needed for detecting and pursuing the unrelenting illegal fishing incidents in maritime territories. This article addresses the problem of coordinating the motion of a fleet of marine vessels (pursuers) to catch an IUU vessel while still in local waters. The problem is formulated as a pursuer-evader problem that is tackled within an ML framework. One or more pursuers, such as law enforcement vessels, intercept an evader (i.e., the illegal fishing ship) using an online reinforcement learning mechanism that is based on a value iteration process. It employs real-time navigation measurements of the evader ship as well as those of the pursuing vessels and returns back model-free interception strategies.

Navigable Space Construction from Sparse Noisy Point Clouds

Robot navigation with noisy perception is a fundamental but challenging task. We present a framework for creating navigable space from cluttered point clouds generated by low-end sensors with high sparsity and noise. Our method incrementally seeds and creates local convex regions free of obstacle points along robot's trajectory. Then a dense version of the point cloud is reconstructed through a map point regulation process where the original noisy map points are first projected onto a series of local convex hull surfaces, after which those points falling inside the convex hulls are culled. We have tested our proposed framework using a public autonomous driving dataset and a manually built structured environment for various performance evaluation, as well as inside our research building for ground robot navigation tasks. Our results reveal that the reconstructed navigable space has small volume loss (error) comparing with the ground truth, and the method is highly efficient, allowing real-time navigation computation.

Intraoperative real-time navigation and intraoperative three-dimensional imaging for patient-specific total temporomandibular joint replacement

Customized solutions for replacement of the temporomandibular joint (TMJ) along with surgical guides enable precise and fast transfer of the virtual plan to the patient. However, these guides lack information on screw vectors and length, and well-defined borders for bony resections towards the medial skull base. This retrospective study was performed to investigate the feasibility and benefit of real-time navigation and intraoperative three-dimensional imaging during total TMJ replacement (TJR), as well as patient clinical outcomes. Between 2016 and 2020, 26 customized prostheses were implanted in 21 patients either with or without real-time navigation and instrument tracking. The clinical, surgical, radiological, and navigational data were analysed. The accuracy of navigation registration with instrument tracking, precision of screw insertion, and implant and screw positions were analysed by fusion of the virtual plan and surgical outcome. Real-time navigation aided orientation during lateral skull base dissection and resection. However, the results of real-time navigation-aided drilling were inconclusive regarding vector and length control. At a mean 15.3±3.0 months of follow-up, average mouth opening had improved from 21.69±2.80mm to 36.40±1.25mm; the average pain score decreased from 6.18±0.74 to 1.06±0.52. Thus, intraoperative real-time navigation for TJR assists lateral skull base dissection and resection.

Ultrasound Doppler-guided real-time navigation of a magnetic microswarm for active endovascular delivery.

Swarming micro/nanorobots offer great promise in performing targeted delivery inside diverse hard-to-reach environments. However, swarm navigation in dynamic environments challenges delivery capability and real-time swarm localization. Here, we report a strategy to navigate a nanoparticle microswarm in real time under ultrasound Doppler imaging guidance for active endovascular delivery. A magnetic microswarm was formed and navigated near the boundary of vessels, where the reduced drag of blood flow and strong interactions between nanoparticles enable upstream and downstream navigation in flowing blood (mean velocity up to 40.8 mm/s). The microswarm-induced three-dimensional blood flow enables Doppler imaging from multiple viewing configurations and real-time tracking in different environments (i.e., stagnant, flowing blood, and pulsatile flow). We also demonstrate the ultrasound Doppler-guided swarm formation and navigation in the porcine coronary artery ex vivo. Our strategy presents a promising connection between swarm control and real-time imaging of microrobotic swarms for localized delivery in dynamic environments.

Constrained App Data Caching Over Edge Server Graphs in Edge Computing Environment

In recent years, edge computing, as an extension of cloud computing, has emerged as a promising paradigm for powering a variety of applications demanding low latency, e.g., virtual or augmented reality, interactive gaming, real-time navigation, etc. In the edge computing environment, edge servers are deployed at base stations to offer highly-accessible computing capacities to nearby end-users, e.g., CPU, RAM, storage, etc. From a service provider’s perspective, caching app data on edge servers can ensure low latency in its users’ data retrieval. Given constrained cache spaces on edge servers due to their physical sizes, the optimal data caching strategy must minimize overall user latency. In this article, we formulate this Constrained Edge Data Caching (CEDC) problem as a constrained optimization problem from the service provider’s perspective and prove its <inline-formula><tex-math notation="LaTeX">$\mathcal {NP}$</tex-math></inline-formula> -hardness. We propose an optimal approach named CEDC-IP to solve this CEDC problem with the Integer Programming technique. We also provide an approximation algorithm named CEDC-A for finding approximate solutions to large-scale CEDC problems efficiently and prove its approximation ratio. CEDC-IP and CEDC-A are evaluated on a real-world data set. The results demonstrate that they significantly outperform four representative approaches.

FaSTrack:A Modular Framework for Real-Time Motion Planning and Guaranteed Safe Tracking

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">planning model</i> of the system. The plan is tracked by the system, represented by a more realistic, higher dimensional <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tracking model</i> . We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton–Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs.

Prediction of BeiDou Satellite Orbit Maneuvers to Improve the Reliability of Real-Time Navigation Products

The positioning, navigation, and timing (PNT) service of the Global Navigation Satellite System (GNSS) is developing in the direction of real time and high precision. However, there are some problems that restrict the development of real-time and high-precision PNT technology. Satellite orbit maneuvering is one of the factors that reduce the reliability of real-time navigation products, especially the high-frequency orbit maneuvering of geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites. The BeiDou Navigation Satellite System (BDS) constellation is designed to contain GEO, IGSO, and medium earth orbit (MEO). These orbit maneuvers bring certain difficulties for data processing, especially for BeiDou satellites, such as decreased real-time service performance, which results in real-time navigation products including unusable maneuvered satellites. Additionally, the performance of real-time navigation products will decrease because the orbit maneuvers could not be known in advance, which diminishes the real-time PNT service performance of BDS for users. Common users cannot obtain maneuvering times and strategies owing to confidentiality, which can lead to a decline in the BDS real-time service performance. Thus, we propose a method to predict orbit maneuvers. BDS data from the broadcast ephemeris were analyzed to verify the availability of the proposed method. In addition, the results of real-time positioning were analyzed by using ultra-rapid orbit products, demonstrating that their reliability is improved by removing maneuvered satellites in advance. This is vital to improve the reliability of real-time navigation products and BDS service performance.

A Moonshot? [Turnstile

Discusses the current activities of Bell Labs. Owned currently by the Finnish company, Nokia, it recently received a US$14.1 million contract from NASA to design and develop a 4G network on the moon. According to Bell Labs, astronauts will use its wireless network for data transmission, controlling of lunar rovers, real time navigation over lunar geography (think Google Maps for the moon), and streaming of highdefinition video It is all a part of the long-term NASA plan, “which entails 37 launches of private and NASA rockets, as well as a mix of robotic and human landers with a ‘Lunar Surface Asset Deployment’ in 2028, likely the beginning of a surface outpost.

Application of three-dimensional reconstruction with a Hisense computer-assisted system in upper pancreatic lymph node dissection during laparoscopic-assisted radical gastrectomy

Background/objectiveTo investigate the feasibility of three-dimensional (3D) reconstruction with an interactive Hisense computer-assisted system (CAS) for preoperative planning and intraoperative guidance during laparoscopic-assisted upper pancreatic lymph node dissection in distal gastrectomy for gastric cancer. MethodsThis study included 28 patients who underwent preoperative 3D reconstruction of the upper border of the pancreas using Hisense CAS (3D reconstruction group) for preoperative planning and intraoperative navigation. To determine its efficacy, the clinical data of these patients were compared with those of 28 patients who did not undergo 3D reconstruction (control group). ResultsFifty-six cases of laparoscopic-assisted distal gastrectomy were performed. Three-dimensional reconstruction was successful in all the patients in the 3D reconstruction group, and real-time navigation was performed during the operation. The rate of correspondence between the 3D reconstruction images and intraoperative findings was 100%. The time taken for upper pancreatic lymph node dissection, number of upper pancreatic lymph node dissections, and number of unnecessary injuries during surgery were superior in the 3D reconstruction group than in the control group. The results of the remaining parameters were not statistically significant. ConclusionPreoperative planning with interactive Hisense CAS 3D reconstruction technology can improve surgeons’ understanding of each patient’s individual anatomy and can reveal anatomical variations, which is helpful for accurate preoperative planning and intraoperative navigation. This technique is helpful for the implementation of the precise dissection of lymph nodes at the upper edge of the pancreas and improves the quality and safety of the surgery.

Evaluation of smartphone interactions on drivers\u2019 brain function and vehicle control in an immersive simulated environment

Smartphones and other modern technologies have introduced multiple new forms of distraction that color the modern driving experience. While many smartphone functions aim to improve driving by providing the driver with real-time navigation and traffic updates, others, such as texting, are not compatible with driving and are often the cause of accidents. Because both functions elicit driver attention, an outstanding question is the degree to which drivers’ naturalistic interactions with navigation and texting applications differ in regard to brain and behavioral indices of distracted driving. Here, we employed functional near-infrared spectroscopy to examine the cortical activity that occurs under parametrically increasing levels of smartphone distraction during naturalistic driving. Our results highlight a significant increase in bilateral prefrontal and parietal cortical activity that occurs in response to increasingly greater levels of smartphone distraction that, in turn, predicts changes in common indices of vehicle control.

Characterization of amplitude scintillation and distribution of positioning error for IRNSS/GPS/SBAS receiver

Indian regional navigation satellite system (IRNSS) or Navigation with Indian Constellation (NaVIC) is the satellite-based navigation system, established and controlled by the Indian Space Research Organization. It provides services of positioning, velocity, and timing for civilian and military applications. Ionospheric irregularities cause the fluctuations in the amplitude and phase of the received navigation signals; this phenomenon is known as scintillation. Scintillation can create the error in precise positioning and degrades the position accuracy of the navigation system. It varies with solar activity, geographical location, seasonal variation, day–night time, and elevation angle of satellites. The S4 index is one of the key parameters to estimate the amplitude scintillation to recognize the quality of the navigation signal. In this paper, amplitude scintillation characteristics are studied with IRNSS/GPS/SBAS in the Ahmedabad region which is at the average altitude of 53 m. S4 index is calculated and analyzed using the real-time navigation data of one week. Here, IRNSS/GPS/SBAS receiver with L1, L5, and S-band, is used to collect the data at low altitude station (IITRAM, Ahmedabad). The position error of normal sunny days is calculated using IRNSS/GPS/SBAS receiver. The probability of position error is also derived and found that it follows the gamma distribution function and validate it statistically using a chi-square test. This study also focuses on the effect of elevation of the satellite on the value of carrier to noise ratio at the location of IITRAM.

Simulation and experimental approach for optimal path planning of UAV using A* and MEA* algorithms

Over the past decades, Unmanned Aerial Vehicle (UAV) have been effectively adapted to perform disaster missions, agricultural and various societal applications. The path planning plays a crucial role in bringing autonomy to the UAVs to attain the designated tasks by avoiding collision in the obstacles prone regions. Optimal path planning of UAV is considered to be a challenging issue in real time navigation during obstacle prone environments. The present article focused on implementing a well-known A* and variant of A* namely MEA* algorithm to determine an optimal path in the varied obstacle regions for the UAV applications which is novel. Simulation is performed to investigate the performance of each algorithm with respect to comparing their execution time, total distance travelled and number of turns made to reach the source to target. Further, experimental flight trails are made to examine the performance of these algorithms using a UAV. The desired position, velocity and yaw of UAV is obtained based on the waypoints of optimal path planned data and effective navigation is performed. The simulation and experimental results are compared for confirming the effectiveness of these algorithms.

Real-time navigation by three-dimensional virtual reconstruction models in robot-assisted laparoscopic pyeloplasty for ureteropelvic junction obstruction: our initial experience.

BackgroundThe aim of this study was to evaluate the effectiveness and safety of real-time surgical navigation by three-dimensional (3D) virtual reconstruction models in robot-assisted laparoscopic pyeloplasty (RALP).MethodsBetween November 2018 and January 2020, 38 patients with ureteropelvic junction obstruction (UPJO) who underwent RALP were retrospectively enrolled. The operations were assisted in real time by 3D models in 16 patients, while 22 patients underwent surgery without navigation. Based on whether patients had a prior intervention history, crossing vessels or congenital deformities, we further divided them into the “complicated UPJO” cohort and the “regular UPJO” cohort for subgroup analysis. The demographic characteristics, intraoperative parameters, perioperative data and follow-up data were recorded and compared between the groups.ResultsAll of the procedures were successfully performed without open or laparoscopic conversion. The mean dissection time to the UPJ was shorter in the navigation group than in the non-navigation group, both in the whole cohort (15.3 vs. 24.8 min, P=0.011) and in the complicated cohort (15.4 vs. 27.5 min, P=0.004), while there was no significant difference in the regular cohort. The overall operative time and estimated blood loss in the navigation group tended to be less, although the difference was not statistically significant. No difference in anastomosis time, postoperative hospital stay or complications was noted between the two groups in either cohort. At a mean follow-up of 11.2 months, the overall success rate was 94.7% (36/38), and there was no significant difference between the two groups.ConclusionsReal-time navigation by 3D virtual reconstruction models might be helpful to improve surgical efficiency and safety of RALP by facilitating the dissection around the UPJ, especially for cases of complicated UPJO. However, the prospective study with larger sample size is further needed to confirm the results.

EPNS: Efficient Privacy Preserving Intelligent Traffic Navigation from Multiparty Delegated Computation in Cloud-Assisted VANETs

Real-time navigation is a fundamental service with the emerging techniques of intelligent transportation and crowdsensing. Unfortunately, the breach of location privacy significantly impedes its wide adoption. Most existing state-of-the-art exploit either pseudonyms or public key fully homomorphic encryption (FHE) to protect location privacy, which requires an online certificate authority (CA) or loads intolerably heavy computational/communication overhead on resource-constrained vehicular users. In this paper, a new cryptographic primitive named efficient multiparty delegated computation (MPDC) is firstly proposed, where any one-way trapdoor permutation is required to perform constant times (i.e. twice) on each resource-constrained data provider, to encrypt a batch of messages and enable types of secure evaluations over ciphertexts encrypted under multiple keys of multiple parties. Based on MPDC, we devise a lightweight privacy-preserving real-time intelligent traffic navigation scheme (EPNS) in cloud-assisted VANETs. The proposed approach predicts an optimal driving route of shortest time without disclosing either vehicular users' private location or the navigation result to the collusion between the semi-trusted cloud/cryptography service provider (CSP) and unauthorized users, by securely evaluating auto-regression moving average (ARMA) model with spatiotemporal correlations of high accuracy and efficiency. Finally, formal security proofs and the extensive evaluations demonstrate the utility and the practicability of our approaches.

IEEE Access Special Section Editorial: Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems

The topic of Artificial Intelligence (AI)-Empowered Intelligent Transportation Systems (ITS) has drawn more attention recently, with the rapid development of ubiquitous networks and smart vehicles. Researchers around the world have been working on new automotive applications to create a comfortable and safer driving environment. Current challenges include: how to run computing-intensive applications on vehicles; how to enable real-time feedback between vehicles and the traffic management server based on the current Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication modes; and how to provide efficient computing capabilities for resource-consumption applications and reasonable resource allocation for vehicles and infrastructures. Recently, AI has made remarkable achievements in many fields such as image processing, pattern recognition, and natural language processing. It is also involved in computing-intensive applications, such as autopilot and real-time navigation through V2V or V2I. However, AI-Empowered ITS is still in its infancy. How can AI be integrated with ITS and function well in dynamic vehicular network scenarios? In addition, there are still questions on how to design more efficient AI solutions for resource management and coordination in ITS.

Review of Aboriginal astronomy and navigation: A Western Australian focus

AbstractThis review of Aboriginal astronomy and navigation brings together accounts from widely dispersed places in Western Australia, from Noongar Country in the south-west, through to the Eastern Goldfields, the Pilbara, the Kimberley and the Central Deserts. Information for this review has been taken from the literature and non-conventional sources, including artist statements of paintings. The intention for the review is that the scope is traditional, pre-European settlement understandings, but post-settlement records of oral accounts, and later articulation by Aboriginal peoples, are necessarily relied upon. In large part, the Western Australian accounts reflect understandings reported for other states. For example, star maps were used for teaching routes on the ground, but available accounts do not evidence that star maps were used in real-time navigation. The narratives or dreamings that differ most from those of other states explain creation of night-sky objects and landforms on Earth, events including thunder, or they address social behaviour.

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing

Mapping and Real-Time Navigation With Application to Small UAS Urgent Landing