Navigation Trajectory Research Articles

Offline consolidation of spatial memory: Do the cerebellar output circuits play a role? A study utilizing a Morris water maze protocol in male Wistar rats

To address whether the cerebellum takes part to spatial memory consolidation related to navigation, male Wistar rats were trained daily (4 days), in a Morris water maze to found a submerged escape platform by use of distal cues (place training test). Retention of the allocentric map was evaluated in the probe test (without platform), before the place test. Bilateral shutdown of deep cerebellar nuclei was carried by infusion of the GABA-A agonist muscimol (0.25 µl at 1 µg/µl) immediately after each place test. Histology revealed a dorsal dentate nucleus (DDN) group, with muscimol diffusion confined to dentate nuclei, and a ventromedial/dentate nuclei (VMDN) group, with muscimol additionally involving fastigial, interpositus and vestibular nuclei. In the place test, Vehicle, DDN and VMDN groups reduced latency and distance to the platform over the 4 days and within the single day, indicative of efficient acquisition and working memory; navigational trajectories however differed in that, while Vehicle and DDN groups evolved to use direct paths, VMDN group indulged to navigate in proximity to the platform, suggesting an impairment in refining the spatial map. In the probe test VMDN, unlike Vehicle and DDN animals, failed to develop a preference for the quadrant where the platform was previously located, indicating a consolidation deficit.In conclusion, ventromedial cerebellar related structures may contribute to the process of consolidation of an allocentric spatial memory: their inactivation may have impaired the offline integration of idiothetic information with allothetic signals within the navigational network, leading to a coarse resolution map.

Fuzzy-adaptive constrained data fusion algorithm for indirect centralized integrated SINS/GNSS navigation system

The main challenge of low-cost strap-down inertial navigation systems (SINSs) is time-growing positioning error due to erroneous measurements of micro-electro mechanical system (MEMS)-based inertial sensors. The global navigation satellite system (GNSS) provides drift-free positioning data that can be appropriately utilized to prevent the cumulative error of stand-alone SINS. The primary aim of this research is to enhance the positioning accuracy, performance, and reliability of low-cost SINS/GNSS-integrated navigation system. To attain this, we propose an applied data fusion algorithm for indirect centralized (IC) integrated SINS/GNSS. The proposed data fusion algorithm is based on fuzzy-adaptive constrained estimation filter. Velocity and altitude constraints are embedded in the integration scheme of the proposed SINS/GNSS system to preserve system reliability during abrupt GNSS outage. In an innovative way, the state constraints of altitude are defined based on the measurements of air-data sensors. The state estimation is effectively optimized since the respective states are projected on a constraint surface. Furthermore, a fuzzy type-2 inference system is developed for adaptively changing the covariance matrix of the estimation algorithm. Inertial measurements are used as the input of the fuzzy inference system. Accordingly, the state estimation algorithm is adaptively modified based on the vehicle’s maneuvering during the navigation trajectory. The proposed SINS/GNSS system is experimentally assessed through several vehicular tests. The results indicate that not only does the proposed algorithm improve the navigation accuracy, it will also enhance the reliability of the integrated navigation system during GNSS outage.

A Novel Method of Missing Road Generation in City Blocks Based on Big Mobile Navigation Trajectory Data

With the rapid development of cities, the geographic information of urban blocks is also changing rapidly. However, traditional methods of updating road data cannot keep up with this development because they require a high level of professional expertise for operation and are very time-consuming. In this paper, we develop a novel method for extracting missing roadways by reconstructing the topology of the roads from big mobile navigation trajectory data. The three main steps include filtering of original navigation trajectory data, extracting the road centerline from navigation points, and establishing the topology of existing roads. First, data from pedestrians and drivers on existing roads were deleted from the raw data. Second, the centerlines of city block roads were extracted using the RSC (ring-stepping clustering) method proposed herein. Finally, the topologies of missing roads and the connections between missing and existing roads were built. A complex urban block with an area of 5.76 square kilometers was selected as the case study area. The validity of the proposed method was verified using a dataset consisting of five days of mobile navigation trajectory data. The experimental results showed that the average absolute error of the length of the generated centerlines was 1.84 m. Comparative analysis with other existing road extraction methods showed that the F-score performance of the proposed method was much better than previous methods.

A New Algorithm for Navigation Trajectory Prediction of Land Vehicles Based on a Generalised Extended Extrapolation Model

Dynamic trajectory prediction is an important topic in the field of navigation and positioning. Due to the drawbacks of a Global Navigation Satellite System (GNSS) receiver, the trajectory of the position always lags behind the dynamic platform's actual position, especially in highly dynamic situations. In order to solve the prediction of a dynamic trajectory, a generalised extension extrapolated model is proposed in this paper. The model utilises the current motion state and a priori position data of the platform, combines the interpolation and fitting method, adds the angle information as a constraint condition and solves the platform position prediction. In this paper, the feasibility of the generalised extended extrapolation algorithm is analysed theoretically and practically. Simulation results show that the prediction error is within 0.2 metres and experimental results show that the algorithm still has high prediction accuracy when a land vehicle platform is turned through a large angle.

Prediction of the Influence of Navigation Scan-Path on Perceived Quality of Free-Viewpoint Videos

Free-Viewpoint Video (FVV) systems allow the viewers to freely change the viewpoints of the scene. In such systems, view synthesis and compression are the two main sources of artifacts influencing the perceived quality. To assess this influence, quality evaluation studies are often carried out using conventional displays and generating predefined navigation trajectories mimicking the possible movement of the viewers when exploring the content. Nevertheless, as different trajectories may lead to different conclusions in terms of visual quality when benchmarking the performance of the systems, methods to identify critical trajectories are needed. This paper aims at exploring the impact of exploration trajectories (defined as Hypothetical Rendering Trajectories: HRT) on perceived quality of FVV subjectively and objectively, providing two main contributions. Firstly, a subjective assessment test including different HRTs was carried out and analyzed. The results demonstrate and quantify the influence of HRT in the perceived quality. Secondly, we propose a new objective video quality assessment measure to objectively predict the impact of HRT. This measure, based on Sketch-Token representation, models how the categories of the contours change spatially and temporally from a higher semantic level. Performance in comparison with existing quality metrics for FVV, highlight promising results for automatic detection of most critical HRTs for the benchmark of immersive systems.

Incorporation of acoustic sensors in the regulation of a mobile robot

ABSTRACTThis article introduces the incorporation of acoustic sensors for the localization of a mobile robot. The robot is considered as a sound source and its position is located applying a Time Delay of Arrival (TDOA) method. Since the accuracy of this method varies with the microphone array, a navigation acoustic map that indicates the location errors is built. This map also provides the robot with navigation trajectories point-to-point and the control is capable to drive the robot through these trajectories to a desired configuration. The proposed localization method is thoroughly tested using both a 900 Hz square signal and the natural sound of the robot, which is driven near the desired point with an average error of 0.067 m.

Influence of Initial State Errors on Perturbation Guidance Accuracy


 
 
 The inertial navigation system is aligned and leveled before the launch of a long-range vehicle. However, the initial state errors caused by the non-uniformity of the Earth can influence the parameters in flight dynamics, which will bring about serious uncertainty for the impact point of a long-range vehicle. Firstly, this paper analyses the influence mechanism of initial state errors on nominal trajectory, navigation trajectory and guidance trajectory. Then, a propagation model of engine-cutoff state deviation caused by initial state errors is derived under the condition of without-guidance. On this basis, an accuracy analytical solution of initial state errors on perturbation guidance is finally proposed to obtain the real impact-point of the long-range vehicle. In the simulations, the influence properties of initial state errors on perturbation guidance is analysed, give influence regularities of single initial state error, and obtain the statistical properties of engine-cutoff state deviations and impact-point deviation by Monte Carlo technique. From the simulation results, it seems that the navigation state tracks the nominal state. However, the real impact- point deviation has not been truly eliminated, instead of the almost target-hit deviation calculated by navigation output. The proposed analytical guidance accuracy model can be rapidly computed to provide a compensation for guidance and control system to improve hit accuracy.
 
 

Decision and navigation in mouse parietal cortex.

Posterior parietal cortex (PPC) has been implicated in navigation, in the control of movement, and in visually-guided decisions. To relate these views, we measured activity in PPC while mice performed a virtual navigation task driven by visual decisions. PPC neurons were selective for specific combinations of the animal's spatial position and heading angle. This selectivity closely predicted both the activity of individual PPC neurons, and the arrangement of their collective firing patterns in choice-selective sequences. These sequences reflected PPC encoding of the animal's navigation trajectory. Using decision as a predictor instead of heading yielded worse fits, and using it in addition to heading only slightly improved the fits. Alternative models based on visual or motor variables were inferior. We conclude that when mice use vision to choose their trajectories, a large fraction of parietal cortex activity can be predicted from simple attributes such as spatial position and heading.

Modeling sensory-motor decisions in natural behavior.

Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.

Observability Degree-Based AUV Single Beacon Navigation Trajectory Optimization with Range-Only Measurements

Aiming at the problem of autonomous underwater vehicle navigation trajectory optimization using single beacon location under direct route condition, a nonlinear system model for AUV single beacon navigation is established, and the linearized system model with error states is constructed by polar coordinate transformation and simplification. Then, current disturbance is considered. To find out the optimum path to utilize range-only measurements, a novel observability degree-based analysis method is proposed, which gives a quantitative insight into convergence characteristics of the error states by using the eigenvalues of the normalized error covariance matrix. Simulation experiments are done to test convergence characteristics of AUV integrated navigation error states with single beacon range-only measurements under direct route control conditions. The experimental results show that the proposed control method is effective, and it has an important engineering application value and provides us with an optimized path.

Scientific issues that need to be addressed for a successful read-across prediction

This work aims to explore the robot automatic navigation model under computer intelligent algorithms and machine vision, so that mobile robots can better serve all walks of life. In view of the current situation of high cost and poor work flexibility of intelligent robots, this work innovatively researches and improves the image processing algorithm and control algorithm. In the navigation line edge detection stage, aiming at the low efficiency of the traditional ant colony algorithm, the Canny algorithm is combined to improve it, and a Canny-based ant colony algorithm is proposed to detect the trajectory edge. In addition, the Single Shot MultiBox Detector (SSD) algorithm is adopted to detect obstacles in the navigation trajectory of the robot. The performance is analyzed through simulation. The results show that the navigation accuracy of the Canny-based ant colony algorithm proposed in this work is basically stable at 89.62%, and its running time is the shortest. Further analysis of the proposed SSD neural network through comparison with other neural networks suggests that its feature recognition accuracy reaches 92.90%. The accuracy is at least 3.74% higher versus other neural network algorithms, the running time is stable at about 37.99 s, and the packet loss rate is close to 0. Therefore, the constructed mobile robot automatic navigation model can achieve high recognition accuracy under the premise of ensuring error. Moreover, the data transmission effect is ideal. It can provide experimental basis for the later promotion and adoption of mobile robots in various fields.

A novel ship trajectory reconstruction approach using AIS data

AIS data plays an increasingly important role in collision avoidance, risk evaluation, and navigation behavior study. However, the raw AIS data contains noise that can result in wrong conclusions. We propose a multi-regime vessel trajectory reconstruction model through three-steps processing, including (i) outliers removal, (ii) ship navigational state estimation and (iii) vessel trajectory fitting. This model allows for vessel trajectory reconstruction in different navigation states, namely hoteling, maneuvering, and normal-speed sailing. The normal-speed navigation trajectory is estimated with a spline model, which can fit any types of the trajectory even with circles. Then, the proposed model is tested and compared with other three popular trajectory reconstruction models based on a large AIS dataset containing the movement of more than 500 ships in Singapore Port. The results show that the proposed model performs significantly better than the linear regression model, polynomial regression model, and weighted regression model. The proposed model can decrease the abnormal rate of speed, acceleration, jerk and ROT (Rate of Turn) from 43.42%, 10.65%, 59.25%, 50.33%–0.00%, 0.00%, 17.28% and 15.81%, respectively. More importantly, the navigational behavior, such as turning operation, could be clearly shown in the trajectory reconstructed by the proposed model.

Drone as an autonomous aerial sensor system for motion planning

A system capable of both autonomous navigation and remote fire detection was developed from first principles. Using a complementary filter and attitude fundamentals, an Euler coordinate system representation of the aircraft’s orientation was measured using a widely available low-cost inertial measurement unit incorporating MEMS accelerometers, gyroscopes and magnetometers. Using line-of-sight guidance principles, navigation trajectories could be calculated in real-time providing autonomous navigation between user designated waypoints. A stabilisation control system using proportional-integral-derivative (PID) controllers was developed in order to achieve stabilised flight on the calculated navigation trajectories. Fire detection was facilitated with the use of three low-cost air composition sensors fed into an artificial neural network. A motion planner was developed to assist in flight planning by using topology information for the flight area to construct an optimal flight path based on distance and climb penalties.

Motivated Optimal Developmental Learning for Sequential Tasks Without Using Rigid Time-Discounts

Many methods for reinforcement learning use symbolic representations-nonemergent-such as Q-learning. We use emergent representations here, without human handcrafted symbolic states (i.e., each state corresponds to a different location). This paper models reinforcement learning for hidden neurons in emergent networks for sequential tasks. In this paper, their influences on sequential tasks (e.g., robot navigation in different scenarios) are investigated where the learned value and results of a behavior rely on not only the current experience just like in a pattern recognition (episodic) but also the prediction of future experiences (e.g., delayed rewards) and environments (e.g., previously learned navigational trajectories). We show that this new model of motivated learning amounts to the computation of the maximum-likelihood estimate through "life" where punishment and reward have increased weights. This new formulation avoids the greediness of time-discount in Q-learning. Its complex nonlinear sequential optimization has been solved in a closed-form procedure under the condition of the limited computational resources and limited learning experience so far, because we convert it into a simpler problem of incremental and linear estimation. The experimental results showed that the serotonin and dopamine systems speed up learning for sequential tasks, because not all events are equally important. As far as we know, this is the first work that studies the influences of reinforcers (via serotonin and dopamine) on hidden neurons (Y neurons) for sequential tasks in dynamic scenarios using emergent representations.

Robust Fuzzy Path Tracking Control of a Platooning Vehicles

The paper present our control approach based in fuzzy controller for platooning vehicles. this approach based to control lateral and longitudinal movement of vehicles in different navigation trajectory. kinematic model of vehicles are described follows by describe of controller design. The communication is provided between vehicles with exchange information, speed and orientation angle with a fixed safety distance between vehicles. 3D simulation developed with matlab, Simulink and v-rep software were carried . Different reference trajectory are used to compared and approve our approach. The simulation results illustrate the efficiency of our control design and open the perspectives for future work.

RETRACTED ARTICLE: Neuromodulation of internal emergent representations for sequential tasks

Serotonin and dopamine transmitters are synthesized in the lower brain but are transmitted widely to many areas of the brain for diffused use. Emergent representations are critical for understanding their effects. In prior work Zheng et al. (in: Proceedings of 2013 international joint conference on neural networks (IJCNN2013), pp 1404–1411, Dallas, Texas, USA, August 4–9, 2013), their effects on internal, non-motor neurons were studied for only pattern recognition tasks. In this paper, we study their effects on sequential tasks—robot navigation under different settings. They are sequential tasks because the outcome of behavior depends on not only the current behavior as in pattern recognition but also the previous behaviors and environment (e.g., previous navigational trajectories). Analytically, we show that the serotonin and dopamine systems affect the performance of sequential tasks in a compound way. Experimentally, we show that the effect on the learning rate of internal feature neurons (in the Y area) allows the agent to approach a friend and avoid an enemy faster as compounding effects of sequential states in static and dynamic environment. Further, we test the effect of punishment and reward schedule with the same initial locations. These simulation experiments all indicate that the reinforcement learning via the serotonin and dopamine systems is beneficial for developing desirable behaviors in this set of sequential tasks—staying statistically close to its friend and away from its enemy. As far as we know, this is the first work that investigates the effects of reinforcer (via serotonin and dopamine) on internal neurons (Y neurons) for sequential tasks using emergent representations.

Cislunar Navigation Constellation by Displaced Solar Sails

Increasing lunar exploration activities are giving rise to higher demands for a navigation constellation system in cislunar space. A novel constellation of solar sails around the Sun-Earth Artificial Lagrangian Points (ALPs) is proposed for cislunar navigation in this paper, which benefits from the numberless and out-of-plane advantages of ALPs compared with the classical Lagrangian points. To relieve the technical pressure on sail equipment, a two-layer optimisation strategy including the navigation constellation architecture and trajectory design is developed to reduce the desired lightness number of the sail's motion. The constellation architecture is constructed in the shape of a regular tetrahedron, whose size and orientation are derived from the realisable lightness number at the ALPs. The powerful Hamiltonian structure-preserving controller and differential evolution algorithm are adopted to propagate the bounded quasi-periodic trajectory with minimum lightness number variation. With the premise of the sail's high feasibility in the mechanism, the numerical navigation simulations for a typical trans-lunar weak stability boundary trajectory indicate that the proposed navigation constellation has a low geometric dilution of precision factor and a good navigation performance.

Augmented-reality integrated robotics in neurosurgery: are we there yet?

Surgical robots have captured the interest-if not the widespread acceptance-of spinal neurosurgeons. But successful innovation, scientific or commercial, requires the majority to adopt a new practice. "Faster, better, cheaper" products should in theory conquer the market, but often fail. The psychology of change is complex, and the "follow the leader" mentality, common in the field today, lends little trust to the process of disseminating new technology. Beyond product quality, timing has proven to be a key factor in the inception, design, and execution of new technologies. Although the first robotic surgery was performed in 1985, scant progress was seen until the era of minimally invasive surgery. This movement increased neurosurgeons' dependence on navigation and fluoroscopy, intensifying the drive for enhanced precision. Outside the field of medicine, various technology companies have made great progress in popularizing co-robots ("cobots"), augmented reality, and processor chips. This has helped to ease practicing surgeons into familiarity with and acceptance of these technologies. The adoption among neurosurgeons in training is a "follow the leader" phenomenon, wherein new surgeons tend to adopt the technology used during residency. In neurosurgery today, robots are limited to computers functioning between the surgeon and patient. Their functions are confined to establishing a trajectory for navigation, with task execution solely in the surgeon's hands. In this review, the authors discuss significant untapped technologies waiting to be used for more meaningful applications. They explore the history and current manifestations of various modern technologies, and project what innovations may lie ahead.

STRUCTURAL SYNTHESIS OF NAVIGATION SUPPORT OF TRIAD INTEGRATED NAVIGATION SYSTEM ON THE BASIS OF INERTIAL AND SATELLITE TECHNOLOGIES

In connection with the increasing complexity of aircraft navigation support, with growing demands placed on them, it is increasingly necessary to develop integrated navigation data processing system by solving synthesis problems based on optimal filtering methods. One of the promising ways to improve aircraft navigation support is to create an integrated navigation system (INS). In this work the triadic structure of a promising integrated navigation system based on inertial and satellite technologies is proposed. Combining nautical meters as part of the triad concept allows the flexible use of various navigation data processing algorithms and trajectory optimization ones at different stages of flight. Based on optimal filtering theory methods the integrated navigation data processing algorithm, which is an extended Kalman filter, is synthesized. A distinctive feature of the algorithm is that the variables of the state vector which are to be measured, are the errors in the determination of appropriate navigation and timing parameters. Priori information about the dynamics of errors is more reliably known than the one about aircraft motion variables. It is shown that the use of the proposed navigation system as a navigation aid for problem solving of highly accurate aircraft position indication satisfies the requirements for navigation and timing data accuracy. The imitating statistical modeling of optimal filtering algorithm of the triad integrated system is carried out. The accuracy characteristics of subsystems for different operating configurations of the constellation are given. Analysis of the results of the accuracy characteristics study of INS in various operating conditions has showed that the positioning accuracy in the navigation computer is up to date satellite navigation receivers.

PDOP 가중치 기반 정밀 탄궤적 정합 알고리즘

최근 다양한 형태와 기능을 갖춘 스마트 무기들이 개발되고 있다. 화포탄의 경우 스마트 탄을 개발하기 위한 초기 연구로 신관 내부에 GNSS 수신기를 장착하여 탄자의 비행위치를 정밀하게 측정하고 이를 바탕으로 탄착점을 추정하는 연구가 진행되고 있다. 하지만, 수신기 성능 및 수신된 데이터에 포함된 다양한 오차유발 원인들로 인해 항법데이터의 위치정확도에 오차가 발생하게 된다. 본 논문에서는 PRODAS로부터 얻은 모의궤적 데이터를 수신기로부터 얻은 항법데이터에 포함된 PDOP 가중치를 적용하여 정합함으로써 탄의 발사부터 탄착까지의 전체 비행궤적 및 탄착점을 보다 정밀하게 추정하는 개선된 알고리즘을 소개한다. Recently, many kind of smart projectiles are being developed. In case of smart projectile, studying in advance, it uses a navigation data acquired from the GNSS receiver to check its location on the geocentric(WGS84) coordinates and to estimate P.O.I(point of impact). However, because of various error inducing factors, the result of positioning involve some errors. We introduce the advanced algorithm for the reconstruction of a navigation trajectory using weighted PDOP, based on a simulated trajectory acquired from PRODAS. It is very fast and robust to noise and shows reliable output. It can be widely used to estimate an actual trajectory of a projectile.