Improve Navigation Performance Research Articles

New algorithm for behaviour-based mobile robot navigation in cluttered environment using neural network architecture

Purpose This study concerns an on-line path planning technique for a behaviour-based wheeled mobile robot local navigation in an unknown environment with hurdles, using the feedforward back-propagation neural network sensor-actuator control technique. The purpose of this study is to find the non-collision path for the mobile robot moving towards the goal in a cluttered environment. Design/methodology/approach Neural network architecture input layers are the different hurdle distance information, which are acquired by an array of equipped sensors, and the output layer is the turning angle (motor control). In this way, the mobile robot is effectively being trained to move autonomously in the environment. Findings Computer simulation and real-time experimental results show that the proposed neural network controller can improve navigation performance in cluttered and unknown environments. Originality/value The proposed neural network controller gives better results (in terms of path length) as compared to previously developed models, which verifies the effectiveness of the proposed architecture.

New method for geometric calibration and distortion correction of conventional C-arm

Image distortion correction and geometric calibration are critical operations for using C-arm DSA (Digital Subtraction Angiography) images to digitally navigate vascular interventional surgery. In traditional ways, C-arm images are corrected with global or local correction methods where a supposed virtual ideal image is needed, and then the corrected images are utilized to calibrate the C-arm with a pin-hole model. In this paper, we propose a new method to calibrate the C-arm with a nonlinear model and to improve navigation performance. We first calibrate the C-arm with a nonlinear model and then the distortion correction is accomplished without virtual ideal image. In this paper, the nonlinear model of C-arm imaging system is addressed at first, and then the C-arm is calibrated with a two-stage method. In the first stage, the C-arm is calibrated with the markers in image center by RAC (radial alignment constraint) method, and in the second stage the calibration parameters are optimized with Levenberg-Marquadt algorithm by minimizing the sum of the square of difference between all markers׳ real distorted positions and their theoretical distorted positions in the phantom image. Based on the calibration result, the image distortion can be corrected. To verify our method, experiments were conducted with a conventional DSA C-arm machine in hospital. The errors in distortion correction and 3D (three-dimensional) reconstruction were quantitatively compared with the global polynomial correction method and visual model method, and the results showed that the proposed method had better performance in distortion correction and 3D reconstruction.

Reinforced Ultra-Tightly Coupled GPS/INS System for Challenging Environment

Among all integration levels currently available for Global Positioning System (GPS) and Inertial Navigation System (INS) Integrated System, ultra-tightly coupled (UTC) GPS/INS system is the best choice for accurate and reliable navigation. Nevertheless the performance of UTC GPS/INS system degrades in challenging environments, such as jamming, changing noise of GPS signals, and high dynamic maneuvers. When low-end Inertial Measurement Units (IMUs) based on MEMS sensors are employed, the performance degradation will be more severe. To solve this problem, a reinforced UTC GPS/INS system is proposed. Two techniques are adopted to deal with jamming and high dynamics. Firstly, adaptive integration Kalman filter (IKF) based on fuzzy logics is developed to reinforce the antijamming ability. The parameters of membership functions (MFs) are adjusted and optimized through self-developed neutral network. Secondly, a Doppler frequency error estimator based on Kalman filter is designed to improve the navigation performance under high dynamics. A complete simulation platform is established to evaluate the reinforced system. Results demonstrate that the proposed system architecture significantly improves navigation performance in challenging environments and it is a more advanced solution to accurate and reliable navigation than traditional UTC GPS/INS system.

An Innovative Architecture of UTC GPS/INS System with Improved Performance under Severe Jamming

Ultratightly coupled (UTC) architecture is believed to be the best architecture for Global Positioning System (GPS) and Inertial Navigation System (INS) integration system due to the advanced data fusion strategy and effective mutual assistance between the subsystems. However the performance of UTC GPS/INS system will be degraded by severe jamming interference, especially when low-grade inertial measurement unit (IMU) is used. To solve this problem an innovative architecture of UTC GPS/INS system is proposed. Since GPS receiver’s antijamming ability is closely related to tracking loop bandwidth, adaptive tracking loop bandwidth based on the fuzzy logics is proposed to enhance antijamming ability for GPS receiver. The bandwidth will be adapted through a fuzzy logic controller according to the calculated carrier to noise intensity ratio(C/N0). Moreover, fuzzy adaptive integration Kalman filter (IKF) is developed to improve estimation accuracy of IKF when measurement noises change. A simulation platform is established to evaluate the innovative architecture and results demonstrate that the proposed scheme improves navigation performance significantly under severe jamming conditions.

랜드마크가 충분하지 않은 환경에서의 관성/비전 통합항법시스템

An INS/vision integrated navigation algorithm is proposed for environments with insufficient number of landmarks. In the proposed algorithm, the raw measurements on the focal plane are directly used in order to cope with the situation where the number of landmarks are not sufficient. In addition to this, the combination of landmarks, which has smallest value of DOP, is used in the update of measurement in order to improve navigation performance. In order to evaluate the performance of the proposed integrated navigation system, Monte-Carlo simulation and van test was performed. The results of the simulation and experiment show that the proposed navigation system gives better navigation performance than an INS/vision integrated navigation system which does not use the raw measurements on the focal plane and the navigation system provides navigation solutions even in environments with insufficient number of landmarks.

Reciprocal Collision Avoidance With Motion Continuity Constraints

This paper addresses decentralized motion planning among a homogeneous set of feedback-controlled, decision-making agents. It introduces the continuous control obstacle ( C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -CO), which describes the set of C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -continuous control sequences (and thus trajectories) that lead to a collision between interacting agents. By selecting a feasible trajectory from C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -CO's complement, a collision-free motion is obtained. The approach represents an extension to the reciprocal velocity obstacle (RVO, ORCA) collision-avoidance methods so that trajectory segments verify C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> continuity rather than piecewise linearity. This allows the large class of robots capable of tracking C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -continuous trajectories to employ it for partial motion planning directly-rather than as a mere tool for collision checking. This paper further establishes that both the original velocity obstacle method and several of its recently developed reciprocal extensions (which treat specific robot physiologies only) correspond to particular instances of C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -CO. In addition to the described extension in trajectory continuity, C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -CO thus represents a unification of existing RVO theory. Finally, the presented method is validated in simulation-and a parameter study reveals under which environmental and control conditions C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">n</sup> -CO with admits significantly improved navigation performance compared with inflated approaches based on ORCA.

An adaptive navigation method for a small unmanned aerial rotorcraft under complex environment

Focusing on low navigation performance of small unmanned aerial rotorcraft under complex environment, a composite navigation method combined with adaptive Kalman filtering and radial basis function neural network prediction method is proposed to improve navigation performance during GPS outages. When the GPS signal is available, an adaptive Kalman filter based on covariance scaling is introduced to deal with the process noise in real time. Meanwhile, a radial basis function neural network is trained on line to construct the projection among input (output of the inertial measurement unit, attitude and GPS losing time) and output (position error and velocity error). During GPS outages, the radial basis function neural network can provide high performance error estimation for position and velocity to improve state information. Finally, a land vehicle test and a flight test have confirmed that the proposed method can improve the navigation performance largely under complex environment.

Ultratight GPS/Reduced-IMU Integration for Land Vehicle Navigation

A reduced inertial measurement unit (RIMU) is ultratightly (UT) integrated with a vector-based Global Positioning System (GPS) receiver using two approaches; vector delay and frequency lock loops (VDLL/VFLL), and a VDLL with a cascaded phase lock loop (CaP). A CaP plus a frequency discriminator (CaPF) composite loop is developed to provide reliable Doppler measurements and improves navigation performance by up to 20% compared with other methods using real world data.

Research on Rotation Modulation for INS Based on MEMS Sensors

nertial sensors based on Micro-electro-mechanical systems (MEMS) are leading a great prospect because they are cheap, small and light. However, large errors limit their applications in many Inertial Navigation Systems (INS). To improve accuracy of INS based on MEMS sensors, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced. RM improves navigation performance without involving any other information sources, saving the essential characteristic of self-contain. The RM effects on sensor biases and navigation errors are analyzed. Different RM schemes including elements like number of rotating axis, rotation direction, continuity and speed are discussed. An INS based on MEMS sensors with an appropriate RM scheme is developed. Static and land vehicle tests are conducted, verifying the effectiveness of RM on improving inertial navigation performance. Through introducing RM, attitude accuracy is improved by 5 times, and velocity/position accuracy by nearly 10 times.

Analysis and Verification of Rotation Modulation Effects on Inertial Navigation System based on MEMS Sensors

Inertial Navigation Systems (INS) were large, heavy and expensive until the development of cost-effective inertial sensors constructed with Micro-electro-mechanical systems (MEMS). However, the large errors and poor error repeatability of MEMS sensors make them inadequate for application in many situations even with frequent calibration. To solve this problem, a systematic error auto-compensation method, Rotation Modulation (RM) is introduced and detailed. RM does no damage to autonomy, which is one of the most important characteristics of an INS. In this paper, the RM effects on navigation performance are analysed and different forms of rotation schemes are discussed. A MEMS-based INS with the RM technique applied is developed and specific calibrations related to rotation are investigated. Experiments on the developed system are conducted and results verify that RM can significantly improve navigation performance of MEMS-based INS. The attitude accuracy is improved by a factor of 5, and velocity/position accuracy by a factor of 10.

Exploiting wheel slips of mobile robots to improve navigation performance

Improving navigation performance of autonomous wheeled mobile robot (WMR) in a dynamic unstructured environment requires improved maneuverability. In such cases, the dynamics of wheel slip may violate the ideal no-slip kinematic constraints generally used to model nonholonomic WMR. In this paper, a new method is proposed to tackle the modeling inadequacy that arises when slip is neglected by including both longitudinal and lateral slip dynamics into the overall dynamics of the WMR. This new model of the WMR provides a realistic simulation environment that can be utilized to develop model-based controllers to improve WMR navigation. In this paper, a dynamic planner with a path-following controller is designed to allow the WMR to navigate efficiently by autonomously regulating the generated traction force due to wheel slip. Extensive simulation results demonstrate the importance of the proposed modeling technique to capture slip phenomenon and the efficacy of the presented control technique to exploit such slip for better navigation performance.

GNSS integration with vision-based navigation for low GNSS visibility conditions

In urban canyons, buildings and other structures often block the line of sight of visible Global Navigation Satellite System (GNSS) satellites, which makes it difficult to obtain four or more satellites to provide a three-dimensional navigation solution. Previous studies on this operational environment have been conducted based on the assumption that GNSS is not available. However, a limited number of satellites can be used with other sensor measurements, although the number is insufficient to derive a navigation solution. The limited number of GNSS measurements can be integrated with vision-based navigation to correct navigation errors. We propose an integrated navigation system that improves the performance of vision-based navigation by integrating the limited GNSS measurements. An integrated model was designed to apply the GNSS range and range rate to vision-based navigation. The possibility of improved navigation performance was evaluated during an observability analysis based on available satellites. According to the observability analysis, each additional satellite decreased the number of unobservable states by one, while vision-based navigation always has three unobservable states. A computer simulation was conducted to verify the improvement in the navigation performance by analyzing the estimated position, which depended on the number of available satellites; additionally, an experimental test was conducted. The results showed that limited GNSS measurements can improve the positioning performance. Thus, our proposed method is expected to improve the positioning performance in urban canyons.

지형 험준도를 고려한 프로파일 기반 지형참조항법과 관성항법의 결합 알고리즘

In recent years alternative navigation system such as a DBRN (Data-Base Referenced Navigation) system using geophysical information is getting attention in the military navigation systems in advanced countries. Specifically TRN (Terrain Referenced Navigation) algorithm research is important because TRN system is a practical DBRN application in South Korea at present time. This paper presents an integrated navigation algorithm that combines a linear profile-based TRN and INS (Inertial Navigation System). We propose a correlation analysis method between TRN performance and terrain roughness index. Then we propose a conditional position update scheme that utilizes the position output of the conventional linear profile type TRN depending on the terrain roughness index. Performance of the proposed algorithm is verified through Monte Carlo computer simulations using the actual terrain database. The results show that the TRN/INS integrated algorithm, even when the initial INS error is present, overcomes the shortcomings of linear profile-based TRN and improves navigation performance.

Spearcons Improve Navigation Performance and Perceived Speediness in Korean Auditory Menus

For decades, auditory menus using both speech (usually text-to-speech, TTS) and non-speech sounds have been extensively studied. Researchers have developed situation-optimized auditory menus involving such cues as auditory icons, earcons, spearcons, and spindex. Spearcons have generally outperformed other cues in terms of providing both contextual information and item-specific information. However, little research has been devoted to exploration of spearcons in languages other than English, or the use of spearcon-only auditory menus. In this study, we evaluated the use of spearcons in Korean menus, as well as the use of spearcons alone. Twenty-five native Korean speakers navigated through a two-dimensional auditory menu presented via TTS, with or without spearcon enhancements. Korean spearcons were successful. Participants also rated the spearcon-enhanced menu as seeming speedier and more fun than the TTS-only menu. After a short learning period, mean time-to-target in the auditory menu was even faster with spearcons alone, compared to traditional TTS-only menus.

A new source difference artificial neural network for enhanced positioning accuracy

Integrated inertial navigation system (INS) and global positioning system (GPS) units provide reliable navigation solution compared to standalone INS or GPS. Traditional Kalman filter-based INS/GPS integration schemes have several inadequacies related to sensor error model and immunity to noise. Alternatively, multi-layer perceptron (MLP) neural networks with three layers have been implemented to improve the position accuracy of the integrated system. However, MLP neural networks show poor accuracy for low-cost INS because of the large inherent sensor errors. For the first time the paper demonstrates the use of knowledge-based source difference artificial neural network (SDANN) to improve navigation performance of low-cost sensor, with or without external aiding sources. Unlike the conventional MLP or artificial neural networks (ANN), the structure of SDANN consists of two MLP neural networks called the coarse model and the difference model. The coarse model learns the input–output data relationship whereas the difference model adds knowledge to the system and fine-tunes the coarse model output by learning the associated training or estimation error. Our proposed SDANN model illustrated a significant improvement in navigation accuracy of up to 81% over conventional MLP. The results demonstrate that the proposed SDANN method is effective for GPS/INS integration schemes using low-cost inertial sensors, with and without GPS.

Spearcons (Speech-Based Earcons) Improve Navigation Performance in Advanced Auditory Menus

The goal of this project is to evaluate a new auditory cue, which the authors call spearcons, in comparison to other auditory cues with the aim of improving auditory menu navigation. With the shrinking displays of mobile devices and increasing technology use by visually impaired users, it becomes important to improve usability of non-graphical user interface (GUI) interfaces such as auditory menus. Using nonspeech sounds called auditory icons (i.e., representative real sounds of objects or events) or earcons (i.e., brief musical melody patterns) has been proposed to enhance menu navigation. To compensate for the weaknesses of traditional nonspeech auditory cues, the authors developed spearcons by speeding up a spoken phrase, even to the point where it is no longer recognized as speech. The authors conducted five empirical experiments. In Experiments 1 and 2, they measured menu navigation efficiency and accuracy among cues. In Experiments 3 and 4, they evaluated learning rate of cues and speech itself. In Experiment 5, they assessed spearcon enhancements compared to plain TTS (text to speech: speak out written menu items) in a two-dimensional auditory menu. Spearcons outperformed traditional and newer hybrid auditory cues in navigation efficiency, accuracy, and learning rate. Moreover, spearcons showed comparable learnability as normal speech and led to better performance than speech-only auditory cues in two-dimensional menu navigation. These results show that spearcons can be more effective than previous auditory cues in menu-based interfaces. Spearcons have broadened the taxonomy of nonspeech auditory cues. Users can benefit from the application of spearcons in real devices.

Distinct contributions of human hippocampal theta to spatial cognition and anxiety.

Current views of the hippocampus assign this structure, and its prominent theta rhythms, a key role in both cognition and affect. We studied this duality of function in humans, where no direct evidence exists. Whole-head magnetoencephalographic (MEG) data were recorded to measure theta activity while healthy participants (N = 25) navigated two virtual Morris water mazes, one in which they risked receiving aversive shocks without warning to induce anxiety and one in which they were safe from shocks. Results showed that threat of shock elevated anxiety level and enhanced navigation performance as compared to the safe condition. MEG source analyses revealed that improved navigation performance during threat was preferentially associated with increased left septal (posterior) hippocampal theta (specifically 4-8 Hz activity), replicating previous research that emphasizes a predominant role of the septal third of the hippocampus in spatial cognition. Moreover, increased self-reported anxiety during threat was preferentially associated with increased left temporal (anterior) hippocampal theta (specifically 2-6 Hz activity), consistent with this region's involvement in mediating conditioned and innate fear. Supporting contemporary theory, these findings highlight simultaneous involvement of the human hippocampus in spatial cognition and anxiety, and clarify their distinct correlates.

Multi-Focal Vision and Gaze Control Improve Navigation Performance

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

Multi-focal Vision and Gaze Control Improve Navigation Performance

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

Head for the hills: The influence of environmental slant on spatial memory organization

Environmental slant is known to improve navigation performance in humans and other animals. Successful navigation relies on accurate spatial orientation and accurate spatial memory retrieval. The role of environmental slant in spatial orientation has been established, but its role in spatial memory organization is unclear. Two experiments using immersive virtual reality explored the influence of environmental slant on reference frame selection during spatial learning. Participants studied object locations on a sloped surface. When no additional environmental cues were present (Experiment 1), spatial memory retrieval was best from the studied perspective aligned with the direction of slope. When the direction of slope was placed in competition with the axis of the surrounding room (Experiment 2), spatial memory retrieval was best from the initially studied perspective. The latter finding contrasts with the results of research showing that pigeons preferentially rely on environmental slant over room shape. The findings are discussed in the context of spatial memory theory.