Passive navigation using image irradiance tracking

Rotorcraft operating at low altitudes require navigational schemes for detecting terrain and obstacles. Due to the nature of the missions to be accomplished and available power onboard, a passive navigation scheme is desirable in this situation. The development of a passive navigation scheme using optical image sequences and vehicle motion variables from an onboard inertial navigation scheme is described. This approach combines the geometric properties of perspective projection and a feedback irradiance tracking scheme at each pixel in the image to determine the range to various objects within the field-of-view. Derivation of the numerical algorithm and simulation results are given. Due to the feedback nature of the implementation, the computational scheme is robust. Other applications of the proposed approach include navigation for autonomous planetary rovers and telerobots

Construction and working principle of the passive gravity-gradient stabilization for small spacecraft

Рассматриваются пассивные системы ориентации, требующие точного предварительного анализа динамики. В качестве самой оптимальной пассивной системой ориентации взята гравитационная система ориентации. Описан принцип действия такой системы ориентации и предварительная компоновка спутника с этой системой.The passive navigation systems considered demanding the exact preliminary analysis of dynamics. As the most optimum passive navigation system the gravitational navigation system is taken. The principle of orientation of this navigation system and preliminary configuration of the satellite with this system is described

Construction and working principle of the passive gravity-gradient stabilization for small spacecraft

Рассматриваются пассивные системы ориентации, требующие точного предварительного анализа динамики. В качестве самой оптимальной пассивной системой ориентации взята гравитационная система ориентации. Описан принцип действия такой системы ориентации и предварительная компоновка спутника с этой системой.The passive navigation systems considered demanding the exact preliminary analysis of dynamics. As the most optimum passive navigation system the gravitational navigation system is taken. The principle of orientation of this navigation system and preliminary configuration of the satellite with this system is described

Robotic air vehicle. Blending artificial intelligence with conventional software

The Robotic Air Vehicle (RAV) system is described. The program's objectives were to design, implement, and demonstrate cooperating expert systems for piloting robotic air vehicles. The development of this system merges conventional programming used in passive navigation with Artificial Intelligence techniques such as voice recognition, spatial reasoning, and expert systems. The individual components of the RAV system are discussed as well as their interactions with each other and how they operate as a system

Artificial Intelligence-Assisted Inertial Geomagnetic Passive Navigation

In recent years, the integration of machine learning techniques into navigation systems has garnered significant interest due to their potential to improve estimation accuracy and system robustness. This doctoral dissertation investigates the use of Deep Learning combined with a Rao-Blackwellized Particle Filter for enhancing geomagnetic navigation in airborne simulated missions. A simulation framework is developed to facilitate the evaluation of the proposed navigation system. This framework includes a detailed aircraft model, a mathematical representation of the Earth\u27s magnetic field, and the incorporation of real-world magnetic field data obtained from online databases. The setup allows an accurate assessment of the performance and effectiveness of the proposed Geomagentic architecture in diverse and realistic geomagnetic scenarios. The results of this research demonstrate the potential of Machine Learning algorithms in improving the performance of the sensor fusion filter for geomagnetic navigation, and introduces a novel approach for resolution enhancing of available geomagnetic models, which provides a better description of the magnetic features within these models. The integration leads to more accurate and robust inertial guidance in airborne missions, thus paving the way for advanced, reliable navigation systems for a variety of aerial vehicles. Overall, this dissertation contributes to the state-of-the-art in geomagnetic navigation research by offering a novel approach to integrating machine learning techniques with traditional estimation methods, with a novel technique to obtain more accurate geomagnetic models required within these navigation architectures. The findings of this work hold promise for the development of advanced, adaptive navigation systems for both civilian and military aviation applications

Investigating the different domains of environmental knowledge acquired from virtual navigation and their relationship to cognitive factors and wayfinding inclinations

When learning an environment from virtual navigation people gain knowledge about landmarks, their locations, and the paths that connect them. The present study newly aimed to investigate all these domains of knowledge and how cognitive factors such as visuospatial abilities and wayfinding inclinations might support virtual passive navigation. A total of 270 participants (145 women) were tested online. They: (i) completed visuospatial tasks and answered questionnaires on their wayfinding inclinations; and (ii) learnt a virtual path. The environmental knowledge they gained was assessed on their free recall of landmarks, their egocentric and allocentric pointing accuracy (location knowledge), and their performance in route direction and landmark location tasks (path knowledge). Visuospatial abilities and wayfinding inclinations emerged as two separate factors, and environmental knowledge as a single factor. The SEM model showed that both visuospatial abilities and wayfinding inclinations support the environmental knowledge factor, with similar pattern of relationships in men and women. Overall, factors related to the individual are relevant to the environmental knowledge gained from an online virtual passive navigation

Virtual reality in neurologic rehabilitation of spatial disorientation

BACKGROUND: Topographical disorientation (TD) is a severe and persistent impairment of spatial orientation and navigation in familiar as well as new environments and a common consequence of brain damage. Virtual reality (VR) provides a new tool for the assessment and rehabilitation of TD. In VR training programs different degrees of active motor control over navigation may be implemented (i.e. more passive spatial navigation vs. more active). Increasing demands of active motor control may overload those visuo-spatial resources necessary for learning spatial orientation and navigation. In the present study we used a VR-based verbally-guided passive navigation training program to improve general spatial abilities in neurologic patients with spatial disorientation. METHODS: Eleven neurologic patients with focal brain lesions, which showed deficits in spatial orientation, as well as 11 neurologic healthy controls performed a route finding training in a virtual environment. Participants learned and recalled different routes for navigation in a virtual city over five training sessions. Before and after VR training, general spatial abilities were assessed with standardized neuropsychological tests. RESULTS: Route finding ability in the VR task increased over the five training sessions. Moreover, both groups improved different aspects of spatial abilities after VR training in comparison to the spatial performance before VR training. CONCLUSIONS: Verbally-guided passive navigation training in VR enhances general spatial cognition in neurologic patients with spatial disorientation as well as in healthy controls and can therefore be useful in the rehabilitation of spatial deficits associated with TD

UWB passive navigation in indoor environments

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