A layered fuzzy logic controller for nonholonomic car-like robot

A system for real time navigation of a nonholonomic car-like robot in a dynamic environment consists of two layers is described: a Sugeno-type fuzzy motion planner; and a modified proportional navigation based fuzzy controller. The system philosophy is inspired by human routing when moving between obstacles based on visual information including right and left views to identify the next step to the goal. A Sugeno-type fuzzy motion planner of four inputs one output is introduced to give a clear direction to the robot controller. The second stage is a modified proportional navigation based fuzzy controller based on the proportional navigation guidance law and able to optimize the robot's behavior in real time, i.e. to avoid stationary and moving obstacles in its local environment obeying kinematics constraints. The system has an intelligent combination of two behaviors to cope with obstacle avoidance as well as approaching a target using a proportional navigation path. The system was simulated and tested on different environments with various obstacle distributions. The simulation reveals that the system gives good results for various simple environments

Real time traffic simulator for self-adaptive navigation system validation

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Improving Foot-Mounted Inertial Navigation Through Real-Time Motion Classification

We present a method to improve the accuracy of a foot-mounted, zero-velocity-aided inertial navigation system (INS) by varying estimator parameters based on a real-time classification of motion type. We train a support vector machine (SVM) classifier using inertial data recorded by a single foot-mounted sensor to differentiate between six motion types (walking, jogging, running, sprinting, crouch-walking, and ladder-climbing) and report mean test classification accuracy of over 90% on a dataset with five different subjects. From these motion types, we select two of the most common (walking and running), and describe a method to compute optimal zero-velocity detection parameters tailored to both a specific user and motion type by maximizing the detector F-score. By combining the motion classifier with a set of optimal detection parameters, we show how we can reduce INS position error during mixed walking and running motion. We evaluate our adaptive system on a total of 5.9 km of indoor pedestrian navigation performed by five different subjects moving along a 130 m path with surveyed ground truth markers.Comment: In Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN'17), Sapporo, Japan, Sep. 18-21, 201

Cloud Enabled Emergency Navigation Using Faster-than-real-time Simulation

State-of-the-art emergency navigation approaches are designed to evacuate civilians during a disaster based on real-time decisions using a pre-defined algorithm and live sensory data. Hence, casualties caused by the poor decisions and guidance are only apparent at the end of the evacuation process and cannot then be remedied. Previous research shows that the performance of routing algorithms for evacuation purposes are sensitive to the initial distribution of evacuees, the occupancy levels, the type of disaster and its as well its locations. Thus an algorithm that performs well in one scenario may achieve bad results in another scenario. This problem is especially serious in heuristic-based routing algorithms for evacuees where results are affected by the choice of certain parameters. Therefore, this paper proposes a simulation-based evacuee routing algorithm that optimises evacuation by making use of the high computational power of cloud servers. Rather than guiding evacuees with a predetermined routing algorithm, a robust Cognitive Packet Network based algorithm is first evaluated via a cloud-based simulator in a faster-than-real-time manner, and any "simulated casualties" are then re-routed using a variant of Dijkstra's algorithm to obtain new safe paths for them to exits. This approach can be iterated as long as corrective action is still possible.Comment: Submitted to PerNEM'15 for revie

Phoenix-XNS - A Miniature Real-Time Navigation System for LEO Satellites

The paper describes the development of a miniature GPS receiver with integrated real-time navigation system for orbit determination of satellites in low Earth orbit (LEO). The Phoenix-XNS receiver is based on a commercial-off-the-shelf (COTS) single-frequency GPS receiver board that has been qualified for use in a moderate space environment. Its firmware is specifically designed for space applications and accounts for the high signal dynamics in the acquisition and tracking process. The supplementary eXtended Navigation System (XNS) employs an elaborate force model and a 24-state Kalman filter to provide a smooth and continuous reduced-dynamics navigation solution even in case of restricted GPS availability. Through the use of the GRAPHIC code-carrier combination, ionospheric path delays can be fully eliminated in the filter, which overcomes the main limitation of conventional single-frequency receivers. Tests conducted in a signal simulator test bed have demonstrated a filtered navigation solution accuracy of better than 1 m (3D rms)

The real-time acquisition and tracking program for the USNS Vanguard

The computer program for the real-time acquisition and tracking program uses a variety of filtering algorithms including an extended Kalman filter to derive real-time orbit determination (position-velocity state vectors) from shipboard tracking and navigation data. Results from Apollo missions are given to show that orbital parameters can be estimated quickly and accurately using these methods

Vehicle Navigation Service Based on Real-Time Traffic Information

GNSS-assisted vehicle navigation services are nowadays very common in most of the developed countries. However, most of those services are either delivered through proprietary technologies, or fall short in flexibility because of the limited capability to couple road information with real-time traffic information. This paper presents the motivations and a brief summary of a vehicle navigation service based on real-time traffic information, delivered through an open protocol that is currently under standardization in the Open Mobile Alliance forum