A Fault Tolerant Multi-Sensor Navigation System for an Unmanned Surface Vehicle

Xu, Tao; Chudley, J.; Sutton, Robert I.

doi:10.1016/b978-008044485-7/50252-9

Cited by 8 publications

(13 citation statements)

References 3 publications

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“…The standard quadratic cost function was minimised with the state weight matrix, Q, equal to C T C, and a control cost R equal to 0.0049, which was the value deemed to give the best results in terms of performance when applied to the way point following mission. The model used by the LQG is that given by (3) to (5). A typical simulation of the way point mission execution using the LQG system is shown in Figure 9.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control

Motwani

Sutton

Yang

et al. 2013

IET Conference on Control and Automation 2013: Uniting Problems and Solutions

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An integrated navigation, guidance and control system is designed for an uninhabited surface vehicle named Springer built at Plymouth University. Interval Kalman filtering is used for navigation. Line of sight is used for guidance. Model predictive control is utilised to build the control system, or autopilot. The performance of this innovative navigation, guidance and control system is benchmarked against a conventional optimal method based on a linear quadratic Gaussian controller which uses traditional Kalman filtering. The performance of the systems are compared and analysed in this paper.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In this simulation, a dynamical model of one of Springer's compasses, a TCM2 digital magnetic compass, is made use of in the KF. Obtained through SI techniques [5], the model of the compass is given by…”

Section: Navigationmentioning

confidence: 99%

Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control

Motwani

Sutton

Yang

et al. 2013

IET Conference on Control and Automation 2013: Uniting Problems and Solutions

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“…While numerous techniques exist, one approach, known as decentralised Kalman filtering, is based on fusing estimates from various local KFs. 6 The method described here is one such technique, but its novelty lies in that it has been designed with the intent of being able to fuse individual weighted interval Kalman filter (wIKF) estimates as well.…”

Section: Objectives and Outlinementioning

confidence: 99%

An interval Kalman filter–based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle

Motwani

Liu

Sharma

et al. 2015

Proceedings of the Institution of Mechanical Engineers, Part M:

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This article presents a novel fuzzy-logic based multi-sensor data fusion algorithm for combining heading estimates from three separate weighted interval Kalman filters to construct a robust, fault-tolerant heading estimator for the navigation of the Springer autonomous surface vehicle. A single, low-cost gyroscopic unit and three independent compasses are used to acquire data onboard the vehicle. The gyroscope data, prone to sporadic bias drifts, are fused individually with readings from each of the compasses via a weighted interval Kalman filter. Unlike the standard Kalman filter, the weighted interval Kalman filter is able to provide a robust heading estimate even when subject to such gyroscope bias drifts. The three ensuing weighted interval Kalman filter estimates of the vehicle's heading are then fused via a fuzzy logic algorithm designed to provide an accurate heading estimate even when two of the three compasses develop a fault at any time. Simulations and real-time trials demonstrate the effectiveness of the proposed method.

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“…The Springer USV project commenced in 2004 by the Marine and Industrial Dynamic Analysis (MIDAS) Research Group at Plymouth University (Xu, 2007).…”

Section: Introductionmentioning

confidence: 99%

“…Several research work programs have been undertaken to improve the reliability and accuracy of the NGC system. For example, a fuzzy logic based multi-sensor data fusion algorithm (Xu et al, 2007) and an interval Kalman filter (Motwani et al, 2013) have been implemented for accurate positioning to navigate the vehicle. In addition, a linear quadratic Gaussian theory based controller and a genetic algorithm based model predictive controller (Sharma and Sutton, 2013) have been developed for the autopilot in the NGC system.…”

Section: Introductionmentioning

confidence: 99%

Smoothed A* algorithm for practical unmanned surface vehicle path planning

Song

Liu

Bucknall

2019

Applied Ocean Research

220

103

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An effective path planning or route planning algorithm is essential for guiding unmanned surface vehicles (USVs) between way points or along a trajectory. The A* algorithm is one of the most efficient algorithms for calculating a safe route with the shortest distance cost. However, the route generated by the conventional A* algorithm is constrained by the resolution of the map and it may not be compatible with the nonholonomic constraint of the USV. In this paper an improved A* algorithm has been proposed and applied to the Springer USV. A new path smoothing process with three path smoothers has been developed to improve the performance of the generated route, reducing unnecessary 'jags', having no redundant waypoints and offering a more continuous route. Both simulation and experimental results show that the smoothed A* algorithm outperforms the conventional algorithm in both sparse and cluttered environments that have been uniformly rasterised. It has been demonstrated that the proposed improved A* route planning algorithm can be applied to the Springer USV providing promising results when tracking trajectories.

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A Fault Tolerant Multi-Sensor Navigation System for an Unmanned Surface Vehicle

Cited by 8 publications

References 3 publications

Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control

Integrated navigation and control system for an uninhabited surface vehicle based on interval Kalman filtering and model predictive control

An interval Kalman filter–based fuzzy multi-sensor fusion approach for fault-tolerant heading estimation of an autonomous surface vehicle

Smoothed A* algorithm for practical unmanned surface vehicle path planning

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