A Two-Mode INS/CNS Navigation Method for Lunar Rovers

Ning, Xiaolin; Liu, Lingling

doi:10.1109/tim.2014.2307972

Cited by 61 publications

(30 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A sample list of papers, containing details about the presented systems, is reported in the references. It indicates that current research is strongly focused on indoor rather than outdoor only navigation [5], [6]. Since there is no dominant technology to realize an indoor navigation and positioning system, the papers appearing in TIM cover a wide set of different approaches and technologies, such as magnetic field sensors [7], ad-hoc UWB sensors [3], [8], ultrasounds [9], wireless RSS, and time-of-flight [10], [11], AC magnetic field sensors [4] and so on.…”

Section: Paolo Carbone Dario Petri and Antonio Tsourdosmentioning

confidence: 99%

“…Since there is no dominant technology to realize an indoor navigation and positioning system, the papers appearing in TIM cover a wide set of different approaches and technologies, such as magnetic field sensors [7], ad-hoc UWB sensors [3], [8], ultrasounds [9], wireless RSS, and time-of-flight [10], [11], AC magnetic field sensors [4] and so on. The algorithms used for signal processing are also diversified according to the type of data available for navigation purposes: while [5] and [10] use data fusion approaches, fingerprinting is used in [7], minimum square error-based techniques are used in [3] and [8], and alignment methods are used in [14]- [16].…”

Section: Paolo Carbone Dario Petri and Antonio Tsourdosmentioning

confidence: 99%

See 1 more Smart Citation

I&M in navigation systems [Framing I&M Topics]

Carbone

Petri²,

Tsourdos

2015

IEEE Instrum. Meas. Mag.

View full text Add to dashboard Cite

Section: Paolo Carbone Dario Petri and Antonio Tsourdosmentioning

confidence: 99%

Section: Paolo Carbone Dario Petri and Antonio Tsourdosmentioning

confidence: 99%

I&M in navigation systems [Framing I&M Topics]

Carbone

Petri²,

Tsourdos

2015

IEEE Instrum. Meas. Mag.

View full text Add to dashboard Cite

“…To improve the reliability of the navigation system, an inertial sensor is usually added to produce an integrated navigation system [ 13 , 14 ]. The INS/CNS integrated navigation system can be divided into two modes: Loose integration and tight integration [ 15 ]. In a loosely integrated navigation system, the inertial and star sensors work independently, where the measurement of the integrated navigation system is subject to the navigation error of both [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

Sun

et al. 2020

Sensors

View full text Add to dashboard Cite

Celestial navigation is required to improve the long-term accuracy preservation capability of near space vehicles. However, it takes a long time for traditional celestial navigation methods to identify the star map, which limits the improvement of the dynamic response ability. Meanwhile, the aero-optical effects caused by the near space environment can lead to the colorization of measurement noise, which affects the accuracy of the integrated navigation filter. In this paper, an INS/CNS deeply integrated navigation method, which includes a deeply integrated model and a second-order state augmented H-infinity filter, is proposed to solve these problems. The INS/CNS deeply integrated navigation model optimizes the attitude based on the gray image error function, which can estimate the attitude without star identification. The second-order state augmented H-infinity filter uses the state augmentation algorithm to whiten the measurement noise caused by the aero-optical effect, which can effectively improve the estimation accuracy of the H-infinity filter in the near space environment. Simulation results show that the proposed INS/CNS deeply integrated navigation method can reduce the computational cost by 50%, while the attitude accuracy is kept within 10” (3 ). The attitude root mean square of the second-order state augmented H-infinity filter does not exceed 5”, even when the parameter error increases to 50%, in the near space environment. Therefore, the INS/CNS deeply integrated navigation method can effectively improve the rapid response ability of the navigation system and the filtering accuracy in the near space environment, providing a reference for the future design of near space vehicle navigation systems.

show abstract

“…Mobile robots have been commonly utilized in outdoor environments where they have to navigate on uneven terrains to complete their operations [1]- [3]. Once their current and target locations are determined, efficient algorithms for path planning and motion control are needed to yield a successful navigation.…”

Section: Introductionmentioning

confidence: 99%

An Improved Dynamic Z* Algorithm for Rapid Replanning of Energy-Efficient Paths

Ganganath

Cheng

Tse

2015

2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

View full text Add to dashboard Cite

Abstract-Recently proposed Dynamic Z* heuristic search algorithm enables mobile robots navigating on uneven terrains to replan energy-efficient paths using previous search results whenever an obstacle is encountered. This paper proposes some vital improvements to Dynamic Z* by optimizing the use of previous search results in replanning. Simulation results show that the proposed improvements can significantly reduce the computational cost of replanning in Dynamic Z* while producing equally energy-efficient paths.

show abstract

A Two-Mode INS/CNS Navigation Method for Lunar Rovers

Cited by 61 publications

References 18 publications

I&M in navigation systems [Framing I&M Topics]

I&M in navigation systems [Framing I&M Topics]

INS/CNS Deeply Integrated Navigation Method of Near Space Vehicles

An Improved Dynamic Z* Algorithm for Rapid Replanning of Energy-Efficient Paths

Contact Info

Product

Resources

About