A Low-cost Vision Based Navigation System for Small Size Unmanned Aerial Vehicle Applications

Sabatini, Roberto; Richardson, Mark A.; Bartel, Celia; Shaid, Tesheen; Ramasamy, Subramanian

doi:10.4172/2168-9792.1000110

Cited by 20 publications

(10 citation statements)

References 20 publications

(26 reference statements)

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“…Although commercially available Micro Electromechanical Systems (MEMS) devices can support the design of low cost IMUs, their accuracy decreases steeply with operating time. The performance of VBN sensors is affected by low visibility conditions which could be due to night/low light conditions, smoke, fog, precipitation as well as presence of transparent or opaque objects [ 70 ]. An ADM virtual sensor is dependent on the aircraft’s physical sensors and is based on the assumption of the aircraft being a rigid body with a constant and static mass distribution [ 71 ].…”

Section: Integration Of Acoustic Sensors In Multi-sensor Navigatiomentioning

confidence: 99%

Acoustic Sensors for Air and Surface Navigation Applications

Kapoor

Ramasamy

Gardi

et al. 2018

Sensors

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This paper presents the state-of-the-art and reviews the state-of-research of acoustic sensors used for a variety of navigation and guidance applications on air and surface vehicles. In particular, this paper focuses on echolocation, which is widely utilized in nature by certain mammals (e.g., cetaceans and bats). Although acoustic sensors have been extensively adopted in various engineering applications, their use in navigation and guidance systems is yet to be fully exploited. This technology has clear potential for applications in air and surface navigation/guidance for intelligent transport systems (ITS), especially considering air and surface operations indoors and in other environments where satellite positioning is not available. Propagation of sound in the atmosphere is discussed in detail, with all potential attenuation sources taken into account. The errors introduced in echolocation measurements due to Doppler, multipath and atmospheric effects are discussed, and an uncertainty analysis method is presented for ranging error budget prediction in acoustic navigation applications. Considering the design challenges associated with monostatic and multi-static sensor implementations and looking at the performance predictions for different possible configurations, acoustic sensors show clear promises in navigation, proximity sensing, as well as obstacle detection and tracking. The integration of acoustic sensors in multi-sensor navigation systems is also considered towards the end of the paper and a low Size, Weight and Power, and Cost (SWaP-C) sensor integration architecture is presented for possible introduction in air and surface navigation systems.

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Section: Integration Of Acoustic Sensors In Multi-sensor Navigatiomentioning

confidence: 99%

Acoustic Sensors for Air and Surface Navigation Applications

Kapoor

Ramasamy

Gardi

et al. 2018

Sensors

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“…As stated earlier, the ADM can employ either a 6-DoF or a 3-DoF ADM with suitable constraints applied in the different phases of the RPAS flight. The input data required to run these knowledge-based modules are made available from aircraft physical sensors (i.e., aircraft data network stream) and from ad-hoc databases [6,7].…”

Section: Aircraft Dynamics Modelmentioning

confidence: 99%

Aircraft Dynamics Model Augmentation for RPAS Navigation and Guidance

Cappello¹,

Bijjahalli²,

Ramasamy³

et al. 2017

J Intell Robot Syst

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In this paper, Aircraft Dynamics Model (ADM) augmentation for Remotely Piloted Aircraft System (RPAS) navigation and guidance is presented. This approach provides additional information suitable to compensate for the shortcomings of vision based navigation sensors and Micro-Electromechanical System Inertial Measurement Unit (MEMS-IMU) sensors for attitude determination tasks. The ADM virtual sensor is essentially a knowledge-based module and is used to augment the navigation state vector by predicting RPAS flight dynamics (aircraft trajectory and attitude motion). The ADM employs a rigid body 6-Degree of Freedom (6-DoF) model and is implemented in integrated multi-sensor data fusion architectures. The integration is accomplished with an Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF). After introducing the key mathematical models describing the 6-DoF ADM, the sensor and integrated system performance are compared in a small RPAS integration scheme (i.e., AEROSONDE RPAS platform) exploring a representative cross-section of the aircraft operational flight envelope and a preliminary sensitivity analysis is performed. In addition to a centralised filter, a dedicated ADM processor (i.e., a local pre-filter) is adopted to account for the RPAS manoeuvring envelope in different flight phases, in order to extend the ADM validity time across all segments of the RPAS trajectory. Sensitivity analysis of the errors caused by perturbations in the input parameters of the aircraft dynamics is performed to demonstrate the robustness of the proposed approach. Results verify that the ADM virtual sensor provides improved performance in terms of attitude data accuracy and a significant extension of the ADM validity time is achieved by pre-filtering.

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“…The satellite position and velocity are calculated from the Kepler's laws of orbital motion using either the YUMA or SEM almanac data [28,29]. Various geometric parameters were extracted from the literature to draw a detailed CATIA model of the AEROSONDE UA [30][31][32][33][34]. The ABIA integration into an existing UAS SAA architecture was studied in cooperative and non-cooperative SAA scenarios.…”

Section: Simulation Activitiesmentioning

confidence: 99%

Assessing avionics-based GNSS integrity augmentation performance in UAS mission- and safety-critical tasks

Sabatini

Moore

Hill

et al. 2015

2015 International Conference on Unmanned Aircraft Systems (ICUAS)

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Abstract-The integration of Global Navigation Satellite System (GNSS) integrity augmentation functionalities in Unmanned Aerial Systems (UAS) has the potential to provide an integrity-augmented Sense-and-Avoid (SAA) solution suitable for cooperative and non-cooperative scenarios. In this paper, we evaluate the opportunities offered by this integration, proposing a novel approach that maximizes the synergies between Avionics Based Integrity Augmentation (ABIA) and UAS cooperative/non-cooperative SAA architectures. When the specified collision risk thresholds are exceeded, an avoidance manoeuvre is performed by implementing a heading-based differential geometry or pseudospectral optimization to generate a set of optimal trajectory solutions free of mid-air conflicts. The optimal trajectory is selected using a cost function with minimum time constraints and fuel penalty criteria weighted for separation distance. The optimal avoidance trajectory also considers the constraints imposed by the ABIA in terms of UAS platform dynamics and GNSS satellite elevation angles (plus jamming avoidance when applicable), thus preventing degradation or loss of navigation data during the Track, Decision and Avoidance (TDA) process. The performance of this IntegrityAugmented SAA (IAS) architecture was evaluated by simulation case studies involving cooperative and noncooperative platforms. Simulation results demonstrate that the proposed IAS architecture is capable of performing highintegrity conflict detection and resolution when GNSS is used as the primary source of navigation data.

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A Low-cost Vision Based Navigation System for Small Size Unmanned Aerial Vehicle Applications

Cited by 20 publications

References 20 publications

Acoustic Sensors for Air and Surface Navigation Applications

Acoustic Sensors for Air and Surface Navigation Applications

Aircraft Dynamics Model Augmentation for RPAS Navigation and Guidance

Assessing avionics-based GNSS integrity augmentation performance in UAS mission- and safety-critical tasks

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