UAV Optimal Obstacle Avoidance while Respecting Target Arrival Specifications

Prévost, Carole Gabrielle; Desbiens, André; Gagnon, Éric; Hodouin, D.

doi:10.3182/20110828-6-it-1002.01626

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…These ground based systems provide information for manoeuvre decisions for terminal-area operations [1]. To obtain the states of the tracked obstacles, Extended Kalman Filter (EKF) is used in order to predict the trajectory in a given time horizon [9]. On-board trajectory re-planning with dynamically updated constraints based on the intruder and the host dynamics is at present used to generate obstacle avoidance trajectories [10].…”

Section: Introductionmentioning

confidence: 99%

Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

Ramasamy

Sabatini

Gardi

2014

2014 IEEE Metrology for Aerospace (MetroAeroSpace)

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Cooperative and non-cooperative Sense-and-Avoid (SAA) systems are key enablers for Unmanned Aircraft (UA) to routinely access non-segregated airspace. In this paper the stateof-the-art cooperative and non-cooperative SAA technologies for small size UA are presented and the associated multisensor data fusion techniques are discussed. The non-cooperative sensors including both passive and active Forward Looking Sensors (FLS) and the cooperative systems including Traffic Collision Avoidance System (TCAS), Automatic Dependent Surveillance - Broadcast (ADS-B) system and/or Mode C transponders form part of the SAA holistic architecture. After introducing the SAA system processes, the key mathematical models are presented. The Interacting Multiple Model (IMM) algorithm is used to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. Adopting these mathematical models, conflict detection and resolution strategies for both cooperative and un-cooperative intruders are identified. Additionally, a detailed error analysis is performed to determine the overall uncertainty volume in the airspace surrounding the intruder tracks. This is accomplished by considering both the navigation and the tracking errors affecting the measurements and translating them to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Detailed simulation case studies are carried out to evaluate the performance of the proposed SAA approach on a representative host platform (AEROSONDE UA) and various intruder platforms, including large transport aircraft and other UA. Results show that the required safe separation distance is always maintained when the SAA process is performed from ranges in excess of 500 metres.

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Section: Introductionmentioning

confidence: 99%

Avionics sensor fusion for small size unmanned aircraft Sense-and-Avoid

Ramasamy

Sabatini

Gardi

2014

2014 IEEE Metrology for Aerospace (MetroAeroSpace)

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“…Additionally, the error propagation from different sources and the impacts of host and intruders dynamics on the ultimate SAA solution were investigated [15]. SAA system requirements can be derived from the current regulations applicable for the human pilot see-and-avoid capability [17][18][19][20][21][22]. The proposed ABIA/SAA integrated architecture is illustrated in Fig.…”

Section: Abia/saa Systems Integrationmentioning

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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