A Laser Obstacle Warning and Avoidance system for Manned and Unmanned Aircraft

Sabatini, Roberto; Gardi, Alessandro; Ramasamy, Subramanian; Richardson, Mark A.

doi:10.1109/metroaerospace.2014.6865998

Cited by 48 publications

(56 citation statements)

References 24 publications

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“…Research groups on robotics have proposed different techniques for obstacle avoidance, based on sensors like LiDAR [4][5][6] and RGB-D [7,8], which show robustness to identifying obstacles; but implementing these devices in a compact MAV is difficult, expensive, and also, these consume additional electrical power. When we work with UAVs and want to implement other device onboard, we need to consider the payload that they can carry, limiting the use of any UAV.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

2017

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This paper describes an obstacle avoidance system for low-cost Unmanned Aerial Vehicles (UAVs) using vision as the principal source of information through the monocular onboard camera. For detecting obstacles, the proposed system compares the image obtained in real time from the UAV with a database of obstacles that must be avoided. In our proposal, we include the feature point detector Speeded Up Robust Features (SURF) for fast obstacle detection and a control law to avoid them. Furthermore, our research includes a path recovery algorithm. Our method is attractive for compact MAVs in which other sensors will not be implemented. The system was tested in real time on a Micro Aerial Vehicle (MAV), to detect and avoid obstacles in an unknown controlled environment; we compared our approach with related works.

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Section: Related Workmentioning

confidence: 99%

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

2017

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“…In all cases, the agents are assumed to be small/mini unmanned systems [20], [21]; expected to operate in close proximity (≤ 500m) to one another. The experimental conditions used in the following studies are presented in Table II.…”

Section: Resultsmentioning

confidence: 99%

“…All UAVs are assumed to have a maximum characteristic size of r = 2.5m; the average size of a mini class UAV [21] and move at a typical cruise speed of 18m/s ≈ 40mph. The simulation time step is defined as 4Hz; the repetition frequency of existing Laser Obstacle Avoidance Marconi (LOAM) sensor devices [20].…”

Section: Resultsmentioning

confidence: 99%

An interval approach to multiple unmanned aerial vehicle collision avoidance

Douthwaite

Freitas

Mihaylova

2017

2017 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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Abstract-Small/micro Unmanned Aerial Systems (UAVs) require the ability to operate with constraints of a diverse, automated airspace where obstacle telemetry is denied. This paper proposes a novel Sense, Detect and Avoid (SDA) algorithm with inherit resilience to sensor uncertainty. This is achieved through the interval geometric formulation of the avoidance problem, which by the use of interval analysis, can be extended to consider multiple obstacles. The approach is shown to demonstrate the ability to both tolerate sensor uncertainty and enact generated 3D avoidance trajectories. Monte-Carlo simulations demonstrate successful avoidance rates of 88%, 96% and 91% in two example collision scenarios and one multi-agent conflict scenario respectively.

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“…The selection of the optimal trajectory from the generated set of safe trajectories is performed, which is then fed to the aircraft guidance subsystems. The implemented decision logic is based on minimisation of the following cost function [26,27]:…”

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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A Laser Obstacle Warning and Avoidance system for Manned and Unmanned Aircraft

Cited by 48 publications

References 24 publications

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

Obstacle Avoidance Based-Visual Navigation for Micro Aerial Vehicles

An interval approach to multiple unmanned aerial vehicle collision avoidance

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

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