Unmanned Aerial Vehicle Optimal Cooperative Obstacle Avoidance in a Stochastic Dynamic Environment

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

doi:10.2514/1.50800

Cited by 15 publications

(2 citation statements)

References 13 publications

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“…Shaferman and Shima 11 propose a distributed evolutionary-based stochastic search method for a team of heterogeneous UAVs to track targets in a known urban terrain. Prevost et al 12 present an extended Kalman filter–based algorithm that predicts the trajectory of a moving object from its measured position.…”

Section: Related Workmentioning

confidence: 99%

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Huang

Xiao

et al. 2018

International Journal of Distributed Sensor Networks

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Persistent surveillance is one of the major tasks envisioned for unmanned aerial vehicles in that some regions of interest need to be continuously surveyed. This distinction from the one-time coverage/exploration does not allow a straightforward application of the most exploration techniques to address the persistent surveillance problem. This article introduces and demonstrates a method of alterable artificial potential field to control a group of unmanned aerial vehicles to stay over the region of interest. We present attractive potential field to drive the unmanned aerial vehicles to desirable areas, obstacle potential field to move away from obstacles, collision avoidance potential field to control unmanned aerial vehicles not to collide with others, and formation of potential field to make unmanned aerial vehicles gather in a group. The simulation results show that the proposed approach could generate collision-free path for unmanned aerial vehicles staying over the region of interest for a long endurance.

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Section: Related Workmentioning

confidence: 99%

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Huang

Xiao

et al. 2018

International Journal of Distributed Sensor Networks

View full text Add to dashboard Cite

show abstract

“…Vitaly et al 10 propose a combined task assignment and motion planning algorithm for multiple UAVs to track targets in a known urban environment. Carole et al 11 present an extended Kalman-filter-based algorithm that predicts the position of obstacles. However, these works are mainly oriented for the one-time mission.…”

Section: Related Workmentioning

confidence: 99%

Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm

2014

Proceedings of the Institution of Mechanical Engineers, Part G:

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Persistent surveillance is a major role envisioned for autonomous unmanned vehicles. The mission of persistent surveillance requires the vehicles to continuously survey a target region. This paper investigates the techniques of persistent surveillance control for a swarm of micro aerial vehicles. We present a flocking algorithm to drive the micro aerial vehicles flying in a coordinate formation with a capability of obstacle avoidance. We propose a new digital pheromone mechanism to control and coordinate the swarms of micro aerial vehicles to search a field of interest and to reduce the uncertainty of every region in the field over time. Simulation results show the effectiveness of our proposed algorithm in generating collision-free persistent surveillance trajectories for a swarm of micro aerial vehicles in a coordinated manner.

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Adaptive control and signal processing literature survey (No. 23)

2011

Adaptive Control & Signal

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In order to keep readers up-to-date, the journal will contain a list of recently published journal articles relevant to the fields of adaptive control and signal processing six times a year. This list is compiled from a wide range of journals. Please note that although the list has been broadly categorized these classifications are by no means strict, and are there to assist the reader. Please also note that inclusion in the list is not an endorsement of a paper's quality. If you have any suggestions or comments, please e-mail Erfu Yang at erfu.yang@eee.strath.ac.uk.

show abstract

Unmanned Aerial Vehicle Optimal Cooperative Obstacle Avoidance in a Stochastic Dynamic Environment

Cited by 15 publications

References 13 publications

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors

Persistent surveillance for a swarm of micro aerial vehicles by flocking algorithm

Adaptive control and signal processing literature survey (No. 23)

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