UAV collision avoidance using cooperative predictive control

Boivin, Eric B; Desbiens, André; Gagnon, Éric

doi:10.1109/med.2008.4602109

Cited by 39 publications

(21 citation statements)

References 15 publications

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“…Lee concludes that the path of the model predictive control is more optimal. Another MPC implementation is done by Boivin et al (2008), Boivon does not use a penalty in the cost function but avoids the obstacles by adding constraints to the optimization process.…”

Section: -2-4 Path Planningmentioning

confidence: 99%

Performance evaluation in obstacle avoidance

Nous

Meertens

Wagter

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Section: -2-4 Path Planningmentioning

confidence: 99%

Performance evaluation in obstacle avoidance

Nous

Meertens

Wagter

et al. 2016

2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…In particular, (Wang et al, 2007, Boivin et al, 2008 proposed approaches based on model predictive control. Despite these approaches allow to effectively deal with the collision problem, they assume the use of a cooperation strategy in a centralized fashion, that can be inefficient in certain cases, e.g.…”

Section: Collision Avoidancementioning

confidence: 99%

“…Furthermore, when more than one UAV is simultaneously in action, the strategies initially adopted in the literature were based on a centralized approach (Boivin et al, 2008, Wang et al, 2007, where the computational load is concentrated on a single central computational unit. However, this strategy becomes inefficient when dealing with a large number of devices.…”

Section: Introductionmentioning

confidence: 99%

“…Longer intervals can be useful in order to obtain less restrictive trajectory changes, however at the cost of a significant increase in the computational complexity of the algorithm. When the operating conditions allow to consider a centralized approach, then strategies based on considering possible collisions in longer time intervals and model predictive control (MPC) can be considered (Boivin et al, 2008, Wang et al, 2007.…”

Section: Introductionmentioning

confidence: 99%

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Uav Positioning and Collision Avoidance Based on RSS Measurements

Masiero

Fissore

Guarnieri

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In recent years, Unmanned Aerial Vehicles (UAVs) are attracting more and more attention in both the research and industrial communities: indeed, the possibility to use them in a wide range of remote sensing applications makes them a very flexible and attractive solution in both civil and commercial cases (e.g. precision agriculture, security and control, monitoring of sites, exploration of areas difficult to reach). Most of the existing UAV positioning systems rely on the use of the GPS signal. Despite this can be a satisfactory solution in open environments where the GPS signal is available, there are several operating conditions of interest where it is unavailable or unreliable (e.g. close to high buildings, or mountains, in indoor environments). Consequently, a different approach has to be adopted in these cases. This paper considers the use of WiFi measurements in order to obtain position estimations of the device of interest. More specifically, to limit the costs for the devices involved in the positioning operations, an approach based on radio signal strengths (RSS) measurements is considered. Thanks to the use of a Kalman filter, the proposed approach takes advantage of the temporal dynamic of the device of interest in order to improve the positioning results initially provided by means of maximum likelihood estimations. The considered UAVs are assumed to be provided with communication devices, which can allow them to communicate with each other in order to improve their cooperation abilities. In particular, the collision avoidance problem is examined in this work.

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“…Therefore it may fail to provide solution in a dense environments. Some of the research addressing this type can be found in (Lapp & Singh, 2004;Boivin et al, 2008). Most of the research done in this field based on decentralized CAS architecture (Borrelli et al, 2004;Lalish & Morgansen, 2008;Keviczky et al, 2008;Roozbehani et al, 2009;Sislak et al, 2011).…”

Section: Other Collision Avoidance Factorsmentioning

confidence: 99%