Experimental autonomous flight of a small-scaled helicopter using accurate dynamics model and low-cost sensors

Vissière, David; Bristeau, Pierre-Jean; Martin, Alain Pierre; Petit, Nicolas

doi:10.3182/20080706-5-kr-1001.02480

Cited by 30 publications

(21 citation statements)

References 9 publications

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“…In these cases, the speed information is obtained from a GPS receiver using the Doppler effect (see [11] for details on the quality of the obtained measurements information) or from odometers (in the case of ground vehicles). Model based approaches permit to reduce the dependence on GPS (see [15]). …”

Section: Introductionmentioning

confidence: 99%

Iterative calibration method for inertial and magnetic sensors

Dorveaux

Vissière²,

Martin

et al. 2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Abstract-We address the problem of three-axis sensor calibration. Our focus is on magnetometers. Usual errors (misalignment, non-orthogonality, scale factors, biases) are accounted for. We consider a method where no specific calibration hardware is required. We solely use the fact that the norm of the sensed field must remain constant irrespective of the sensors orientation. The proposed algorithm is iterative. Its convergence is studied. Experiments conducted with MEMS sensors (magnetometers) stress the relevance of the approach.

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Section: Introductionmentioning

confidence: 99%

Iterative calibration method for inertial and magnetic sensors

Dorveaux

Vissière²,

Martin

et al. 2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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“…Actually, distributed filtering provides a two-level fusion of the distributed sensor measurements. At the first level, local filters running at each USV provide an estimate of the target's state vector by fusing the cartesian coordinates of the target with the target's distance from a reference surface which is measured in an inertial coordinates system [14]. At a second level, fusion of the local estimates is performed with the use of the EIF and the UIF.…”

Section: Target Tracking In Mobile Sensor Network the Problem Of Dismentioning

confidence: 99%

Distributed Control of Unmanned Surface Vessels Using the Derivative-free Nonlinear Kalman Filter

2015

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Intelligence and autonomy is becoming a prerequisite for maritime transportation systems. In this paper a distributed control problem for unmanned surface vessels (USVs) is formulated as follows: there are N USVs which pursue another vessel (moving target). Each USV can be equipped with various sensors, such as IMU, cameras and non-imaging sensors such as sonar, radar and thermal signature sensors. At each time instant each USV can obtain measurements of the target's cartesian coordinates. Additionally, each USV is aware of the target's distance from a reference monitoring station (coastal or satellite monitoring units). The objective is to make the USVs converge in a synchronized manner towards the target, while avoiding collisions between them and avoiding collisions with obstacles in their motion plane. A distributed control law is developed for the USVs which enables not only convergence of the USVs to the goal position, but also makes possible to maintain the cohesion of the multi-USV system. Moreover, distributed filtering is performed, so as to obtain an estimate of the target vessel's state vector. This provides the desirable state vector to be tracked by each one of the USVs. To this end, a new distributed nonlinear filtering method of improved accuracy and computation speed is introduced. This filtering approach, under the name Derivative-free distributed nonlinear Kalman filter is based on differential flatness theory and on an exact linearization of the target vessel's dynamic/kinematic model. The performance of the proposed distributed filtering scheme is compared against the Extended and the Unscented Information filter.

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“…Actually, distributed filtering provides a two-level fusion of the distributed sensor measurements. At the first level, local filters running at each mobile sensor provide an estimate of the target's state vector by fusing the cartesian coordinates and bearing measurements of the target with the target's distance from a reference surface which is measured in an inertial coordinates system (Vissière et al, 2008). At a second level, fusion Comparing to the traditional centralized or hierarchical fusion architecture, the network-centric architectures for the considered multi-robot system has the following advantages: (i) Scalability: since there are no limits imposed by centralized computation bottlenecks or lack of communication bandwidth, every mobile robot can easily join or quit the system, (ii) Robustness: in a decentralized fusion architecture no element of the system is mission-critical, so that the system is survivable in the event of on-line loss of part of its partial entities (mobile robots), (iii) Modularity: every partial entity is coordinated and does not need to possess a global knowledge of the network topology.…”

Section: The Problem Of Distributed Target Trackingmentioning

confidence: 99%

Target Tracking for Mobile Sensor Networks Using Distributed Motion Planning and Distributed Filtering

Rigatos¹

2011

Multi-Robot Systems, Trends and Development

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Experimental autonomous flight of a small-scaled helicopter using accurate dynamics model and low-cost sensors

Cited by 30 publications

References 9 publications

Iterative calibration method for inertial and magnetic sensors

Iterative calibration method for inertial and magnetic sensors

Distributed Control of Unmanned Surface Vessels Using the Derivative-free Nonlinear Kalman Filter

Target Tracking for Mobile Sensor Networks Using Distributed Motion Planning and Distributed Filtering

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