Flocking Towards the Source: Indoor Experiments with Quadrotors

Datar, Adwait; Paulsen, Peter; Werner, Herbert

doi:10.23919/ecc51009.2020.9143867

Cited by 6 publications

(17 citation statements)

References 0 publications

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“…The results obtained from hard-IQCs can be interpreted with dissipativity based arguments and vice-versa 1 . This equivalence connects our earlier work [4], [9] on analyzing stability of source-seeking dynamics with dissipativity based arguments to this paper where we use IQCs.…”

Section: Introductionsupporting

confidence: 83%

“…Stability analysis for source-seeking dynamics within the flocking framework is studied in [4], [9] albeit without any performance guarantees. Our main motivation in this paper is to extend these results to performance analysis.…”

Section: Related Workmentioning

confidence: 99%

“…These vehicles are typically assumed to be able to estimate the gradient of the field at their respective locations either via communication with others or by making several measurements around their positions (see [2]. [3], [4] for details). Although the theoretical results are under the assumption of strongly convex fields and availability of gradients, experimental results [4] on quasi-convex and non-convex fields with noisy measurements show that with a pre-filtering step, which involves fitting a strongly convex field with data collected in the local neighborhood, these protocols perform reasonably well for some scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…[3], [4] for details). Although the theoretical results are under the assumption of strongly convex fields and availability of gradients, experimental results [4] on quasi-convex and non-convex fields with noisy measurements show that with a pre-filtering step, which involves fitting a strongly convex field with data collected in the local neighborhood, these protocols perform reasonably well for some scenarios.…”

Section: Introductionmentioning

confidence: 99%

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Robust Performance Analysis of Source-Seeking Dynamics with Integral Quadratic Constraints

Datar¹,

Werner²

2021

Preprint

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We analyze the performance of source-seeking dynamics involving either a single vehicle or multiple flockingvehicles embedded in an underlying strongly convex scalar field with gradient based forcing terms. For multiple vehicles under flocking dynamics embedded in quadratic fields, we show that the dynamics of the center of mass are equivalent to the dynamics of a single agent. We leverage the recently developed framework of α-integral quadratic constraints (IQCs) to obtain convergence rate estimates. We first present a derivation of hard Zames-Falb (ZF) α-IQCs involving general non-causal multipliers based on purely time-domain arguments and show that a parameterization of the ZF multiplier, suggested in the literature for the standard version of the ZF IQCs, can be adapted to the α-IQCs setting to obtain quasi-convex programs for estimating convergence rates. Owing to the time-domain arguments, we can seamlessly extend these results to linear parameter varying (LPV) vehicles possibly opening the doors to non-linear vehicle models with quasi-LPV representations. We illustrate the theoretical results on a linear time invariant (LTI) model of a quadrotor, a non-minimum phase LTI plant and two LPV examples which show a clear benefit of using general non-causal dynamic multipliers to drastically reduce conservatism.

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Section: Introductionsupporting

confidence: 83%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robust Performance Analysis of Source-Seeking Dynamics with Integral Quadratic Constraints

Datar¹,

Werner²

2021

Preprint

Self Cite

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“…[35] implemented gradientbased control of three DJI Flamewheel 450 UAVs in fixed circular formations to demonstrate moving light source seeking and three Crazyflie 2.0 UAVs to perform level set tracking/tracing about a simulated scalar field (computed, not sensed) in a small enclosed area ~2 m 2 . [73] used seven Crazyflie 2.1 UAVs at fixed altitudes to swarm towards simulated scalar field sources (computed, not sensed) in a region ~12 m 2 .…”

Section: Ia Adaptive Navigation In 2d Scalar Fieldsmentioning

confidence: 99%

Multiple UAV Adaptive Navigation for Three-Dimensional Scalar Fields

Lee¹,

Kitts

Neumann

et al. 2021

IEEE Access

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Adaptive Navigation (AN) control strategies allow an agent to autonomously alter its trajectory based on realtime measurements of the environment. Compared to conventional navigation methods, these techniques can reduce required time and energy to explore scalar characteristics of unknown and dynamic regions of interest (e.g., temperature, concentration level). Multiple Uncrewed Aerial Vehicle (UAV) approaches to AN can improve performance by exploiting synchronized spatially-dispersed measurements to generate realtime information regarding the structure of the local scalar field, which is then used to inform navigation decisions. This article presents initial results of a comprehensive program to develop, verify, and experimentally implement mission-level AN capabilities in three-dimensional (3D) space using our unique multilayer control architecture for groups of vehicles. Using our flexible formation control system, we build upon our prior 2D AN work and provide new contributions to 3D scalar field AN by a) demonstrating a wide range of 3D AN capabilities using a unified, multilayer control architecture, b) extending multivehicle 2D AN control primitives to navigation in 3D scalar fields, and c) introducing statebased sequencing of these primitive AN functions to execute 3D mission-level capabilities such as isosurface mapping and plume following. We verify functionality using high-fidelity simulations of multicopter drone clusters, accounting for vehicle dynamics, outdoor wind gust disturbances, position sensor inaccuracy, and scalar field sensor noise. This paper presents the multilayer architecture for multivehicle formation control, the 3D AN control primitives, the sequencing approaches for specific mission-level capabilities, and simulation results that demonstrate these functions.

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