Control of Ensembles of Aerial Robots

Michael, Nathan; Kumar, Vijay

doi:10.1109/jproc.2011.2157275

Cited by 32 publications

(15 citation statements)

References 45 publications

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“…This was meant as an educational tool to teach young scholars to work with hardware and to give them a testbed to implement their ideas, but the swarm only had a handful of robots in it. Other indoor swarms utilize motion capture systems for localization [207], [208]. More recently, the field has focused on expanding the size of the swarm, with one of the largest indoor swarms consisting of 49 CrazyFlie quadrotors simultaneously flying in a motion capture system [209].…”

Section: A Platformsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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Abstract-The use of aerial swarms to solve real-world problems has been increasing steadily, accompanied by falling prices and improving performance of communication, sensing, and processing hardware. The commoditization of hardware has reduced unit costs, thereby lowering the barriers to entry to the field of aerial swarm robotics. A key enabling technology for swarms is the family of algorithms that allow the individual members of the swarm to communicate and allocate tasks amongst themselves, plan their trajectories, and coordinate their flight in such a way that the overall objectives of the swarm are achieved efficiently. These algorithms, often organized in a hierarchical fashion, endow the swarm with autonomy at every level, and the role of a human operator can be reduced, in principle, to interactions at a higher level without direct intervention. This technology depends on the clever and innovative application of theoretical tools from control and estimation. This paper reviews the state of the art of these theoretical tools, specifically focusing on how they have been developed for, and applied to, aerial swarms. Aerial swarms differ from swarms of ground-based vehicles in two respects: they operate in a three-dimensional (3-D) space, and the dynamics of individual vehicles adds an extra layer of complexity. We review dynamic modeling and conditions for stability and controllability that are essential in order to achieve cooperative flight and distributed sensing. The main sections of the paper focus on major results covering trajectory generation, task allocation, adversarial control, distributed sensing, monitoring, and mapping. Wherever possible, we indicate how the physics and subsystem technologies of aerial robots are brought to bear on these individual areas.

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Section: A Platformsmentioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…γ,xy s xy (11) whereγ xy ∈ R 2 represents the sole wind effect supposed constant in the inertial frame, and ϕ and θ can be easily computed yielding the desired ϕ d and θ d sent to the orientation controller.…”

Section: B Horizontal Position Controlmentioning

confidence: 99%

“…In order to test the controller with the minimal set of parameters, we have implemented the sole reduced version, i.e., the version in (8), (9), (10) and (11).…”

Section: Center Of Mass Estimationmentioning

confidence: 99%

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Experimental validation of a new adaptive control scheme for quadrotors MAVs

Antonelli

Cataldi

Giordano

et al. 2013

2013 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-In this paper, an adaptive trajectory tracking controller for quadrotor MAVs is presented. The controller exploits the common assumption of a faster orientation dynamics w.r.t. the translational one, and is able to asymptotically compensate for parametric uncertainties (e.g., displaced center of mass), as well as external disturbances (e.g., wind). The good performance of the proposed controller is then demonstrated by means of an extensive experimental evaluation performed with a commercially-available quadrotor MAV.

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“…3 It is reported in [13] that the study of second-order consensus protocols in mutiagent systems (an example of coordinated control research) is quite challenging, in that second-order consensus may fail after adding just one link between a pair of agents, which unlikely happens to first-order consensus. [19], [20], [22], [24] Computationally expensive algorithms involved [12], [21], [23], [25], [26], [30], [35] Single integrator vehicle dynamics [27], [28] Flocking (no FC); double integrator vehicle dynamics (a22 = 0) [31] Only for marginally stable linear systems (a22 < 0) [29] Desired trajectory/formation known to every agent [30], [32] No collision avoidance [14] Balanced (no fully-decentralized) communication topology [33], [34] Fixed (no time-varying) communication topology ergodic (not even non-negative). Convergence analysis about the product of general matrices is quite difficult (even boundedness of the product is undecidable [17]).…”

mentioning

confidence: 99%