Cooperative quadrocopter ball throwing and catching

Ritz, Robin; Muller, Mark; Hehn, Markus; D’Andrea, Raffaello

doi:10.1109/iros.2012.6385963

Cited by 121 publications

(69 citation statements)

References 12 publications

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“…These GPS-based solutions have powered various commercial aerial swarm shows by Intel, EHang, and others. In indoor GPSdenied environments, optical motion capture systems enable millimeter-level position tracking utilizing multiple infrared cameras [216], [217]. Alternatively, UWB-based solutions offer a less expensive and more flexible, but less accurate, state estimate for large-scale indoor aerial swarms [218].…”

Section: Pose and State Estimationmentioning

confidence: 99%

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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: Pose and State Estimationmentioning

confidence: 99%

“…Centralized topologies with one ground station and multiple agents are used for most cases [209], [216], [217]. Decentralized communication topologies are still mostly used in the realm of theoretical research [57], [59].…”

Section: Communication Infrastructurementioning

confidence: 99%

A Survey on Aerial Swarm Robotics

et al. 2018

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“…Each control signal defines the desired rollΦ and pitchΘ angle, the yaw rotational speedΨ and the vertical velocityz; each as a fraction of the respective maximal allowed value 3 . This corresponds exactly to the information provided by a human pilot remote-controlling the quadrocopter via smartphone.…”

Section: Controlmentioning

confidence: 99%

“…Significant progress has been made, and recent examples include aggressive flight manoeuvres [1], collaborative construction tasks [2], ball throwing and catching [3] or the coordination of large fleets of quadrocopters [4]. However, all of these systems require external motion capture systems.…”

Section: Introductionmentioning

confidence: 99%

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Engel

Sturm

Cremers

2014

Robotics and Autonomous Systems

185

118

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We present a complete solution for the visual navigation of a small-scale, low-cost quadrocopter in unknown environments. Our approach relies solely on a monocular camera as the main sensor, and therefore does not need external tracking aids such as GPS or visual markers. Costly computations are carried out on an external laptop that communicates over wireless LAN with the quadrocopter. Our approach consists of three components: a monocular SLAM system, an extended Kalman filter for data fusion, and a PID controller. In this paper, we (1) propose a simple, yet effective method to compensate for large delays in the control loop using an accurate model of the quadrocopter's flight dynamics, and (2) present a novel, closed-form method to estimate the scale of a monocular SLAM system from additional metric sensors. We extensively evaluated our system in terms of pose estimation accuracy, flight accuracy, and flight agility using an external motion capture system. Furthermore, we compared the convergence and accuracy of our scale estimation method for an ultrasound altimeter and an air pressure sensor with filtering-based approaches. The complete system is available as open-source in ROS. This software can be used directly with a low-cost, off-the-shelf Parrot AR.Drone quadrocopter, and hence serves as an ideal basis for follow-up research projects.

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“…However, Global Positioning System (GPS)-based flight [1] can not fly in the indoor environments and closely to the ground or urban canyons, where there is no/unstable GPS service. Motion Capture System (MCS)-based flying [39] just work in the indoor and local space where many expensive cameras with high speed are constructed. Laser Range Finder (LRF)-based flight [9] can not work in the open and broad places because of its limited detection distance.…”

Section: Related Workmentioning

confidence: 99%

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

Olivares-Méndez

Suarez-Fernandez

et al. 2013

J Intell Robot Syst

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In this paper, we developed a novel Cross-Entropy Optimization (CEO)-based Fuzzy Logic Controller (FLC) for Fail-Safe UAV to expand its collision avoidance capabilities in the GPS-denied environments using Monocular Visual-Inertial SLAM-based strategy. The function of this FLC aims to control the heading of Fail-Safe UAV to avoid the obstacle, e.g. wall, bridge, tree line et al, using its real-time and accurate localization information. In the Matlab Simulink-based training framework, the Scaling Factor (SF) is adjusted according to the collision avoidance task firstly, and then the Membership Function (MF) is tuned based on the optimized Scaling Factor to further improve the control performances. After obtained the optimal SF and MF, 64 % of rules has been reduced (from 125 rules to 45 rules), and a large number of real see-and-avoid tests with a quadcopter have done. The simulation and experiment results show that this new proposed FLC can precisely navigates the Fail-Safe UAV to avoid the obstacle, obtaining better performances compared to only SF optimization-based FLC. To our best knowledge, this is the first work to present the optimized FLC using Cross-Entropy method in both SF and MF optimization, and apply it in the UAV.

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Cooperative quadrocopter ball throwing and catching

Cited by 121 publications

References 12 publications

A Survey on Aerial Swarm Robotics

A Survey on Aerial Swarm Robotics

Scale-aware navigation of a low-cost quadrocopter with a monocular camera

Monocular Visual-Inertial SLAM-Based Collision Avoidance Strategy for Fail-Safe UAV Using Fuzzy Logic Controllers

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