The GRASP Multiple Micro-UAV Testbed

By Nathan Michael, Daniel Mellinger, Quentin Lindsey, Vijay R. Kumar. IEEE robotics & automation magazine, 2010

845 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/20b0f0268bc11c55389816223d712d85203e2936

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Citations in the corpus

Flying Ad-Hoc Networks (FANETs): A survey (2013, background): “General Robotics, Automation, Sensing, and Perception (GRASP) [103] is another indoor test bed developed in University of Pennsylvania.”
Comprehensive Simulation of Quadrotor UAVs Using ROS and Gazebo (2012, background): “Recently, external optical tracking for the acquisition of ground truth data has been used with great success [8,17].”
Cooperative Grasping and Transport Using Multiple Quadrotors (2010, methodology): “We use an approach similar to those described in [12, 13].”
Design, modeling, estimation and control for aerial grasping and manipulation (2011, background): “In contrast, ingressive grippers excel at handling objects that do not have well-defined attachment points which are made of wood, foam, fabric or other porous or deformable materials.”
Opportunities and challenges with autonomous micro aerial vehicles (2012, background): “, 2010), direct sunlight, and GPS shadowing can adversely affect estimation algorithms while wind effects either due to external gusts or varying downwash arising from vehicle–environment interactions (Michael et al., 2010) can introduce perturbations in the vehicle dynamics.”
Vision-based state estimation for autonomous rotorcraft MAVs in complex environments (2013, methodology): “As such, we implemented an LQR controller based on a linearized system model [21].”
Adaptive Control of Quadrotor UAVs: A Design Trade Study With Flight Evaluations (2013, background): “tracking and regulation in indoor and outdoor settings [4], and cooperative manipulation [11], [12].”
Image Object Label 3 D CAD Model Candidate Grasps Google Object Recognition Engine Google Cloud Storage Select Feasible Grasp with Highest Success Probability Pose EstimationCamera Robots Cloud 3 D Sensor (2014, background): “definition is intended to include future systems and many existing systems that involve networked teleoperation or networked groups of mobile robots such as UAVs [100], [116] or warehouse robots [44], [96] as well as advanced assembly lines, processing plants, and home automation systems, and system”
Differential flatness based control of a rotorcraft for aggressive maneuvers (2011, methodology): “Researchers with the GRASP Lab have developed control schemes for flips, landing on walls, and flying through narrow windows [11].”
Survey on Unmanned Aerial Vehicle Networks for Civil Applications: A Communications Viewpoint (2016, methodology): “ZigBee is used for sending the control commands in various experimentation works under consideration [123], [190].”
Autonomous multi-floor indoor navigation with a computationally constrained MAV (2011, methodology): “We employ proportional-derivative feedback control laws to transform these kinematic inputs to appropriate dynamic control inputs [19].”
Information theoretic MPC for model-based reinforcement learning (2017, methodology): “We use the quadrotor model from [23], but we treat body frame angular rates and net thrust as control inputs.”
Improving quadrotor trajectory tracking by compensating for aerodynamic effects (2017, methodology): “For the configuration of the quadrotor in which motor the motors are numbered 1 through 4, increasing index in the counterclockwise direction and with motor 1 being the front motor, the equation that relates the net thrust F and net moments Mx, My , and Mz to the individual thrusts Ti is given by[13”