Towards a swarm of agile micro quadrotors

By Aleksandr Kushleyev, Vijay R. Kumar, Daniel Mellinger. Autonomous Robots, 2012

578 citations recorded by the source corpus.

We describe a prototype 75 g micro quadrotor with onboard attitude estimation and control that operates autonomously with an external localization system. The motivation for designing quadrotors at this scale comes from two observations. First, the agility of the robot increases with a reduction in size, a fact that is supported by experimental results in this paper. Second, smaller robots are able to operate in tight formations in constrained, indoor environments. We describe the hardware and software used to operate the vehicle as well our dynamic model. We also discuss the aerodynamics of vertical flight and the contribution of ground effect to the vehicle performance. Finally, we discuss architecture and algorithms to coordinate a team of these quadrotors, and provide experimental results for a team of 20 micro quadrotors.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/b063239bd450038531eeb2db5466eaed34a0f9a0

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Citations in the corpus

Geometrically Constrained Trajectory Optimization for Multicopters (2021, background): “Intuitively, the quadcopter can only achieve no more than 1 revolution per second (rps), making it less agile than small quadcopters [72] that can achieve 5rps.”
A Novel Overactuated Quadrotor Unmanned Aerial Vehicle: Modeling, Control, and Experimental Validation (2015, methodology): “employing quadrotor UAVs as robotics platforms: planning and control for aggressive flight maneuvers [6], collective control of multiple small- and micro-quadrotors [7], [8], and vision-based state estimation for autonomous flight [9] are just a few examples.”
Physical and chemical reinforcement of chitosan film using nanocrystalline cellulose and tannic acid (2015, background): “Researchers are now studying swarm technology with the objective of distributing a group of BMAV in prescribed flying patterns (Kushleyev et al. 2013).”
Learning monocular reactive UAV control in cluttered natural environments (2012, background): “Our work is primarily concerned with navigating MAVs that have very low payload capabilities, and operate close to the ground where they cannot avoid dense obstacle fields.”
Optimized flocking of autonomous drones in confined environments (2018, background): “Autonomous drone swarms also appear in the scientific literature, using indoor motion capture–based (29, 30), outdoor Global Positioning System (GPS)–based (24, 31–33), or even vision-assisted (34, 35) navigation.”
Time-varying formation control for unmanned aerial vehicles with switching interaction topologies (2014, background): “In the past decade, many results on UAV formation have been proposed based on leader-follower, virtual structure and behavior approaches (see [4]–[8] and references therein).”
Crazyswarm: A large nano-quadcopter swarm (2017, methodology): “describe the design, planning, and control of a custom micro quadcopter with experiments involving up to 20 vehicles [1].”
Pushbroom stereo for high-speed navigation in cluttered environments (2014, background): “Recently we have seen an explosion of exciting results on small, unmanned aerial vehicles (UAVs) such as obstacle avoidance and trajectory tracking [24], formation flight [20], [25], and cooperative interactions with the environment [6], [23], [29].”
Time-Varying Formation Control for Unmanned Aerial Vehicles: Theories and Applications (2015, background): “Digital Object Identiﬁer 10.1109/TCST.2014.2314460 swarm systems based on virtual structure approaches can be found in [17]– [19].”
A Survey on Aerial Swarm Robotics (2018, background): “In indoor GPS-denied environments, optical motion capture systems enable millimeter-level position tracking utilizing multiple infrared cameras [216], [217].”
Embedded Data Representations (2017, background): “Swarms of self-organizing drones [28] could also use light, motion, or projection to visualize data at environmental scales (as in Figure 1f).”