This paper presents a method for controlling a swarm of quadrotors to perform agile interleaved maneuvers while holding a fixed relative formation, or transitioning between different formations. The method prevents collisions within the swarm, as well as between the quadrotors and static obstacles in the environment. The method is built upon the existing notion of a virtual structure, which serves as a framework with which to plan and execute complex interleaved trajectories, and also gives a simple, intuitive interface for a single human operator to control an arbitrarily large aerial swarm in real time. The virtual structure concept is integrated with differential flatness-based feedback control to give an end-to-end integrated swarm teleoperation system. Collision avoidance is achieved by using multiple layered potential fields. Our method is demonstrated in hardware experiments with groups of 3-5 quadrotors teleoperated by a single human operator, and simulations of 200 quadrotors teleoperated by a single human operator.

This paper presents a method for controlling quadrotor swarms in an environment with obstacles, with an intuitive human-swarm interface operated by a single human user. Our method allows for the quadrotor swarm to maintain a desired formation, while also keeping the quadrotors a safe distance from obstacles and from one another. We use a Virtual Rigid Body abstraction to provide a bridge between the single human user and the quadrotor swarm, so that a human user can fly an arbitrarily large quadrotor swarm from a single joystick. By applying multiple vector fields, collisions are automatically avoided within the swarm of quadrotors, and between the quadrotors and obstacles, while the Virtual Rigid Body is controlled by the human user. Our method is demonstrated in hardware experiments with groups of quadrotor micro aerial vehicles teleoperated by a single human operator in a motion capture system.

In this paper, we present a fully distributed solution to drive a team of robots to reach a desired formation in the absence of an external positioning system that localizes them. Our solution addresses two fundamental problems that appear in this context. First, we propose a 3-D distributed control law, designed at a kinematic level, that uses two simultaneous consensus controllers: one to control the relative orientations between robots, and another for the relative positions. The convergence to the desired configuration is shown by comparing the system with time-varying orientations against the equivalent approach with fixed orientations, showing that their difference vanishes as time goes to infinity. Second, in order to apply this controller to a group of aerial robots, we combine this idea with a novel sensor fusion algorithm to estimate the relative pose of the robots by using onboard cameras and information from the inertial measurement unit. The algorithm removes the influence of roll and pitch from the camera images and estimates the relative pose between robots by using a structure from the motion approach. Simulation results, as well as hardware experiments with a team of three quadrotors, demonstrate the effectiveness of the controller and the vision system working together.

This paper proposes a method for controlling a team of quadrotor micro aerial vehicles to perform agile maneuvers while holding a fixed relative formation, as well as transitioning between a sequence of formations. The objective is to coordinate the quadrotors to fly in intricate interlaced patterns, similarly to an air show demonstration team. The paper proposes a new abstraction, called a Virtual Rigid Body, which allows the quadrotors to hold relative positions while executing agile maneuvers as a group. By planning trajectories for the Virtual Rigid Body in SE(3), trajectories for each quadrotor are obtained in order to maintain the desired formation during the maneuver. The paper also proposes a method for sequencing a series of Virtual Rigid Body formations, and automatically designing collision free transitions between successive formations, while the team simultaneously executes a trajectory in SE(3). The resulting sequence of formations and transitions gives trajectories that weave intricate designs while avoiding collisions. The method is demonstrated experimentally with three KMel K500 quadrotors flying in a motion capture environment.

This paper proposes a differential flatness-based method for maneuvering a quadrotor so that its position follows a specified velocity vector field. Existing planning and control algorithms often give a 2D or 3D velocity vector field to be followed by a robot. However, quadrotors have complex nonlinear dynamics that make vector field following difficult, especially in aggressive maneuvering regimes. This paper exploits the differential flatness property of a quadrotor's dynamics to control its position along a given vector field. Differential flatness allows for the analytical derivation of control inputs in order to control the 12D dynamical state of the quadrotor such that the 2D or 3D position of the quadrotor follows the flow specified by a given vector field. The method is derived mathematically, and demonstrated in numerical simulations and in experiments with a quadrotor robot for three different vector fields.