Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular in the film and entertainment industries, in part because of their maneuverability and perspectives they enable. While there exists methods for controlling the position and orientation of the drones for visibility, other artistic elements of the filming process, such as focal blur, remain unexplored in the robotics community. The lack of cinematographic robotics solutions is partly due to the cost associated with the cameras and devices used in the filming industry, but also because state-of-the-art photo-realistic robotics simulators only utilize a full in-focus pinhole camera model which does not incorporate these desired artistic attributes. To overcome this, the main contribution of this work is to endow the well-known drone simulator, AirSim, with a cinematic camera as well as extend its API to control all of its parameters in real time, including various filming lenses and common cinematographic properties. In this paper, we detail the implementation of our AirSim modification, CinemAirSim, present examples that illustrate the potential of the new tool, and highlight the new research opportunities that the use of cinematic cameras can bring to research in robotics and control.

In this article, we propose an online 3-D planning algorithm for a drone to race competitively against a single adversary drone. The algorithm computes an approximation of the Nash equilibrium in the joint space of trajectories of the two drones at each time step, and proceeds in a receding horizon fashion. The algorithm uses a novel sensitivity term, within an iterative best response computational scheme, to approximate the amount by which the adversary will yield to the ego drone to avoid a collision. This leads to racing trajectories that are more competitive than without the sensitivity term. We prove that the fixed point of this sensitivity enhanced iterative best response satisfies the first-order optimality conditions of a Nash equilibrium. We present results of a simulation study of races with 2-D and 3-D race courses, showing that our game theoretic planner significantly outperforms a model predictive control (MPC) racing algorithm. We also present results of multiple drone racing experiments on a 3-D track in which drones sense each others' relative position with onboard vision. The proposed game theoretic planner again outperforms the MPC opponent in these experiments where drones reach speeds up to 1.25 m/s.

In this paper we consider the problem of controlling a team of non-holonomic robots to reach a desired formation. The formation is described in terms of the desired relative positions and orientations the robots need to keep with respect to each other, and it is assumed that the robots do not have a common shared reference frame. In other words, the robots can use only on-board sensing to achieve the formation. We first consider a holonomic framework, using a well known distance-based approach to reach a formation for the positions. We then include a control law for the orientations. We further discuss the problem of mirror configurations that appear when different desired relative orientations can satisfy the same distance-based constraints through different formations. Exploiting the concept of chirality, we present a relabeling strategy to reassign the robots' roles to reach the desired pattern when a mirror configuration occurs. The distance-based holonomic control is then transformed to cope with the non-holonomic constraints using a piecewise-smooth function. Simulation results, as well as hardware experiments with five m3pi robots demonstrate the applicability of our approach.

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.