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Time-optimal model predictive control is important for achieving fast racing drones but is computationally intensive and thereby rarely used onboard small quadcopters with limited computational resources. In this work, we simplify the optimal control problem (OCP) of the position loop for several maneuvers by exploiting the fact that the solution resembles a so-called ‘bang-bang’ in the critical direction, where only the switching time needs to be found. The noncritical direction uses a ‘minimum effort’ approach. The control parameters are obtained through bisection search schemes on an analytical path prediction model. The approach is compared with a classical PID controller and theoretical time-optimal trajectories in simulations. We explain the effects of the OCP simplifications and introduce a method of mitigating one of these effects. Finally, we have implemented the ‘bang-bang’ controller as a model predictive controller (MPC) onboard a Parrot Bebop and performed indoor flights to compare the controller’s performance to a PID controller. We show that the light novel controller outperforms the PID controller in waypoint-to-waypoint flight while requiring only minimal knowledge of the quadcopter’s dynamics.

This paper proposes an integral approach for accurate ultra-wideband indoor position control of flapping-wing micro-air vehicles. Three aspects are considered to achieve a reliable and accurate position controller. The first aspect is a velocity/attitude flapping-wing model for drag compensation. The model is compared with real flight data and shown to be applicable for more than one type of flapping-wing drone. The second improvement regards a voltage-dependent thrust control. Lastly, a characterisation of ground effects in flapping-wing flight is obtained from hovering experiments. The proposed controller improves position control by a factor [Formula: see text], reaching a mean absolute error of 10[Formula: see text]cm for the position in [Formula: see text] and [Formula: see text], and 4.9[Formula: see text]cm for the position in [Formula: see text].

In the field of robotics, a major challenge is achieving high levels of autonomy with small vehicles that have limited mass and power budgets. The main motivation for designing such small vehicles is that compared to their larger counterparts, they have the potential to be safer, and hence be available and work together in large numbers. One of the key components in micro robotics is efficient software design to optimally utilize the computing power available. This paper describes the computer vision and control algorithms used to achieve autonomous flight with the [Formula: see text]30[Formula: see text]g tailless flapping wing robot, used to participate in the International Micro Air Vehicle Conference and Competition (IMAV 2018) indoor microair vehicle competition. Several tasks are discussed: line following, circular gate detection and fly through. The emphasis throughout this paper is on augmenting traditional techniques with the goal to make these methods work with limited computing power while obtaining robust behavior.

Autonomous navigation is a major challenge in the development of Micro Aerial Vehicles (MAVs). Especially, when an algorithm has to be efficient, insect intelligence can be a source of inspiration. One of the elementary navigation tasks of insects and robots is “homing”, which is the task of returning to an initial starting position. A promising approach uses learned visual familiarity of a route to determine reference headings during homing. In this paper, an existing biological proof-of-concept is transferred to an algorithm for micro drones, using vision-in-the-loop experiments in indoor environments. An artificial neural network determines which control actions to take place.

This paper proposes a trajectory planning and control strategy to optimally visit a given set of waypoints in the presence of wind. First, aerodynamic properties of quadrotors which affect trajectory planning and tracking performance are investigated. Blade flapping, induced and parasitic drag are derived and an extended method to identify all coefficients from flight test data is developed. Then, a three-step approach is suggested to optimize the trajectory. These steps reduce the size of the optimization problem and thereby increase computational efficiency while still guaranteeing near optimal results. The trajectories are optimized for minimal aerodynamic drag and minimal jerk. The derived smooth trajectory generation is compared with traditional trajectory planning consisting of discrete point to point tracking followed by low-pass filtering. The new trajectories yield a clear reduction in maximal needed thrust and in Euler angle aggressiveness. A thrust vectoring controller is designed, which exploits the a priori trajectory information and identified aerodynamic properties. Its performance is compared to a standard PID controller and results show a reduction in tracking delay and an increase in thrust and attitude angle margins, which overall enable faster flight.Read More: http://www.worldscientific.com/doi/abs/10.1142/S2301385018500024?src=recsys