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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 $\sim$30g 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.

In recent times, research in the area of flapping flight has attracted renewed interest with an endeavor to use this mechanism in Micro Air vehicles (MAVs). For a sustained and high-endurance flight, having larger payload carrying capacity we need to identify a simple and efficient flapping-kinematics. In this paper, we have used flow visualizations and Discrete Vortex Method (DVM) based simulations for the study of flapping flight. Our results highlight that simple flapping kinematics with down-stroke period (t_D) shorter than the upstroke period (t_U) would produce a sustained lift. We have identified optimal asymmetry ratio (Ar = t_D/t_U), for which flapping-wings will produce maximum lift and find that introducing optimal wing flexibility will further enhances the lift.

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.