Towards a Fully Autonomous Indoor Helicopter

Despite the significant progress in micro and information technologies and all the interest of the scientific community in the Micro Aerial Vehicles (MAV), fully autonomous micro-helicopters of the size of a small bird are still not available. The Mesicopter group at Stanford University [1] studied the feasibility of a centimeter scale quadrotor. The group of Prof. Nonami at Chiba University [2] achieved a 13g semiautonomous coaxial helicopter which is able to fly for three minutes. Unfortunately, none of these developments combine reasonable endurance and autonomous navigation in narrow environments. The European project muFly [3] was born in this context; it targets the development and implementation of a fully autonomous micro-helicopter, with a maximum span of 20 cm and mass of 50 g. The consortium is composed of six partners; each one will provide a subsystem of the entire helicopter. One of the objectives of muFly project is to introduce low processing-power localization algorithms for micro helicopters. However, compact and lightweight indoorlocalization sensors do not exist yet (unlike GPS for outdoor). Thus, CSEM research center (Switzerland) who is involved in muFly is presently designing a miniature omni-directional camera [4], to be coupled with a laser source and used as a 360 triangulation-based range finder. This sensor and the muFly platform will be available for testing in a couple of months. Until then, the algorithms have to be developed and validated with another sensor and on a different flying platform. The main scope of application for the helicopter are indoor environments which raises constraints which are not present when flying outdoors. Due to the absence of GPS information, the robot has to rely on other on-board sensors. Furthermore, the accuracy of the positioning system is an essential requirement for indoor operations, which is characterized by a limited safety margin (i.e. robot crossing a doorway). In the last decade, navigation systems for autonomous flying vehicles have received an increasing attention by the research community. Ng and colleagues [5] developed effective control algorithms for outdoor helicopters using reinforcement learning techniques. Haehnel et al. [6] proposed a 3D mapping technique for outdoor environments. For indoor navigation, Tournier et al. [7] used monocular vision in order to estimate and control the current pose of a quadrotor. Roberts et al. [8] utilized ultrasound sensors for control a quadrotor in an indoor environment. Recently, He et al. [9] described planning in the information space for