Abstract-In search and rescue missions, Micro Air Vehicles (MAV's) can assist rescuers to faster locate victims inside a large search area and to coordinate their efforts. Acoustic signals play an important role in outdoor rescue operations. Emergency whistles, as found on most aircraft life vests, are commonly carried by people engaging in outdoor activities, and are also used by rescue teams, as they allow to signal reliably over long distances and far beyond visibility. For a MAV involved in such missions, the ability to locate the source of a distress sound signal, such as an emergency whistle blown by a person in need of help, is therefore significantly important and would allow the localization of victims and rescuers during night time, through foliage and in adverse conditions such as dust, fog and smoke. In this paper we present a sound source localization system for a MAV to locate narrowband sound sources on the ground, such as the sound of a whistle or personal alarm siren. We propose a method based on a particle filter to combine information from the cross correlation between signals of four spatially separated microphones mounted on the MAV, the dynamics of the aerial platform, and the doppler shift in frequency of the sound due to the motion of the MAV. Furthermore, we evaluate our proposed method in a real world experiment where a flying micro air vehicle is used to locate and track the position of a narrowband sound source on the ground.

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Abstract-In a team of autonomous drones, individual knowledge about the relative location of teammates is essential. Existing relative positioning solutions for teams of small drones mostly rely on external systems such as motion tracking cameras or GPS satellites that might not always be accessible. In this letter, we describe an onboard solution to measure the 3-D relative direction between drones using sound as the main source of information. First, we describe a method to measure the directions of other robots from perceiving their engine sounds in the absence of self-engine noise. We then extend the method to use active acoustic signaling to obtain the relative directions in the presence of self-engine noise, to increase the detection range, and to discriminate the identity of robots. Methods are evaluated in real world experiments and a fully autonomous leader-following behavior is illustrated with two drones using the proposed system.

Abstract-This paper considers the question providing effective feedback of vehicle dynamic forces to a pilot in haptic teleoperation of aerial robots. We claim that the usual state-ofthe-art haptic interface, based on research motivated by robotic manipulator slaves and virtual haptic environments, does a poor job of reflecting dynamic forces of a mobile robotic vehicle to the user. This leads us to propose a novel new force feedback user interface for mobile robotic vehicles with dynamics. An analysis of the closed-loop force-displacement transfer functions experienced by the master joystick for the classical and the new approach clearly indicate the advantages of proposed formulation. Both the classical and the proposed approach have been implemented in the teleoperation of a quadrotor vehicle and we present quantitative and cognitive performance data from a user study that corroborates the expected performance advantages.

Abstract-Employing a group of independently controlled flying micro air vehicles (MAVs) for aerial coverage missions, instead of a single flying robot, increases the robustness and efficiency of the missions. Designing a group of MAVs requires addressing new challenges, such as inter-robot collision avoidance and formation control, where individual's knowledge about the relative location of their local group members is essential. A relative positioning system for a MAV needs to satisfy severe constraints in terms of size, weight, processing power, power consumption, three-dimensional coverage and price. In this paper we present an on-board audio based system that is capable of providing individuals with relative positioning information of their neighbouring sound emitting MAVs. We propose a method based on coherence testing among signals of a small onboard microphone array to obtain relative bearing measurements, and a particle filter estimator to fuse these measurements with information about the motion of robots throughout time to obtain the desired relative location estimates. A method based on fractional Fourier transform (FrFT) is used to identify and extract sounds of simultaneous chirping robots in the neighbourhood. Furthermore, we evaluate our proposed method in a real world experiment with three simultaneously flying micro air vehicles.