In search and rescue activities, unmanned aerial vehicles (UAV) should exploit sound information to compensate for poor visual information. This paper describes the design and implementation of a UAV-embedded microphone array system for sound source localization in outdoor environments. Four critical development problems included water-resistance of the microphone array, efficiency in assembling, reliability of wireless communication, and sufficiency of visualization tools for operators. To solve these problems, we developed a spherical microphone array system (SMAS) consisting of a microphone array, a stable wireless network communication system, and intuitive visualization tools. The performance of SMAS was evaluated with simulated data and a demonstration in the field. Results confirmed that the SMAS provides highly accurate localization, water resistance, prompt assembly, stable wireless communication, and intuitive information for observers and operators.

This paper proposes an autopilot system for a small and light unmanned air vehicle called Kiteplane. The Kiteplane has a large delta-shaped main wing that is easily disturbed by the wind, which was minimized by utilizing trim flight with drift. The proposed control system for autonomous trajectory following with a wind disturbance included fuzzy logic controllers, a speed controller, a wind disturbance attenuation block, and low-level feedback controllers. The system was implemented onboard the aircraft. Experiments were performed to test the performance of the proposed system and the Kiteplane nearly succeeded in following the desired trajectory, under the wind disturbance. Although the path was not followed perfectly, the airplane was able to traverse the waypoints by utilizing a failsafe waypoint updating rule

In this paper, an algorithm to estimate wind direction by using a small and light Unmanned Air Vehicle(UAV) called KITEPLANE was proposed. KITEPLANE had a big main wing which is a kite-like delta shape and, therefore, it was easy to be disturbed by wind. However, this disadvantage implies that the KITEPLANE has an ability to sense wind and that it is expected to use the KITEPLANE as a sensor for wind estimation. In order to achieve this feature, dynamics of the KITEPLANE under wind disturbance were derived and a numerical estimation method was proposed. Devices equipped on board were also developed and the proposed method was implemented. Results of an experiment showed the effectiveness of the proposed method.

Drone audition, or auditory processing for drones equipped with a microphone array, is expected to compensate for problems affecting drones' visual processing, in particular occlusion and poor-illumination conditions. The current state of drone audition still assumes a single sound source. When a drone hears sounds originating from multiple sound sources, its sound-source localization function determines their directions. If two sources are very close to each other, the localization function cannot determine whether they are crossing or approaching-then-departing. This ambiguity in tracking multiple sound sources is resolved by data association. Typical methods of data association use each label of the separated sounds, but are prone to errors due to identification failures. Instead of labeling by classification, this study uses a set of classification measures determined by support vector machines (SVM) to avoid labeling failures and deal with unknown signals. The effectiveness of the proposed approach is validated through simulations and experiments conducted in the field.

Drone audition, drone's auditory capabilities for a multi-rotor helicopter (hereinafter, drone), has been developed to improve real-world tasks, e.g. search-and-rescue tasks, by compensating for the weakness of visual capabilities due to darkness and occlusion. Most of current implementations of robot audition focus on a single sound source. This paper focuses on the estimation of multiple sound source positions from acoustic signals captured by a drone equipped with a microphone array. Due to ego-noise such as rotor and airflow noise around the drone, the estimation of the sound source position is obscured and prone to error. In particular, in case of multiple sound sources, data association between localization information and sound sources is critical to the performance of such estimation. To cope with uncertainty in data association, we extend Global Nearest Neighbor (GNN) to exploit sound source features (GNN-c) because drone audition needs real-time or least latency. The resulting system demonstrates that it can estimate multiple sound source positions with an accuracy of about 3 m.