Felix Stephan Schill

Robust acoustic source localization of emergency signals from Micro Air Vehicles

In search and rescue missions, Micro Air Vehicles (MAVs) 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.

Representation of vehicle dynamics in haptic teleoperation of aerial robots

This paper considers the question of providing effective feedback of vehicle dynamic forces to a pilot in haptic teleoperation of aerial robots. We claim that the usual state-of-the-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 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 the 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.

Audio-based Relative Positioning System for Multiple Micro Air Vehicle Systems

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 individuals 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.

Audio-based Localization for Swarms of Micro Air Vehicles

Localization is one of the key challenges that needs to be considered beforehand to design truly autonomous MAV teams. In this paper, we present a cooperative method to address the localization problem for a team of MAVs, where individuals obtain their position through perceiving a sound-emitting beacon MAV that is flying relative to a reference point in the environment. For this purpose, an on-board audio-based localization system is proposed that allows individuals to measure the relative bearing to the beacon robot and furthermore to localize themselves and the beacon robot simultaneously, without the need for a communication network. Our method is based on coherence testing among signals of a small on-board microphone array, to obtain the relative bearing measurements, and an estimator, to fuse these measurements with sensory information about the motion of the robot throughout time, to estimate robustly the MAV positions. The proposed method is evaluated both in simulation and in real world experiments.

Comparative Study of Haptic Interfaces for Bilateral Teleoperation of VTOL Aerial Robots

Force feedback is a powerful sensory cue that can provide a user, remotely teleoperating a vertical takeoff and landing (VTOL) aerial robotic vehicle, with an enhanced perception of the robot motion and interaction with the local environment. The accepted framework for mobile vehicle teleoperation, inspired by decades of research into manipulator teleoperation, maps joystick displacements to vehicle velocity set points and reflects interactive forces from the vehicle to the human operator. This paper proposes a novel admittance configured haptic interface where the force input from the user is used as a velocity set point and the joystick position is servo-controlled to reflect the vehicle velocity. This paper provides a comparative study, both analytic and experimental, of the two interfaces to investigate the performance and perceptual differences in the context of teleoperation of a VTOL aerial robot.

On-Board Relative Bearing Estimation for Teams of Drones Using Sound

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.

Automated Real-Time Control of Fluidic Self-Assembly of Microparticles

Self-assembly is a key coordination mechanism for large multi-unit systems and a powerful bottom-up technology for micro/nanofabrication. Controlled self-assembly and dynamic reconfiguration of large ensembles of microscopic particles can effectively bridge these domains to build innovative systems. In this perspective, we present SelfSys, a novel platform for the automated control of the fluidic self-assembly of microparticles. SelfSys centers around a water-filled microfluidic chamber whose agitation modes, induced by a coupled ultrasonic actuator, drive the assembly. Microparticle dynamics is imaged, tracked and analyzed in real-time by an integrated software framework, which in turn algorithmically controls the agitation modes of the microchamber. The closed control loop is fully automated and can direct the stochastic assembly of microparticle clusters of preset dimension. Control issues specific to SelfSys implementation are discussed, and its potential applications presented. The SelfSys platform embodies at microscale the automated self-assembly control paradigm we first demonstrated in an earlier platform.

Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control

Stochastic self-assembly provides promising means for building micro-/nano-structures with a variety of properties and functionalities. Numerous studies have been conducted on the control and modeling of the process in engineered self-assembling systems constituted of modules with varied capabilities ranging from completely reactive nano-/micro-particles to intelligent miniaturized robots. Depending on the capabilities of the constituting modules, different approaches have been utilized for controlling and modeling these systems. In the quest of a unifying control and modeling framework and within the broader perspective of investigating how stochastic control strategies can be adapted from the centimeter-scale down to the (sub-)millimeter-scale, as well as from mechatronic to MEMS-based technology, this work presents the outcomes of our research on self-assembly during the past few years. As the first step, we leverage an experimental platform to study self-assembly of water-floating passive modules at the centimeter scale. A dedicated computational framework is developed for real-time tracking, modeling and control of the formation of specific structures. Using a similar approach, we then demonstrate controlled self-assembly of microparticles into clusters of a preset dimension in a microfluidic chamber, where the control loop is closed again through real-time tracking customized for a much faster system dynamics. Finally, with the aim of distributing the intelligence and realizing programmable self-assembly, we present a novel experimental system for fluid-mediated programmable stochastic self-assembly of active modules at the centimeter scale. The system is built around the water-floating 3-cm-sized Lily robots specifically designed to be operative in large swarms and allows for exploring the whole range of fully-centralized to fully-distributed control strategies. The outcomes of our research efforts extend the state-of-the-art methodologies for designing, modeling and controlling massively-distributed, stochastic self-assembling systems at different length scales, constituted of modules from centimetric down to sub-millimetric size. As a result, our work provides a solid milestone in structure formation through controlled self-assembly.

Localization of emergency acoustic sources by micro aerial vehicles

For micro aerial vehicles (MAVs) involved in search and rescue missions, the ability to locate the source of a distress sound signal is significantly important and allows fast localization of victims and rescuers during nighttime, through foliage and in dust, fog, and smoke. Most emergency sound sources, such as safety whistles and personal alarms, generate a narrowband signal that is difficult to localize by human listeners or with the common localization methods suitable for broadband sounds. In this paper, we present three methods for MAV-based emergency sound localization system. The first method involves designing a new emergency source for immediate localization by the MAV using a common localization method. The other two novel methods allow localizing the currently available emergency sources, or other narrowband sounds in general, that are difficult to localize due to the periodicity in the sequence of sound samples. The second method exploits the Doppler shift in the sound frequency, caused due to the motion of the MAV and the dynamics of the MAV to assist with the localization. The third method involves active control of the robots attitude and fusing acoustic and attitude measurements for achieving accurate and robust estimates. We evaluate our methods in real-world experiments with real flying robots.

A scalable electro-magnetic communication system for underwater swarms

Scalable, reliable underwater communication is still a major challenge limiting the deployment of large-scale cooperative underwater systems, such as swarms of AUVs, which generally rely on frequent updates from surrounding units for control and mission objectives. Communication bandwidth is extremely limited in the underwater domain, hence it is crucial to efficiently manage and distribute this scarce resource between nodes. This paper discusses a symmetric, distributed time-slotted channel access algorithm for large-scale, dynamic ad-hoc networks. In contrast to traditional network algorithms which are mostly designed for sporadic point-to-point communication, this algorithm specifically optimises fast, continuous information distribution, locally and globally, while achieving close to 100% channel utilisation. A theoretical analysis of the performance is presented, along with real-time simulation results for dozens of nodes performing rapid reconfigurations. The impact of swarm dynamics on network performance is analysed in detail.