Water as a medium poses a number of challenges for robots, limiting the progress of research in underwater robotics vis-á-vis ground or aerial robotics. The primary challenges are satellite based positioning and radio communication being unusable due to high attenuation of electromagnetic waves in water. We have developed miniature, agile, easy to carry and deploy Autonomous Underwater Vehicles (AUVs) equipped with a suite of sensors for underwater environmental sensing. We previously demonstrated adaptive sampling and feature tracking, and gathered data from a lake for limnological research, with the AUV performing inertial navigation. In this paper, we demonstrate a new underwater acoustic positioning system, which allows on-board estimation of AUV position. Our system uses absolute time information from GNSS for initial clock synchronization and uses one-way-travel-time for range measurements, which makes it scalable in the number of robots. It is easily deployable and does not rely on any installed infrastructure in the environment. We describe various hardware and software components of our system, and present results from experiments in Lake Geneva.

Applications of robots for gathering data in underwater environments has been limited due to the challenges posed by the medium. We have developed a miniature, agile, easy to carry and deploy Autonomous Underwater Vehicle (AUV) equipped with a suite of sensors for underwater environmental sensing. We have also developed a compact high resolution fast temperature sensing module for the AUV for microstructure and turbulence measurements in water bodies. In this paper, we describe a number of algorithms and subsystems of the AUV that enable autonomous real-world operation, and present the data gathered in an experimental campaign in collaboration with limnologists. We demonstrate adaptive sampling missions where the AUV could autonomously locate a zone of interest and adapt its trajectory to stay in it. Further, it could execute specific behaviors to accommodate special sensing requirements necessary to enhance the quality of the data collected. In these missions, the AUV could autonomously trace a feature and capture horizontal variation in various quantities, including turbidity and temperature fluctuations, allowing limnologists to study lake phenomena in an additional dimension.

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.

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.

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.

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.

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.

Micro aerial vehicles (MAVs) are a rapidly growing area of research and development in robotics. For autonomous robot operations, localization has typically been calculated using GPS, external camera arrays, or onboard range or vision sensing. In cluttered indoor or outdoor environments, onboard sensing is the only viable option. In this paper we present an appearance-based approach to visual SLAM on a flying MAV using only low quality vision. Our approach consists of a visual place recognition algorithm that operates on 1000 pixel images, a lightweight visual odometry algorithm, and a visual expectation algorithm that improves the recall of place sequences and the precision with which they are recalled as the robot flies along a similar path. Using data gathered from outdoor datasets, we show that the system is able to perform visual recognition with low quality, intermittent visual sensory data. By combining the visual algorithms with the RatSLAM system, we also demonstrate how the algorithms enable successful SLAM.

This paper proposes the use of optical flow from a moving robot to provide force feedback to an operator's joystick to facilitate collision free teleoperation. Optic flow is measured by wide angle cameras on board the vehicle and used to generate a virtual environmental force that is reflected to the user through the joystick, as well as feeding back into the control of the vehicle. The coupling between optical flow (velocity) and force is modelled as an impedance - in this case an optical impedance. We show that the proposed control is dissipative and prevents the vehicle colliding with the environment as well as providing the operator with a natural feel for the remote environment. The paper focuses on applications to aerial robotics vehicles, however, the ideas apply directly to other force actuated vehicles such as submersibles or space vehicles, and the authors believe the approach has potential for control of terrestrial vehicles and even teleoperation of manipulators. Experimental results are provided for a simulated aerial robot in a virtual environment controlled by a haptic joystick.

Localisation and mapping relies on the representation and recognition of features or patterns detected in sensor data. An important aspect is the temporal relationship of observations in sensor data streams. This article proposes a new approach for simultaneous localisation and mapping based on temporal relations in the flow of characteristic events in the sensor data channels. A dynamical system is employed to acquire these correlations between simultaneous and sequential events from different sources, to map causal sequences, while considering time spans, and to recognise previously observed patterns (localisation). While this system is applicable to sensor modalities with different characteristics and timing behaviours, it is especially suitable for distributed computing. Mapping and localisation take place simultaneously in an life-long unsupervised distributed online learning process. The dynamical system was implemented as a distributed real-time system with symmetric processes. A real-time clustering network reduces the dimension of raw sensor data. Cluster transitions are used as input for the dynamical mapping system. Results from physical experiments with one sensor modality are presented.