Marek Doniec

AquaOptical: A lightweight device for high-rate long-range underwater point-to-point communication

This paper describes AquaOptical, an underwater optical communication system. Three optical modems have been developed: a long range system, a short range system, and a hybrid. We describe their hardware and software architectures and highlight trade-offs. We present pool and ocean experiments with each system. In clear water AquaOptical was tested to achieve a data rate of 1.2Mbit/sec at distances up to 30m. The system was not tested beyond 30m. In water with visibility estimated at 3m AquaOptical achieved communication at data rates of 0.6Mbit/sec at distances up to 9m.

Characterization of the indoor magnetic field for applications in Localization and Mapping

To improve our understanding of the indoor properties of the perturbed Earths magnetic field, we have developed a methodology to obtain dense and spatially referenced samples of the magnetic vector field on the grounds surface and in the free space above. This methodology draws on the use of various tracking techniques (photometric, odometric, and motion capture) to accurately determine the pose of the magnetic sensor, which can be positioned manually by humans or autonomously by robots to acquire densely gridded sample datasets. We show that the indoor magnetic field exhibits a fine-grained and persistent micro-structure of perturbations in terms of its direction and intensity. Instead of being a hindrance to indoor navigation, we believe that the variations of the three vector components are sufficiently expressive to form re-recognizable features based on which accurate localization is possible. We provide experimental results using our methodology to map the magnetic field on the grounds surface in our indoor research facilities. With the use of a magnetometer and very little computation, these resulting maps can serve to compensate the perturbations and subsequently determine pose of a human or robot in dead reckoning applications.

Magnetic maps of indoor environments for precise localization of legged and non-legged locomotion

The magnetic field in indoor environments is rich in features and exceptionally easy to sense. In conjunction with a suitable form of odometry, such as signals produced from inertial sensors or wheel encoders, a map of this field can be used to precisely localize a human or robot in an indoor environment. We show how the use of this field yields significant improvements in terms of localization accuracy for both legged and non-legged locomotion. We suggest various likelihood functions for sequential Monte Carlo localization and evaluate their performance based on magnetic maps of different resolutions. Specifically, we investigate the influence that measurement representation (e.g., intensity-based, vector-based) and map resolution have on localization accuracy, robustness, and complexity. Compared to other localization approaches (e.g., camera-based, LIDAR-based), there exist far fever privacy concerns when sensing the indoor environments magnetic field. Furthermore, the required sensors are less costly, compact, and have a lower raw data rate and power consumption. The combination of technical and privacy-related advantages makes the use of the magnetic field a very viable solution to indoor navigation for both humans and robots.

Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments

We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.

Autonomous Depth Adjustment for Underwater Sensor Networks: Design and Applications

To fully understand the ocean environment requires sensing the full water column. Utilizing a depth adjustment system on an underwater sensor network provides this while also improving global sensing and communications. This paper presents a depth adjustment system for waters up to 50 m deep that connects to the aquanode sensor network nodes. We performed experiments characterizing and demonstrating the functionality of the depth adjustment system. We discuss the application of this device in improving acoustic communication and also verify the functionality of a decentralized depth adjustment algorithm that optimizes the placement of the nodes for collecting sensing data.

AMOUR V: A Hovering Energy Efficient Underwater Robot Capable of Dynamic Payloads

In this paper we describe the design and control algorithms of AMOUR, a low-cost autonomous underwater vehicle (AUV) capable of missions of marine survey and monitoring. AMOUR is a highly maneuverable robot capable of hovering and carrying dynamic payloads during a single mission. The robot can carry a variety of payloads. It uses internal buoyancy and balance control mechanisms to achieve power efficient motions regardless of the payload size. AMOUR is designed to operate in synergy with a wireless underwater sensor network (WUSN) of static nodes. The robot’s payload was designed in order to deploy, relocate and recover the static sensor nodes. It communicates with the network acoustically for signaling and localization and optically for data muling. We present control algorithms, navigation algorithms, and experimental data from pool and ocean trials with AMOUR that demonstrate its basic navigation capabilitrials with AMOUR that demonstrate its basic navigation capabilities, power efficiency, and ability to carry dynamic payloads.

An End-to-End Signal Strength Model for Underwater Optical Communications

In this paper, we present a generic model of signal strength in underwater optical communications. The model includes light sources, detectors, amplifier and detector circuitry, optics, as well as a simple extinction model of the water channel. The end-to-end model provides insights into optimization approaches for underwater optical modems and enables relative pose estimation between underwater optical transmitters and receivers. We instantiate our model to the AquaOptical model by determining its parameters and verifying the model prediction in a suite of pool experiments.

Using optical communication for remote underwater robot operation

Underwater vehicles are typically operated using a tether or a slow acoustic link. We present an underwater optical communication system that enables a high-throughput and low-latency link to an underwater robot. The optical link allows the robot to operate in cluttered environments without the need for a tether. We demonstrate the performance of the system in a number of experiments which characterize the optical link and demonstrate remote control of the robot using a human input device.

Adaptive decentralized control of underwater sensor networks for modeling underwater phenomena

Understanding the dynamics of bodies of water and their impact on the global environment requires sensing information over the full volume of water. We develop a gradient-based decentralized controller that dynamically adjusts the depth of a network of underwater sensors to optimize sensing for computing maximally detailed volumetric models. We prove that the controller converges to a local minimum. We implement the controller on an underwater sensor network capable of adjusting their depths. Through simulations and experiments, we verify the functionality and performance of the system and algorithm.

Autonomous depth adjustment for underwater sensor networks

To fully understand the ocean environment requires sensing the full water column. Utilizing a depth adjustment system on an underwater sensor network provides this while also improving global sensing and communications. This paper presents a depth adjustment system for waters of up to 50m deep that connects to the AquaNode sensor network. We performed experiments characterizing the system and demonstrating its functionality. We discuss the application of this device in improving acoustic communication.