A Dynamical System Approach for Resource-Constrained Mobile Robotics

The revolution of autonomous vehicles has led to the development of robots with abundant sensors, actuators with many degrees of freedom, high-performance computing capabilities, and high-speed communication devices. These robots use a large volume of information from sensors to solve diverse problems. However, this usually leads to a significant modeling burden as well as excessive cost and computational requirements. Furthermore, in some scenarios, sophisticated sensors may not work precisely, the real-time processing power of a robot may be inadequate, the communication among robots may be impeded by natural or adversarial conditions, or the actuation control in a robot may be insubstantial. In these cases, we have to rely on simple robots with limited sensing and actuation, minimal onboard processing, moderate communication, and insufficient memory capacity. This reality motivates us to model simple robots such as bouncing and underactuated robots making use of the dynamical system techniques. In this dissertation, we propose a four-pronged approach for solving tasks in resource-constrained scenarios: 1) Combinatorial filters for bouncing robot localization; 2) Bouncing robot navigation and coverage; 3) Stochastic multi-robot patrolling; and 4) Deployment and planning of underactuated aquatic robots. First, we present a global localization method for a bouncing robot equipped with only a clock and contact sensors. Space-efficient and finite automata-based combinatorial filters are synthesized to solve the localization task by determining the robot’s pose (position and orientation) in its environment. Second, we propose a solution for navigation and coverage tasks using single or multiple bouncing robots. The proposed solution finds a navigation plan for a single bouncing robot from the robot’s initial pose to its goal pose with limited sensing. Probabilistic paths from several policies of the robot are combined artfully so that the actual coverage distribution can become as close as possible to a target coverage distribution. A joint trajectory for multiple bouncing robots to visit all the locations of an environment is incrementally generated. Third, a scalable method is proposed to find stochastic strategies for multi-robot patrolling under an adversarial and communication-constrained environment. Then, we evaluate the vulnerability of our patrolling policies by finding the probability of capturing an adversary for a location in our proposed patrolling scenarios. Finally, a data-driven deployment and planning approach is presented for the underactuated aquatic robots called drifters that creates the generalized flow pattern of the water, develops a Markov-chain based motion model, and studies the long- term behavior of a marine environment from a flow point-of-view. In a broad summary, our dynamical system approach is a unique solution to typical robotic tasks and opens a new paradigm for the modeling of simple robotics system

Sea glider guidance around a circle using distance measurements to a drifting acoustic source

International audienceThis paper describes a simple yet robust sea glider guidance method in a constellation of Lagrangian drifters under the polar ice cap. The glider has to perform oceanographic measurements, mainly conductivity, temperature and depth, in the area enclosed by the drifters and can not rely on GNSS (Global Navigation Satellite System) positionning data as the polar ice cap makes it impossible to surface. The originality of the presented method resides in 2 points. First, a very simple PID (Proportional, Integral and Derivative) controller based on a basic kinematic model is tuned. Second, the method does not use a localization algorithm to estimate state space model data but interval analysis methods are performed to bound the errors in range to the transponder and its derivative. Moreover, only one acoustic beacon is used. Validation is then performed through simulations

Realization of Underwater Acoustic Networks.

This work contains a study of underwater acoustic networks. The concept of underwater acoustic networks has been presented with its benefits and drawbacks. An overview of the marine research areas oceanography, seismology, waterside security, marine pollution and marine biology has been made and a review of conventional methods and instrumentation committed. The research methods used today have been compared with the potential of underwater acoustic networks as a platform for maritime applications. Underwater acoustic networks were reviewed as feasible within all areas with some restrictions. The fact that respectable data rate is best achieved for nodes deployed in a high density grid give limitations on the coverage area. Battery as an energy source limits the life span of an underwater acoustic network and makes it best suited for missions for short term monitoring, if not a recharging technology is applied. The energy restrictions also put constraint on the amount of sensing done and the temporal solution in measurements. Underwater acoustic networks were found applicable for intrusion detection in waterside security to increase the range of current ultrasonic surveillance systems or realize distributed systems for passive diver detection. In oceanography and pollution monitoring current in situ sensors may enable underwater acoustic networks to do autonomous synoptic sampling of limited areas to measure a number of parameters, e.g. oxygen, turbidity, temperature and salinity. For seismic exploration this technology might save costs for permanent seismic installations in constant monitoring of producing oil fields. It might also aid marine biologists in habitat monitoring

Augmented Terrain-Based Navigation to Enable Persistent Autonomy for Underwater Vehicles in GPS-Denied Environments

Aquatic robots, such as Autonomous Underwater Vehicles (AUVs), play a major role in the study of ocean processes that require long-term sampling efforts and commonly perform navigation via dead-reckoning using an accelerometer, a magnetometer, a compass, an IMU and a depth sensor for feedback. However, these instruments are subjected to large drift, leading to unbounded uncertainty in location. Moreover, the spatio-temporal dynamics of the ocean environment, coupled with limited communication capabilities, make navigation and localization difficult, especially in coastal regions where the majority of interesting phenomena occur. To add to this, the interesting features are themselves spatio-temporally dynamic, and effective sampling requires a good understanding of vehicle localization relative to the sampled feature. Therefore, our work is motivated by the desire to enable intelligent data collection of complex dynamics and processes that occur in coastal ocean environments to further our understanding and prediction capabilities. The study originated from the need to localize and navigate aquatic robots in a GPS-denied environment and examine the role of the spatio-temporal dynamics of the ocean into the localization and navigation processes. The methods and techniques needed range from the data collection to the localization and navigation algorithms used on-board of the aquatic vehicles. The focus of this work is to develop algorithms for localization and navigation of AUVs in GPS-denied environments. We developed an Augmented terrain-based framework that incorporates physical science data, i.e., temperature, salinity, pH, etc., to enhance the topographic map that the vehicle uses to navigate. In this navigation scheme, the bathymetric data are combined with the physical science data to enrich the uniqueness of the underlying terrain map and increase the accuracy of underwater localization. Another technique developed in this work addresses the problem of tracking an underwater vehicle when the GPS signal suddenly becomes unavailable. The methods include the whitening of the data to reveal the true statistical distance between datapoints and also incorporates physical science data to enhance the topographic map. Simulations were performed at Lake Nighthorse, Colorado, USA, between April 25th and May 2nd 2018 and at Big Fisherman\u27s Cove, Santa Catalina Island, California, USA, on July 13th and July 14th 2016. Different missions were executed on different environments (snow, rain and the presence of plumes). Results showed that these two methodologies for localization and tracking work for reference maps that had been recorded within a week and the accuracy on the average error in localization can be compared to the errors found when using GPS if the time in which the observations were taken are the same period of the day (morning, afternoon or night). The whitening of the data had positive results when compared to localizing without whitening

Wireless Sensor Networks for Underwater Localization: A Survey

Autonomous Underwater Vehicles (AUVs) have widely deployed in marine investigation and ocean exploration in recent years. As the fundamental information, their position information is not only for data validity but also for many real-world applications. Therefore, it is critical for the AUV to have the underwater localization capability. This report is mainly devoted to outline the recent advance- ment of Wireless Sensor Networks (WSN) based underwater localization. Several classic architectures designed for Underwater Acoustic Sensor Network (UASN) are brie y introduced. Acoustic propa- gation and channel models are described and several ranging techniques are then explained. Many state-of-the-art underwater localization algorithms are introduced, followed by the outline of some existing underwater localization systems

Underwater Sensor Networks: Applications, Advances, and Challenges

This paper examines the main approaches and challenges in the design and implementation of underwater wireless sensor networks. We summarize key applications and the main phenomena related to acoustic propagation, and discuss how they affect the design and operation of communication systems and networking protocols at various layers. We also provide an overview of communications hardware, testbeds, and simulation tools available to the research community

Real-time measurement of wide-area near-surface ocean current

Of all of the physical parameters of the ocean realm, the speed and direction of the movement of ocean water, otherwise referred to as ocean “current,” is one of the most problematic to characterize. Currents influence the global climate, used for producing power, are crucial in determining the oil spill trajectories and ocean contaminant control, can either work against or with the movement of ships at sea and govern the movements of icebergs. Icebergs are a threat to offshore industries and marine transportations, particularly in places like the Northwest Atlantic, because of damages they can cause once they strike the oil platforms or ship hulls. They are steered by the near-surface current and not the surface current. Therefore, measurment of the real-time ocean currents at desired depths is valuable for the industries or researchers who are dealing with or studying the oceanographic data. Ocean current measurment methods that are currently being employed for ocean monitorings, are not able to measure the real-time current at certain desired depths over a larg area of the ocean. Thus, the existing current measurement methods need improvements. Limitations of the existing methods are as follows. Acoustic dopler current profilers (ADCP), are one of the most popular methods employed by most of the industries dealing with the oceanograghy. ADCPs are capable of measuring the current at any desired depth; however, their measurement method is of a point nature and they cannot measure an area averaged current data. Other techniques such as high frequency radio detecting and ranging systems (HF-RADAR) are also used to measure the surface currents (down to 15 m). These shore-based current meters with radio antenna, follow the same premise of the ADCP. In other words their measurement is dependant on the Doppler effect to determine the direction and velocity of the currents; however, they are capable of evaluating only the surface currents and not the near-surface currents (70-100 meter of depth is considered in this thesis as this is the depth oil structures are deployed in the Northwest Atlantic Ocean). Another group of instruments used for current measurement are floats and drifters which report their data to a centre device which is usually a satelite. The current data obtained with these instruments are fed into modeling systems, e.g. in (Chassignet, Hurlburt et al. 2006), for the ocean forcasting. The problems that exist with the available real-time current data from the satelite is that it is the very shallow current data (down to 15m that can be called surface). The data from other devices like floats is very sparse to include the horizontal information. Hence, Chassignet et al. use data assimilation of the past knowledge and ocean dynamics in order to predict the ocean features. Therefore, it is important to develop a method by which adequate data could be provided for the ocean prediction and modeling system. Thus, the focus of this thesis is on designing a method which is real-time and measures the near-surface current. On the other hand, energy suplies to the instruments in open water is limited as they work mainly rely on batteries and it is difficult to access the instruments in harsh condition to replace the batteries. Moreover, in cold regions the solar power is very limitted and thus using solar cells is not practical. Therefore, in order to measure the ocean current in real time, a novel method along with a sustainable architechture design is being proposed in this dissertation. The new method is based on transit time with the difference that in transit time method waves need to travel in both directions; up- and down-stream. But with a modification in the newly designed architecture; which is adding an extra node in the center of the network’s cells, sound waves need to travel on only one direction. This helps with saving a great amount of energy and covering a larger area in comparison with the networks which are developed using transit time method. Experimental results as well as simulations verify that the new proposed method is both efficient and practical