This paper presents an autonomous robot system that is designed to autonomously search for and geo-localize potential underwater archaeological sites. The system, based on Autonomous Underwater Vehicles, invokes a multi-step pipeline. First, the AUV constructs a high altitude scan over a large area to collect low-resolution side scan sonar data. Second, image processing software is employed to automatically detect and identify potential sites of interest. Third, a ranking algorithm assigns importance scores to each site. Fourth, an AUV path planner is used to plan a time-limited path that visits sites with a high importance at a low altitude to acquire high-resolution sonar data. Last, the AUV is deployed to follow this path. This system was implemented and evaluated during an archaeological survey located along the coast of Malta. These experiments demonstrated that the system is able to identify valuable archaeological sites accurately and efficiently in a large previously unsurveyed area. Also, the planned missions led to the discovery of a historical plane wreck whose location was previously unknown.

This paper presents an underwater robotic sensor system for localizing acoustic transmitters when the robot's hydrophones cannot be time-synchronized. The development of the system is motivated by applications where tracking of marine animals that are tagged with an underwater acoustic transmitter is required. The system uses two novel real-time calibration algorithms that improve the accuracy of time of flight (TOF) and time difference of arrival (TDOA) measurements. The first algorithm corrects non-linear clock skews in TOF measurements based on temperature variation. The second algorithm compensates the localized relative clock skew between clocks using a mixed integer linear program. To validate the system's performance, an Autonomous Underwater Vehicle (AUV) was deployed to track a moving tag where GPS data was used as ground truth. Compared to traditional TOF and TDOA filtering methods, the results show that the proposed system can achieve reduction of mean localization errors by 59%, and a reduction of the standard deviation of measurements by 44%.

This paper presents a method for constructing 3D maps of marine archaeological sites using deployments of Autonomous Underwater Vehicles (AUV) equipped with sonar and cameras. The method requires multiple AUV missions in which the first mission directs the AUV to conduct a high altitude lawnmower scan over the area to create a course bathymetry map using sonar. Subsequent AUV missions then direct the AUV to make low altitude fly-overs just above the wreck with the goal of obtaining camera images from multiple viewpoints of the wreck to enable offboard 3D mapping via photogrammetric reconstruction. This approach uses a coarse map generated after the first mission to construct AUV paths that attempt to maximize information gain, i.e. maximize the number of viewpoints of the wreck within a time limit. Presented is a motion planner derived from Rapidly-Exploring Random Trees (RRT) that have sampling strategies modified for this problem. Specifically, the random node selection and new node generation are designed to consider the kinematics of an AUV and the information gain associated with each flyover. Simulation results demonstrate improvements of up to 152% when these sampling strategies are used. Experiment results, involving deployments for mapping two known wrecks located along the coast of Malta, validate the system's ability to construct 3D maps and associated visualizations.

This paper presents a graph-based model of periodic migrations of tagged fish populations and two multi-AUV stochastic controllers developed to track these fish from the model. The model presented in this paper characterizes patterns in the historical movement of tagged fish and is used to develop stochastic tracking by a “model based control” and a “feedback control”. These two controllers permit swarms of AUVs to track the transition probabilities of the tagged population between vertices of the model. To validate these controllers, a periodic model is developed for a simulated population based on three months of geolocation data from a kelp bass (Paralabrax clathratus), and AUV teams utilizing both controllers are simulated in tracking this population. Results show the viability of stochastic controls for multi-AUV tracking of populations whose behavior is well-approximated by the graph-based model. Preliminary trials with physical AUV systems indicate the plausibility of hardware implementation.

This paper presents a multi-AUV state-estimator that can determine the 3D position of a tagged fish. In addition to angle measurements, the state-estimator also incorporates distance and depth measurements. These additional sensor measurements allow for greater accuracy in the position estimates. A newly developed motion model that better accounts for multiple hypotheses of the motion of a tagged fish is used to increase the robustness of the state-estimator. A series of multi-AUV shark tracks were conducted at Santa Catalina Island, California over the span of four days to demonstrate the ability of the state-estimator to determine the 3D position of a tagged leopard shark. Additional experiments in which the AUVs tracked a tagged boat of known location were conducted to quantify the performance of the presented state-estimator. Experimental results demonstrate a three-fold decrease in mean state-estimation error compared to previous works.

This paper presents a system of multiple coordinating autonomous underwater vehicles (AUV) that can localize and track a shark tagged with an acoustic transmitter. Each AUV is equipped with a stereo-hydrophone system that provides measurements of the relative bearing to the transmitter, as well as an acoustic modem that allows for inter-AUV communication and hence cooperative shark state estimation and decentralized tracking control. Online state estimation of the shark's state is performed using a Particle Filter in which measurements are shared between AUVs. The decentralized control system enables the AUVs to circumnavigate a dynamic target, (i.e. the estimated shark location). Each AUV circles the target by tracking circles of different radii and at different phase angles with respect to the target so as to obtain simultaneous sensor vantage points and minimize chance of AUV collision. A series of experiments using two AUVs were conducted in Big Fisherman's Cove in Santa Catalina Island, CA and demonstrated the ability to track a tagged leopard shark (Triakis semifasciata). The performance of the tracking was compared to standard manual tracking performed using an directional hydrophone operated by a researcher in a boat. In an additional experiment, the AUVs tracked an acoustic tag attached to the tracking boat to quantify the error of the state estimation of the system.

This paper discusses the use of a cooperative multiple robot system as applied to distributed tracking and sampling of a boundary edge. Within this system the boundary edge is partitioned into subsegments, each allocated to a particular robot such that workload is balanced across the robots. Also, to minimize the time between sampling local areas of the boundary edge, it may be desirable to minimize the difference between each robots progression (i.e. phase) along its allocated sub segment of the edge. The paper introduces a new distributed controller that handles both workload and phase balancing. Simulation results are used to illustrate the effectiveness of the controller in an Autonomous Underwater Vehicle (AUV) under ice edge sampling application. Successful results from experimentation with three iRobot Creates are also presented.

Typical multi-robot systems consist of robots cooperating to maximize global fitness functions. However, in some scenarios, the set of interacting robots may not share common goals and thus the concept of a global fitness function becomes invalid. This work examines multi-robot communities (MRC), in which individual robots have independent goals. Within the MRC context, we present a task allocation architecture that optimizes individual robot fitness functions over long time horizons using reciprocal altruism. Previous work has shown that reciprocating altruistic relationships can evolve between two willing robots, using market-based task auctions, while still protecting against selfish robots aiming to exploit altruism. As these relationships grow, robots are increasingly likely to perform tasks for one another without any reward or promise of payback. This work furthers this notion by considering cases where an imbalance exists in the altruistic relationship. The imbalance occurs when one robot can perform another robot's task, thereby exhibiting altruism, but the other robot cannot reciprocate since it is physically unable (e.g. lack of adequate sensors or actuators). A new altruistic controller to deal with such imbalances is presented. The controller permits a robot to build altruistic relationships with the community as a whole (one-to-many), instead of just with single robots (one-to-one). The controller is proven stable and guarantees altruistic relationships will grow, if robots are willing, while still minimizing the effects of selfish robots. Results indicate that the one-to-many controller performs comparable to the one-to-one on most problems, but excels in the case of an unbalanced altruistic relationship.

This paper documents the application of several underwater robot mapping and localization techniques used during an archaeological expedition. The goal of this project was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. They often consisted of several connected chambers, still containing water. A sonar-equipped Remotely Operated Vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Four different mapping and localization techniques were employed including 1) Sonar image mosaics using stationary sonar scans, and 2) Simultaneous Localization and Mapping (SLAM) while the vehicle was in motion, 3) SLAM using stationary sonar scans, and 4) Localization using previously created maps. Two dimensional maps of 6 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C.

As a natural consequence of steady increase of average population age in developed countries, Alzheimer's disease is becoming an increasingly important public health concern. The financial and emotional toll of the disease is exacerbated with lack of standard diagnostic procedures available at the community clinics and hospitals, where most patients are evaluated. In our recent preliminary results, we have reported that the event related potentials (ERPs) of the electroencephalogram can be used to train an ensemble-based classifier for automated diagnosis of Alzheimer's disease. In this study, we present an updated alternative approach by combining complementary information provided by ERPs obtained from several parietal region electrodes. The results indicate that ERPs obtained from parietal region of the cortex carry substantial complementary diagnostic information. Specifically, the diagnostic ability of such an approach is substantially better, compared to the performance obtained by using data from any of the individual electrodes alone. Furthermore, the diagnostic performance of the proposed approach compares very favorably to that obtained at community clinics and hospitals.

With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates

As the population of our elderly suffering from Alzheimer's disease increases rapidly, the need for an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to local community clinics becomes an increasingly critical public health concern. We propose multiresolution analysis of the electroencephalogram (EEG) followed by an ensemble based classification designed to fuse data from different EEG channels. Several classifier combination rules, including competence based weighted combination have been implemented to evaluate their data fusion performance, with particular emphasis on diagnosing the disease at its earliest stages. Diagnostic performance of the proposed approach has been very promising.

The diagnosis of Alzheimer's disease at an early stage is a major concern due to the growing number of the elderly population affected, as well as the lack of a standard and effective diagnosis procedure available to community healthcare providers. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a non-invasive biomarker for Alzheimer's disease and had varying degrees of success. These studies have traditionally used automated classifiers such as neural networks; however the use of an ensemble of classifiers has not been previously explored and may prove to be beneficial. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEG which are then used with the ensemble of classifiers based Learn++ algorithm. We describe the approach, and present our promising preliminary results.

A new motion planning framework is presented that enables multiple mobile robots with limited ranges of sensing and communication to maneuver and achieve goals safely in dynamic environments. To combine the respective advantages of centralized and de-centralized planning, this framework is based on the concept of centralized planning within dynamic robot networks. As the robots move in their environment, localized robot groups form networks, within which world models and robot goals can be shared. Whenever a network is formed, new information then becomes available to all robots in this network. With this new information, each robot uses a fast, centralized planner to compute new coordinated trajectories on the fly. Planning over several robot networks is decentralized and distributed. Both simulated and real-robot experiments have validated the approach.

A method of planning paths for formations of mobile robots with nonholonomic constraints is presented. The kinematics equations presented in this paper allow a general geometrical formation of mobile robots to be maintained while the group as a whole travels an arbitrary path. It is possible to represent a formation of mobile robots by a single entity with the same type of nonholonomic constraint as the individual members. Thus, any path-planner or control method may be used with the formation as would be applied to an individual robot. Equations are developed for changing the geometrical formation and hardware results are presented from the Stanford MARS Testbed.