Synthetic Aperture Sonar (SAS) is a technique to improve the spatial resolution from a moving set of receivers by extending the array in time, increasing the effective array length and aperture. This technique is limited by the accuracy of the receiver position estimates, necessitating highly accurate, typically expensive aided-inertial navigation systems for submerged platforms. We leverage simultaneous localization and mapping to fuse acoustic and navigational measurements and obtain accurate pose estimates even without the benefit of absolute positioning for lengthy underwater missions. We demonstrate a method of formulating the well-known SAS problem in a SLAM framework, using acoustic data from hydrophones to simultaneously estimate platform and beacon position. An empirical probability distribution is computed from a conventional beamformer to correctly account for uncertainty in the acoustic measurements. The non-parametric method relieves the familiar Gaussian-only assumption currently used in the localization and mapping discipline and fits effectively into a factor graph formulation with conventional factors such as ground-truth priors and odometry. We present results from field experiments performed on the Charles River with an autonomous surface vehicle which demonstrate simultaneous localization of an unknown acoustic beacon and vehicle positioning, and provide comparison to GPS ground truths.

Multi-vehicle operations in the ocean are increasingly attractive as more vehicles become commercially available, however acoustics still defines the main practical channel for long-range wireless communication underwater. Long transmission times, scheduling delays and packet losses remain obstacles to implementing real-time multi-vehicle cooperative control for marine vehicles. We present a control system for dynamic pursuit with multiple vehicles exchanging time-difference-of-arrival (TDOA) measurements via underwater acoustic communications. Using data from field experiments conducted with autonomous surface vehicles, we show that a decentralized controller can track fast-moving targets and formation changes, leveraging partial information updates and decreased effective cycle times. As a comparison, we show results for trials conducted with one similar but centralized control scheme.

We apply the stationary multi-armed bandit (MAB) formalism to the problem of maximizing underwater acoustic data transmission through adaptive positioning of a mobile relay. Shallow-water environments, in particular, show complex and variable performance across physical space. This is a classic exploration vs. exploitation scenario, since choosing to visit new sites to learn their properties may be disadvantageous for cumulative data transmission if a better-performing site is already known with high confidence. Normal Gittins indices define a rule by which an optimal relay position can be chosen at each decision epoch, without assuming prior information and while pursuing the overall goal of maximizing total data transmission. We present results from shallow-water field experiments conducted with autonomous surface vehicles and acoustic modems transmitting data through a one-way, two-hop network in the Charles River Basin, Boston.

Marine robots are an increasingly attractive means for observing and monitoring in the ocean, but underwater acoustic communication (“acomms”) remains a major challenge, especially for real-time control. Packet loss occurs widely, bit rates are low, and there are significant delays. We consider here strategies for feedback control with acomms links in either the sensor-controller channel, or the controller-actuator channel. On the controller-actuator side we implement sparse packetized predictive control (S-PPC), which simultaneously addresses packet-loss and the data rate limit. For the sensor-controller channel we study a modified information filter (MIF) in a Linear Quadratic Gaussian (LQG) control scheme. Field experiments were carried out with both approaches, regulating crosstrack error in a robotic kayak using acomms. Outcomes with both the S-PPC and MIF LQG confirm that good performance is achievable.

Underwater acoustic communication channels display highly variable and stochastic performance, especially in multipath-limited shallow-water and harbor environments. A mobile acoustic node can, however, learn the channel's properties as it moves about. Maximizing the cumulative data transmission through adaptive node positioning is a clean exploitation vs. exploration scenario because learning about poorly characterized locations must be balanced against exploiting known ones. While this problem is well described with the stochastic multi-armed bandit formalism, the classical assumption of costless switching is untenable in the field, where slow-moving vehicles often cover large distances. We present a heuristic adaptation to the MAB Gittins index rule with limited policy enumeration to account for switching costs, and describe field experiments conducted in the Charles River (Boston MA). The field data establish that the MAB and its switching cost extension are tractable in this application, and that performance is consistently superior to that of ϵ-greedy policies.

This paper reports on a study to develop power supplies for small mobile robots performing long duration missions. It investigates the use of fuel cells to achieve this objective, and in particular Proton Exchange Membrane (PEM) fuel cells. It is shown through a representative case study that, in theory, fuel cell based power supplies will provide much longer range than the best current rechargeable battery technology. It also briefly discusses an important limitation that prevents fuel cells from achieving their ideal performance, namely a practical method to store their fuel (hydrogen) in a form that is compatible with small mobile field robots. A very efficient fuel storage concept based on water activated lithium hydride (LiH) is proposed that releases hydrogen on demand. This concept is very attractive because water vapor from the air is passively extracted or waste water from the fuel cell is recycled and transferred to the lithium hydride where the hydrogen is “stripped” from water and is returned to the fuel cell to form more water. This results in higher hydrogen storage efficiencies than conventional storage methods. Experimental results are presented that demonstrate the effectiveness of the approach.

This paper evaluates the potential for using robotic assistance for the insertion of commercial Perimodiolar Electrode Arrays (PEA's) that use a stylet and a pre-curved electrode body.We propose an algorithm for coordinated robotic insertion of the pre-curved electrode body and pulling of the stylet in order to provide partial control over the PEA shape. However, for robotic insertion to be a viable option for PEA's, an understanding of the statistical variability of their characteristics is essential. This paper investigates the kinematics of PEA's, their shape variability, and their expected performance during robotic insertion. An investigation of the required number of Degrees of Freedom (DoF) and the workspace is presented. A study of shape variability is carried out for characterizing shape repeatability across different insertion trials using the same electrode array. Also a study of shape variability is carried out between different electrode arrays. We simulate and evaluate the expected performance of three and four DoF robotic instruments for PEA insertion. The results support our proposed algorithm for coordinated insertion of the electrode array body and pulling of the stylet. They show that existing commerical perimodiolar arrays provide good shape repeatability. These results also point to the need for designing robotic insertion tools with at least four DoF. We believe that these results provide guidelines for future robotic instrument design for cochlear implant PEA's.