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.