The meroplanktonic larvae of many invertebrate and vertebrate species rely on physical transport to move them across the shelf to their adult habitats. One potential mechanism for cross‐shore larval transport is Stokes drift in internal waves. Here, we develop theory to quantify the Stokes velocities of neutrally buoyant and depth‐keeping organisms in linear internal waves in shallow water. We apply the analyses to theoretical and measured internal wave fields, and compare results with a numerical model. Near the surface and bottom boundaries, both neutrally buoyant and depth‐keeping organisms were transported in the direction of the wave's phase propagation. However, neutrally buoyant organisms were transported in the opposite direction of the wave's phase at mid depths, while depth‐keeping organisms had zero net transport there. Weakly depth‐keeping organisms had Stokes drifts between the perfectly depth‐keeping and neutrally buoyant organisms. For reasonable wave amplitudes and phase speeds, organisms would experience horizontal Stokes speeds of several centimeters per second—or a few kilometers per day in a constant wave field. With onshore‐polarized internal waves, Stokes drift in internal waves presents a predictable mechanism for onshore transport of meroplanktonic larvae and other organisms near the surface, and offshore transport at mid depths.
This paper describes a novel communication-spare cooperative localization algorithm for a team of mobile unmanned robotic vehicles. Exploiting an event-based estimation paradigm, robots only send measurements to neighbors when the expected innovation for state estimation is high. Since agents know the event-triggering condition for measurements to be sent, the lack of a measurement is thus also informative and fused into state estimates. The robots use a Covariance Intersection (CI) mechanism to occasionally synchronize their local estimates of the full network state. In addition, heuristic balancing dynamics on the robots' CI-triggering thresholds ensure that, in large diameter networks, the local error covariances remains below desired bounds across the network. Simulations on both linear and nonlinear dynamics/measurement models show that the event-triggering approach achieves nearly optimal state estimation performance in a wide range of operating conditions, even when using only a fraction of the communication cost required by conventional full data sharing. The robustness of the proposed approach to lossy communications, as well as the relation-ship between network topology and CI-based synchronization requirements, are also examined. I. IntroductionThe decrease in the price and weight of robotic hardware (wireless communication, sensor suites, actuators) can make possible the autonomous deployment of large teams of unmanned aerospace vehicles in surveillance, exploration, search-and-rescue, and cargo-transportation missions. While today's technology has advanced tremendously, algorithms that can endow robotic teams with the desired autonomy are still lacking. In particular, the successful execution of higher-level tasks often relies on accurate robot position information; e.g. to be used in path planning or decisionmaking routines. In the case that full position information is unavailable, a relevant question is how vehicles can exploit their partial access to information to produce joint Cooperative Localization (CL) algorithms.The CL problem arises when mobile robots try to localize themselves with respect to other mobile robots, whom they can also communicate with. In CL, robots typically obtain and share measured/estimated relative pose information to improve their own local pose estimates (which may possibly be augmented with the pose estimates of other robots to obtain a 'moving map'). This approach has many close connections to the well-known simultaneous localization and mapping (SLAM) problem [1], where in CL the 'map features/landmarks' are dynamic and can also actively provide information to one another. As such, many different statistical state estimation techniques for decentralized CL have been developed to most efficiently leverage the sensing and computing capabilities of multiple robots. However, state of the art CL algorithms make many significant trades in terms of required communication bandwidth, computational processing, localization accuracy, network topology constraints, and robustn...
Internal waves are important to oceanographers because, as they travel, they are capable of displacing mass, such as plankton and small fish. This paper considers a group of drogues estimating the physical parameters that determine the dynamics of an ocean linear internal wave. While underwater, individual drogues do not have access to absolute position information and can only rely on inter-drogue measurements. Building on this data and the knowledge of the drogue dynamics under the flow induced by the internal wave, we propose the Vanishing Distance Derivative Detection Strategy to allow individual drogues to determine the wave parameters. We analyze the correctness and robustness of this strategy under noiseless and noisy measurements, respectively. We also introduce a general methodology, termed pth-Order Parameter Fusion, for combining the parameter estimates obtained at different times and characterize its error. Simulations illustrate our results.Index Terms-Cooperative parameter estimation, data fusion, Lagrangian dynamics, ocean linear internal waves, robustness to error, underwater robotic drifters.
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