Zhuoyuan Song

A Compact Autonomous Underwater Vehicle With Cephalopod-Inspired Propulsion

In this paper, a bioinspired, compact, cost-effective autonomous underwater vehicle system is presented. Designed to operate in a heterogeneous, multivehicle collaboration hierarchy, the presented vehicle design features 3D printing technology to enable fast fabrication with a complex internal structure. Similar to a previous vehicle prototype, this system generates propulsive forces by expelling unsteady, pulsed jets, inspired by the locomotion of cephalopods and jellyfish. The novel thrusters enable the vehicle to be fully actuated in horizontal plane motions, without sacrificing the low-forward-drag, slender vehicle profile. By successively ingesting water and expelling finite water jets, periodic actuation forces are generated at all possible vehicle velocities, eliminating the need for control surfaces used in many conventional underwater vehicle designs. A semiactive buoyancy control system, inspired by the nautilus, adjusts the vehicle depth by passively allowing water flowing into and actively expelling water out of an internal bladder. A compact embedded system is developed to achieve the control and sensing capabilities necessary for multiagent interactions with the minimum required processing power and at a low energy cost. The new vehicle design also showcases an underwater optical communication system for short-range, high-speed data transmission, supplementing the conventional acoustic communication system. Experimental results show that, with the thruster motors powered at a 60% duty-cycle, the new vehicle is able to achieve a 1/4 zero-radius turn in 3.5 s and one-body-width sway translation in 2.5 s.

A distributed localization hierarchy for an AUV swarm

Localization of teams of autonomous underwater vehicles (AUVs) still remains as a challenge in large-scale ocean currents. In this study, moving references, drifting under the influence of the ocean background flow, were employed in order to improve the cooperative localization (CL) performance of an AUV swarm in harsh ocean flows. More capable AUVs (dubbed as mother AUVs) with less localization error were utilized as moving references for improving localization error of less capable AUVs (called daughter AUVs). Limitations of a previously proposed modified extended Kalman filter (MEKF) were identified. A particle filter (PF) based algorithm was proposed to address those issues. The performance of the PF algorithm was compared with the MEKF algorithm in several simulated examples including CL in an N-vortex background flow field. Both algorithms can effectively avoid the diverging behavior of localization error in pure CL. The PF algorithm is more robust in choosing the better localized AUV. With a large number of particles, the PF algorithm outperforms the MEKF algorithm at the expense of computational efforts.

Cooperative Underwater Localization in Ocean Currents

The localization accuracy determines the value of underwater meteorology data collected using unmanned vehicle fleets. Due to the rapid attenuation of radio frequency in water, the localization problem for autonomous underwater vehicles is very challenging, especially in the presence of strong currents. Due to the small sizes and weights of such vehicles, their motions are more susceptible to background flows than lager platforms. Our recent studies, among others, have shown that, in the presence of strong background flows, unmanned vehicles can follow near fuel-optimal trajectories found by Lagrangian coherent structures based fluid control algorithm, which improves the vehicles’ runtime and path following accuracy. With these vehicles, we propose a mother-daughter cooperative underwater localization method to address localization problems of other lower-cost underwater platforms in ocean currents. A probabilistic formulation of the problem is provided and we divided it into two independent sub problems, i.e. the dynamic simultaneous localization and mapping problem and the cooperative localization problem. The cooperative algorithm is derived in both separated and integrated models. Simulations are performed using the extended Kalman filter to prove the theory. Comparisons between the pure dead reckoning method and the proposed method show our algorithm yields bounded localization errors.

Towards background flow based AUV localization

Underwater localization faces many constrains and long-term persistent global localization for autonomous underwater vehicles (AUVs) is very difficult. In this paper, we propose a novel AUV localization method taking advantage of the recent progress in ocean general circulation models (OGCMs). During navigation, the AUV performs intermittent local background flow velocity measurements or estimates using on-board sensors. A series of preloaded flow velocity forecast maps generated by OGCMs are referred by a particle filter in updating particle weights based on resemblance between forecasts and local estimation. A rigorous derivation of the problem in probability theory is presented to reveal the recursive structure of the target distribution function. Simulations in a simple double-gyre velocity field exhibit satisfactory converging localization error. Further simulations in a flow field with local flow fluctuations that are not resolved by OGCMs show similar convergent localization error with a slower converging rate. As a first step towards a new set of underwater localization methods, this work presents promising results and reveals the possibility of realizing converging global underwater localization through partial utilization of the background flow information that is easily accessible.

Fluid-based cooperative underwater localization

Localization for autonomous underwater vehicles (AUV) is difficult because of the lack of adequate light, distinguishable landmarks and global positioning system (GPS) radio frequency (RF) signals in undersea areas. Due to maneuverability limitations, small AUVs are usually susceptible to strong ocean currents. The impacts of ocean flow on vehicle motion are usually too large to be considered as disturbances. Simulations of large-scale ocean currents are available to track changes of the background flow. Fluid-based multi-vehicle path planning algorithms have been developed to achieve the feasibility in the presence of the background flow and optimized energy consumption. With certain information from both simulations and measurements of the background flow, the performance of multi-AUV cooperative localization can be improved. In this work, a multi-AUV cooperation hierarchy is proposed. More capable AUVs with bounded localization errors are used as localization references to improve the localization performance of other low-cost AUVs. The proposed algorithm is fully distributed and it is verified by using a modified extended Kalman filter (MEKF). This paper focuses on the outline of the algorithm and its associated matching techniques. The performance of the algorithm is evaluated based on simulations in the flow field generated by the N-vortex system. Localization errors of low-cost AUVs are all bounded at satisfactory levels. The diverging behavior of the pure cooperative localization is effectively avoided.