They are highly capable platforms and have been widely used on long duration missions approaching true persistence ( Manley and Willcox, 2010 Wiggins et al., 2010). These platforms operate by converting energy from the environment such as wave, wind, or solar into locomotive power. The other broad category of persistent platforms is surface energy harvesters such as the Waveglider ( Hine et al., 2009). These limitations preclude their use on mapping, survey, and surveillance missions. Additionally, their operational speeds are generally very slow (on the order of one knot ( Page et al., 2017)), and turning radiuses are on the order of 30–50 m ( Sherman et al., 2001). As gliders operate by converting vertical velocity into forward velocity with an external wing, they are unable to travel in level trajectories. The two primary drawbacks to glider deployments are caused by limitations imposed by their buoyancy driven nature. This results in vehicle endurance on the order of months to years ( Jones, 2012), especially when thermal harvesting is included ( Ma et al., 2016). This motion is incredibly efficient, as the vehicle only needs to expend significant energy during changes in net buoyancy. The net buoyant force causes the glider to sink (or rise) and an external wing along with internal actuation translates the purely vertical motion into sawtooth or helical flight ( Zhang et al., 2013). Underwater gliders locomote through controlling their net buoyancy. While both categories provide persistent presence in oceanic applications, they suffer from limitations in movement freedom and speed. This is largely due to the energy sustainability issue.Įxisting persistent-autonomous platforms can be generally broken down into two categories: 1) underwater gliders and 2) surface energy harvesters. Despite this, long-term monitoring is currently limited to a few unique platforms such as Waveglider ( Hine et al., 2009) and Slocum ( Webb et al., 2001). Understanding the marine environment is critical for a variety of missions ranging from safety and surveillance to biological studies and global weather forecasting. This method, when combined with recent work on docking station design, intelligent cooperative path planning, underwater communication, and underwater power transfer, will enable true persistent undersea operation in the extremely dynamic ocean environment. The vehicle should be able to successfully dock in the majority of foreseeable scenarios when re-attempts are considered. In the event of failed docking, a Dubins path is generated to efficiently drive the vehicle to re-attempt docking. The terminal homing phase was capable of handling up to 5 m offsets with approximately 70% accuracy (12 of 17 tests). In experimental testing using an Oceanserver Iver3 and Bluefin SandShark, the approach phase reached the target handoff within 2 m in 48 of 48 tests. A light tracking algorithm drives the vehicle from the handoff location into the dock. The path is followed using integral line of sight control until handoff to the terminal homing method. Specifically, the algorithm dynamically re-plans Dubins paths to create an efficient trajectory from the current vehicle position through approach into terminal homing. This paper presents an integrated navigational algorithm to facilitate reliable underwater docking of autonomous underwater vehicles. One emerging technology to enable persistent performance is the use of autonomous recharging and retasking through underwater docking stations. This is particularly challenging for traditional propeller-driven autonomous underwater vehicles which operate using energy intensive thrusters. One of the main limiting factors in deployment of marine robots is the issue of energy sustainability. School of Mechanical Engineering, Purdue University, West Lafayette, IN, United States.Page Reeve Lambert Jalil Chavez‐Galaviz Nina Mahmoudian*
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