Autonomous Flow-Based Navigation in Unsteady Underwater Environments

Citation

Gunnarson, Peter John (2024) Autonomous Flow-Based Navigation in Unsteady Underwater Environments. Dissertation (Ph.D.), California Institute of Technology. doi:10.7907/vnh6-3t44. https://resolver.caltech.edu/CaltechTHESIS:06052024-052757779

Abstract

Autonomous ocean-exploring robots promise to significantly enhance the rate at which we can explore ocean environments. However, the limited range and speed of existing autonomous underwater vehicles (AUVs) are barriers to comprehensive ocean exploration. To address these limitations, the work in this thesis investigates strategies for improving the capabilities of existing AUVs, such as targeted sampling and efficient navigation through background flows. Inspired by the ability of aquatic animals to navigate via flow sensing, hydrodynamic cues are investigated as a sensory input for accomplishing these feats of autonomous navigation using only onboard sensors. First, reinforcement learning (RL) is investigated as an algorithm for accomplishing efficient point-to-point navigation in simulated cylinder flow. The algorithm entails inputting point measurements of flow quantities such as velocity and vorticity into a deep neural network, which then determines a swimmer's actions. Using point velocity as the sensory input, the RL algorithm achieved a near 100 percent success rate in reaching the target locations while approaching the time-efficiency of optimal navigation trajectories. To test RL and flow-based navigation in a physical setting, we next developed the Caltech autonomous reinforcement learning robot (CARL), a palm-sized underwater robotic platform. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot was tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. Using a navigation policy trained with RL in a simulated flow environment, CARL successfully located the turbulent plumes at more than twice the rate of random searching by detecting mean flow gradients with the onboard pressure sensors. Lastly, combing both flow sensing and efficient navigation, the accelerometer onboard CARL was used to sense and exploit the flow from a passing vortex ring for energy-efficient propulsion. Body acceleration and rotation were shown to be effective methods of indirect flow sensing, which enabled the energy-efficient vortex ring surfing strategy. Throughout this work, efforts are made to understand the governing physics behind the discovered navigation strategies to generalize the results beyond a specific navigation problem, sensor type, or robotic implementation.

Thesis Files