Khalasi: Energy-Efficient Navigation for Surface Vehicles in Vortical Flow Fields

2025/12/07 by Gadhvi, Rushiraj, Manjanna, Sandeep
Computer Science · Engineering · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Maritime Navigation and Safety #Robotic Path Planning Algorithms #Robotics (cs.RO) #Underwater Vehicles and Communication Systems

paper · doi:10.48550/arxiv.2512.06912

openalex publication_date 2025/12/07 · openalex created_date 2025/12/10 · openalex updated_date 2026/07/28

Abstract

For centuries, khalasi (Gujarati for sailor) have skillfully harnessed ocean currents to navigate vast waters with minimal effort. Emulating this intuition in autonomous systems remains a significant challenge, particularly for Autonomous Surface Vehicles tasked with long duration missions under strict energy budgets. In this work, we present a learning-based approach for energy-efficient surface vehicle navigation in vortical flow fields, where partial observability often undermines traditional path-planning methods. We present an end to end reinforcement learning framework based on Soft Actor Critic that learns flow-aware navigation policies using only local velocity measurements. Through extensive evaluation across diverse and dynamically rich scenarios, our method demonstrates substantial energy savings and robust generalization to previously unseen flow conditions, offering a promising path toward long term autonomy in ocean environments. The navigation paths generated by our proposed approach show an improvement in energy conservation 30 to 50 percent compared to the existing state of the art techniques.

Citations

• Enhancing Efficiency and Propulsion in Bio-mimetic Robotic Fish through End-to-End Deep Reinforcement Learning
• Addressing Function Approximation Error in Actor-Critic Methods
• Efficient collective swimming by harnessing vortices through deep reinforcement learning
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
• Proximal Policy Optimization Algorithms
• Planning and Implementing Trajectories for Autonomous Underwater Vehicles to Track Evolving Ocean Processes Based on Predictions from a Regional Ocean Model

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