A hybrid RVO-MPPI approach for efficient collision avoidance for multiple autonomous underwater vehicles

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Collision-avoidance path planning for Autonomous Underwater Vehicles (AUVs) in complex underwater environments presents a significant challenge, particularly considering the maneuverability limitations of fin-rudder-controlled AUVs. This study proposes a novel navigation approach that combines the strengths of Reciprocal Velocity Obstacle (RVO) principles and Model Predictive Path Integral (MPPI) control to enhance autonomous obstacle avoidance for multiple AUVs. The key innovation lies in leveraging RVO-based obstacle avoidance velocities to enhance both action sampling and cost function optimization in MPPI framework. Specifically, a neural network trained through supervised learning is utilized to convert the RVO-provided avoidance velocities into desired actions for MPPI. Additionally, these velocities are incorporated into the MPPI cost function, thereby enhancing the optimization process and facilitating convergence towards optimal, collision-free navigation solutions. Simulation experiments validated the method's capability to provide effective obstacle avoidance behavior across various complex scenarios. Results indicate that this approach successfully integrates MPPI's predictive capabilities with RVO's velocity avoidance logic, enabling AUVs to navigate efficiently through complex underwater environments while ensuring mission safety and effectiveness. This approach offers a promising new solution for autonomous navigation of AUVs, with potential applications in ocean exploration, underwater search and rescue, and other maritime operations. Future research will explore the performance of this approach in real-world underwater environments and consider the impact of additional dynamic factors.