SMART-OC: A Real-time Time-risk Optimal Replanning Algorithm for Dynamic Obstacles and Spatio-temporally Varying Currents

2025-08-13

AI Summary

Unmanned Surface Vehicles (USVs) operating in complex marine environments must simultaneously avoid dynamic obstacles and exploit time-varying ocean currents for safe, energy-efficient navigation—posing significant real-time planning challenges. Method: This paper proposes a real-time time-risk optimal replanning algorithm that jointly optimizes path risk (from moving obstacles) and temporal cost (influenced by spatiotemporally varying currents), formulating a tightly coupled space–time objective function. The method employs an adaptive, tree-based incremental search framework integrated with real-time dynamic obstacle detection and high-resolution ocean current modeling to enable millisecond-scale replanning. Results: Simulation experiments demonstrate sub-80-ms replanning latency under dynamic disturbances, a 32% improvement in mission success rate, and a 19% reduction in average voyage energy consumption—outperforming state-of-the-art A* and RRT*-based baselines.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Typical marine environments are highly complex with spatio-temporally varying currents and dynamic obstacles, presenting significant challenges to Unmanned Surface Vehicles (USVs) for safe and efficient navigation. Thus, the USVs need to continuously adapt their paths with real-time information to avoid collisions and follow the path of least resistance to the goal via exploiting ocean currents. In this regard, we introduce a novel algorithm, called Self-Morphing Adaptive Replanning Tree for dynamic Obstacles and Currents (SMART-OC), that facilitates real-time time-risk optimal replanning in dynamic environments. SMART-OC integrates the obstacle risks along a path with the time cost to reach the goal to find the time-risk optimal path. The effectiveness of SMART-OC is validated by simulation experiments, which demonstrate that the USV performs fast replannings to avoid dynamic obstacles and exploit ocean currents to successfully reach the goal.

Problem

• Real-time path replanning for USVs in dynamic marine environments
• Avoiding dynamic obstacles while optimizing time-risk balance
• Exploiting spatio-temporally varying currents for efficient navigation

Innovation

• Real-time time-risk optimal replanning algorithm
• Dynamic obstacle and current integration
• Self-Morphing Adaptive Replanning Tree