Consistency-Aware Local Path Planning for Maritime Autonomous Surface Ships Under Perception Uncertainty: A Field-Validated Framework
Zhibo He, Chenguang Liu, Pulin Zhang, Huimin Chen, Ran Yan, Xiumin Chu · IEEE Transactions on Intelligent Transportation Systems · 2026
Ensuring consistent and reliable collision avoidance under perception uncertainty remains a major challenge for maritime autonomous surface ships (MASS). This paper proposes a lightweight and consistency-aware local path planning framework, called consistent Kalman virtual potential field (CKVPF), to improve decision robustness in dynamic and uncertain maritime environments. The framework combines virtual potential field-based planning with Kalman-filtered state estimation and introduces a historical consistency constraint to reduce decision fluctuations over time. CKVPF is implemented on a 45-meter-long MASS platform and tested through full-scale sea trials, with onboard real-time execution achieved using a Raspberry Pi 5. The experiments cover representative encounter scenarios, including head-on, overtaking, crossing and mixed-traffic, where the target ships exhibit unstable motion patterns. Results show that CKVPF maintains consistent decision-making and safely avoids collisions, achieving a 48.9% improvement in navigation path stability compared to baseline methods, while maintaining an average planning time of 177.8 ms. These findings demonstrate the method’s practical applicability and real-time performance for autonomous navigation under real-world uncertainty.