Related Experiment Video
Updated: Jul 11, 2025

Remote Magnetic Navigation for Accurate, Real-time Catheter Positioning and Ablation in Cardiac Electrophysiology Procedures
Published on: April 21, 2013
Autonomous ship navigation with an enhanced safety collision avoidance technique
Hub Ali1, Gang Xiong2, Qu Tianci1
1State Key Laboratory of Multimodal Artificial Intelligence Systems, Beijing Engineering Research Center of Intelligent Systems and Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
Abstract:
The motion of an autonomous ship is different from that of ground and aerial robots due to its maneuvering and environmental constraints. As a result, many techniques have been introduced for autonomous ship path planning. This paper presents a novel technique for global and local navigation planning of autonomous ships under complex static and dynamic constraints. Our technique, termed safety-enhanced path planning (SPP), has been developed to avoid potential collisions with underwater obstacles near seaside areas. SPP pre-processes the map to preserve the shape of visible obstacles and mark a safety-outline around the shores. Subsequently, an offset safety line (OSL) is drawn about the original shore to protect the ship when passing close to threat-defined offshore areas. The global path is produced with an enhanced A* multi-directional algorithm, considering the kinematic constraint of the ship. To ensure optimal path quality, the global path is further refined with a smoothing filter to improve consistency and smoothness. Additionally, local navigation is introduced to help the autonomous ship avoid collisions with other obstacle ships. Local offset trajectories are produced with 4th and 5th degree polynomials along longitudinal and lateral coordinates in time t. Distance closest point approach (DCPA) is utilized for early obstacle prediction to help the ship maneuver in complex dynamic obstacle avoidance scenarios. The trajectory set is filtered with an efficient cost policy to obtain the best trajectory for dynamic collision avoidance. We conduct simulations in MATLAB and compared with other maritime path planning methods to verify the effectiveness of our approach.

