Zhengyu Zhou
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Research Subjects
Research Interests
Zhengyu Zhou has a total of 18 co-authors. Here are the top co-authors by number of shared publications:
- Hongrui Lu
Navigation College, Dalian Maritime University, Dalian 116026, China
2 shared publications Yingjun Zhang
2 shared publications- Yingjun Zhang
Navigation College, Dalian Maritime University, Dalian, China
2 shared publications Bingqi Ding
1 shared publication- Dongming Wang
Southeast University
1 shared publication - Feixiang Zhu
Navigation College, Dalian Maritime University, Dalian, China
1 shared publication
Latest Publications
- 6 July 2024
- Published by Elsevier in Ocean Engineering
- Vol. 310
Research on autonomous berthing control of MASS based on real time kinematic
- 6 July 2024
- Published by Elsevier in Ocean Engineering
- Vol. 310
Research on autonomous berthing control of MASS based on real time kinematic
- 5 February 2024
- Published by Elsevier in Ocean Engineering
- Vol. 295
Indicator designing for performance evaluation of collision avoidance algorithms programs on autonomous ships
- 5 February 2024
- Published by Elsevier in Ocean Engineering
- Vol. 295
Indicator designing for performance evaluation of collision avoidance algorithms programs on autonomous ships
- 4 May 2023
- Published by Elsevier in Ocean Engineering
- Vol. 280
A system for the validation of collision avoidance algorithm performance of autonomous ships
- 4 May 2023
- Published by Elsevier in Ocean Engineering
- Vol. 280
A system for the validation of collision avoidance algorithm performance of autonomous ships
- Open Access
- 27 October 2022
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 10 (11) , 1588
Randomly Testing an Autonomous Collision Avoidance System with Real-World Ship Encounter Scenario from AIS Data
Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.
- 27 October 2022
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 10 (11) , 1588
Open AccessRandomly Testing an Autonomous Collision Avoidance System with Real-World Ship Encounter Scenario from AIS Data
Maritime Autonomous Surface Ship (MASS) is promoted as the future of intelligent shipping. While autonomy technologies offer a solution for MASS, they have also resulted in new challenges for performance validation. To address this, a scenario-based validation method to test the autonomous collision avoidance system is proposed in this paper, including mining ship encounter scenarios from massive historical AIS data and randomly generated virtual test scenarios according to the parameter probability distributions from the collected real scenarios, as well as the final experiments: a total of 2900 generated scenarios including single ship and multi-ship encounter situations are created and applied to conduct testing experiments on the further assessment of our collision avoidance algorithm. The results indicate that the proposed method has the ability to quickly create appropriate testing scenarios according to AIS records, which are helpful to catch potential defects in a collision avoidance algorithm of MASS and to further analyze its navigating features. As a result, the research forms a systematic set of validation procedures from data gathering to practical experiments conduction, incorporating both the real statistics and the random generation method.
- Open Access
- 31 October 2021
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 9 (11) , 1202
A Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships
Large ships are typically with large inertia and longtime delay in motion, in prevailing collision avoidance methods, their maneuverability is generally neglected, there could be a dangerous situation if the system fails to control the ship course as ordered in a timely manner. This paper proposes a coordination system which consists of two algorithms for avoiding risk and then returning to scheduled waypoint. The avoiding risk algorithm are based on VO (velocity obstacle) method, the returning algorithm is derived from LOS (light of sight) guidance. For better performance, the ship model for simulation is a nonlinear Norrbin Model, with the controller improved by CGSA (closed loop gain shaping algorithm) method from traditional PID control, COLREGS (Convention on the International Regulations for Preventing Collisions at Sea) constrains are considered. To test the effectiveness of the proposed system, a series of complex scenarios including Imazu problem are applied.
- 31 October 2021
- Published by MDPI AG in Journal of Marine Science and Engineering
- Vol. 9 (11) , 1202
Open AccessA Coordination System between Decision Making and Controlling for Autonomous Collision Avoidance of Large Intelligent Ships
Large ships are typically with large inertia and longtime delay in motion, in prevailing collision avoidance methods, their maneuverability is generally neglected, there could be a dangerous situation if the system fails to control the ship course as ordered in a timely manner. This paper proposes a coordination system which consists of two algorithms for avoiding risk and then returning to scheduled waypoint. The avoiding risk algorithm are based on VO (velocity obstacle) method, the returning algorithm is derived from LOS (light of sight) guidance. For better performance, the ship model for simulation is a nonlinear Norrbin Model, with the controller improved by CGSA (closed loop gain shaping algorithm) method from traditional PID control, COLREGS (Convention on the International Regulations for Preventing Collisions at Sea) constrains are considered. To test the effectiveness of the proposed system, a series of complex scenarios including Imazu problem are applied.
- 27 April 2019
- Published by Springer Nature
- p. 488-499
HDAA: High-Speed Data Acquisition Algorithm of IoT
- 27 April 2019
- Published by Springer Nature
- p. 488-499
HDAA: High-Speed Data Acquisition Algorithm of IoT