Hybrid Partition-Based Patrolling Scheme for Maritime Area Patrol with Multiple Cooperative Unmanned Surface Vehicles - Jiajia Xie; Rui Zhou; Jun Luo; Yan Peng; Yuan Liu; Shaorong Xie; Huayan Pu (Journal of Marine Science and Engineering, 2020)
Multi-robot cooperative patrolling systems have been extensively employed in the civilian and military fields, including monitoring forest fires, marine search-and-rescue, and area patrol. Multi-robot area patrol problems refer to the activity that a team of robots works cooperatively and regularly to visit the key targets in the given area for security. Following consideration of the low cost and high safety of unmanned surface vehicles (USV), a team of USVs is organized to perform area patrol in a sophisticated maritime environment. In this paper, we establish a mathematical model considering the characteristics of the cooperative patrol task and the limited conditions of USVs. A hybrid partition-based patrolling scheme is proposed for a multi-USV system to visit targets with different importance levels in a maritime area. Firstly, a centralized area partition algorithm is utilized to partition the patrolling area according to the number of USVs. Secondly, a distributed path planning algorithm is applied to planning the patrolling path for each USV to visit the targets in a maritime environment to minimize the length of the patrolling path for the USV team. Finally, comparative experiments between the proposed scheme and other methods are carried out to validate the performance of the hybrid partition-based patrolling scheme. Simulation results and experimental analysis show the efficiency of the proposed hybrid partition-based patrolling scheme compared to several previous patrolling algorithms.

Multi-AUVs Cooperative Target Search Based on Autonomous Cooperative Search Learning Algorithm - Yuan Liu; Min Wang; Zhou Su; Jun Luo; Shaorong Xie; Yan Peng; Huayan Pu; Jiajia Xie; Rui Zhou (Journal of Marine Science and Engineering, 2020)
As a new type of marine unmanned intelligent equipment, autonomous underwater vehicle (AUV) has been widely used in the field of ocean observation, maritime rescue, mine countermeasures, intelligence reconnaissance, etc. Especially in the underwater search mission, the technical advantages of AUV are particularly obvious. However, limited operational capability and sophisticated mission environments are also difficulties faced by AUV. To make better use of AUV in the search mission, we establish the DMACSS (distributed multi-AUVs collaborative search system) and propose the ACSLA (autonomous collaborative search learning algorithm) integrated into the DMACSS. Compared with the previous system, DMACSS adopts a distributed control structure to improve the system robustness and combines an information fusion mechanism and a time stamp mechanism, making each AUV in the system able to exchange and fuse information during the mission. ACSLA is an adaptive learning algorithm trained by the RL (Reinforcement learning) method with a tailored design of state information, reward function, and training framework, which can give the system optimal search path in real-time according to the environment. We test DMACSS and ACSLA in the simulation test. The test results demonstrate that the DMACSS runs stably, the search accuracy and efficiency of ACSLA outperform other search methods, thus better realizing the cooperation between AUVs, making the DMACSS find the target more accurately and faster.

Characterization of a Cu2+, SDS, alcohol and glucose tolerant GH1 β-glucosidase from Bacillus sp. CGMCC 1.16541 - Yi-Rui Yin; Peng Sang; Feng-Li Yang; Tao Li; Run-Feng Yang; Hong-Yan Liu; Zong-Long Luo; Wen-Jun Li; Li-Quan Yang (Antonie van Leeuwenhoek, 2020)

Anfibatide Preserves Blood–Brain Barrier Integrity by Inhibiting TLR4/RhoA/ROCK Pathway After Cerebral Ischemia/Reperfusion Injury in Rat - Peng Gong; Rui Li; Hui-Yu Jia; Zheng Ma; Xiao-Yi Li; Xiang-rong Dai; Sheng-Yong Luo (Journal of Molecular Neuroscience, 2020)