You Yusong

Bio: You Yusong is an academic researcher from Tianjin Polytechnic University. The author has contributed to research in topics: Deterministic algorithm & Heuristic (computer science). The author has an hindex of 1, co-authored 2 publications receiving 9 citations.

Wave glider network management system

Abstract: The invention discloses a wave glider network management system, and particularly relates to the field of wave glider autonomous navigation observation, and a wave glider is an unmanned autonomous vehicle which converts wave fluctuation into forward power by using a special double-body structure of the wave glider. Overall planning is made for remote management, data returning and receiving, dataanalysis and remote maneuverability of the wave glider, and ocean power environment data, marine organism environment data and air-sea interface data are obtained.

Task allocation and route planning of multiple UAVs in a marine environment based on an improved particle swarm optimization algorithm

Abstract: Unmanned aerial vehicles (UAVs) are considered a promising example of an automatic emergency task in a dynamic marine environment. However, the maritime communication performance between UAVs and offshore platforms has become a severe challenge. Due to the complex marine environment, the task allocation and route planning efficiency of multiple UAVs in an intelligent ocean are not satisfactory. To address these challenges, this paper proposes an intelligent marine task allocation and route planning scheme for multiple UAVs based on improved particle swarm optimization combined with a genetic algorithm (GA-PSO). Based on the simulation of an intelligent marine control system, the traditional particle swarm optimization (PSO) algorithm is improved by introducing partial matching crossover and secondary transposition mutation. The improved GA-PSO is used to solve the random task allocation problem of multiple UAVs and the two-dimensional route planning of a single UAV. The simulation results show that compared with the traditional scheme, the proposed scheme can significantly improve the task allocation efficiency, and the navigation path planned by the proposed scheme is also optimal.

Route planning for autonomous vessels based on improved artificial fish swarm algorithm

Abstract: ABSTRACT Path planning is one of the key technologies in the research of autonomous surface vessels (ASVs). In this paper, an improved artificial fish swarm algorithm (IAFSA) is proposed. The algorithm is modified from four perspectives: (1) A directional operator is introduced to improve the efficiency. (2) To avoid local optimum, a probability weight factor is proposed to adjust the frequency of executing random behaviour. (3) An adaptive operator has been applied aims at better convergence performance. (4) The waypoint modifying path smoother is used to improve the path quality. A comparative study has been carried out between IAFSA and the other state-of-the-art algorithms, and the results indicate that the proposed algorithm outperforms the others in both efficiency and path quality. Finally, IAFSA is integrated into the GNC system in a model ship. A computer-based sea trial around the Nan Hai area has been conducted, and environmental disturbances including wind, waves, and currents are considered. The results have shown that IAFSA is suitable for practical application.

Dynamic Path Planning for Forklift AGV Based on Smoothing A* and Improved DWA Hybrid Algorithm

Abstract: FAGV is a kind of heavy equipment in the storage environment. Its path needs to be simple and smooth and should be able to avoid sudden obstacles in the process of driving. According to the environmental characteristics of intelligent storage and the task requirements of FAGV, this paper proposed a hybrid dynamic path planning algorithm for FAGV based on improved A* and improved DWA. The improved A* algorithm can plan the global optimal path more suitable for FAGV. The improved evaluation function of DWA can ensure that the local path of FAGV is closer to the global path. DWA combines the rolling window method for local path planning to avoid sudden unknown static and dynamic obstacles. In addition, this paper verifies the effectiveness of the algorithm through simulation. The simulation results show that the algorithm can avoid obstacles dynamically without being far away from the global optimal path.