Optimal Spacecraft Formation Reconfiguration with Collision Avoidance Using Particle Swarm Optimization

2012 ◽  
Vol 41 (2) ◽  
Author(s):  
Haibin Huang ◽  
Guangfu Ma ◽  
Yufei Zhuang ◽  
Yueyong Lv
Author(s):  
Ziyad Allawi ◽  
Turki Abdalla

In this paper, a new optimization method for the Reciprocal Velocity Obstacles (RVO) is proposed. It uses the well-known Particle Swarm Optimization (PSO) for navigation control of multiple mobile robots with kinematic constraints. The RVO is used for collision avoidance between the robots, while PSO is used to choose the best path for the robot maneuver to avoid colliding with other robots and to get to its goal faster. This method was applied on 24 mobile robots facing each other. Simulation results have shown that this method outperforms the ordinary RVO when the path is heuristically chosen.


2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Yisong Zheng ◽  
Xiuguo Zhang ◽  
Zijing Shang ◽  
Siyu Guo ◽  
Yiquan Du

In the process of ship collision avoidance decision making, steering collision avoidance is the most frequently adopted collision avoidance method. In order to obtain an effective and reasonable steering angle, this paper proposes a decision-making method for ship collision avoidance based on improved cultural particle swarm. Firstly, the ship steering angle direction is to be determined. In this stage, the Kalman filter is used to predict the ship’s trajectory. According to the prediction parameters, the collision risk index of the ship is calculated and the situation with the most dangerous ship is judged. Then, the steering angle direction of the ship is determined by considering the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs). Secondly, the ship steering angle is to be calculated. In this stage, the cultural particle swarm optimization algorithm is improved by introducing the index of population premature convergence degree to adaptively adjust the inertia weight of the cultural particle swarm so as to avoid the algorithm falling into premature convergence state. The improved cultural particle swarm optimization algorithm is used to find the optimal steering angle within the range of the steering angle direction. Compared with other evolutionary algorithms, the improved cultural particle swarm optimization algorithm has better global convergence. The convergence speed and stability are also significantly improved. Thirdly, the ship steering angle direction decision method in the first stage and the ship steering angle decision method in the second stage are integrated into the electronic chart platform to verify the effectiveness of the decision-making method of ship collision avoidance presented in this paper. Results show that the proposed approach can automatically realize collision avoidance from all other ships and it has an important practical application value.


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