Abstract
Autonomous ships or Unmanned Surface Vehicles (USV) collision avoidance and path planning problems among multi-vessels are investigated in this paper. Firstly, a modified fuzzy dynamic risk of collision model based on time and space collision risk index is proposed, which is much closer to real ship applications. Then, the fitness functions based on the risk of collision, navigational economy, International Regulations for Preventing Collisions at Sea 1972 (COLREGs) and collision avoidance timing are established respectively to ensure the rationality of ship collision avoidance decisions. Moreover, path planning with global search capability is realized by the multi-objective decision theory combined with a genetic algorithm. The practicability and rationality of the recommended trajectory are guaranteed. Meanwhile, the problem of the non-inferior solution can be addressed by adapting the weight method and the constraint method and the optimized solution of the decision-making system can be achieved finally. Simulation results are further presented to validate the effectiveness of the proposed path planning and collision avoidance methods.
Highlights
In recent years, with the rapid development of Artificial Intelligence (AI) technology [1], [2], many intelligent algorithms, such as genetic algorithms, expert intelligence systems [3], [4], neural network algorithms [5]–[7], and fuzzy logic algorithms [8], [9] are widely used in the automation research of automobiles and aerial vehicle [10]
The fitness functions based on the risk of collision, navigational economy and collision avoidance timing are established respectively
The fitness functions based on the risk of collision, navigational economy, COLREGs and collision avoidance timing were established to solve the multi-encounter ship collision avoidance decision problems
Summary
With the rapid development of Artificial Intelligence (AI) technology [1], [2], many intelligent algorithms, such as genetic algorithms, expert intelligence systems [3], [4], neural network algorithms [5]–[7], and fuzzy logic algorithms [8], [9] are widely used in the automation research of automobiles and aerial vehicle [10]. It is a method to search for the optimal solution by simulating the natural evolution process [25] It has been revealed in [22] that the genetic algorithm can be used to model the ship’s trajectory planning and collision avoidance during a multi-vessel encounter situation. Based on the multi-objective genetic algorithm, path planning with global search capability and collision avoidance can be realized Both the practicability and rationality of the recommended trajectory are guaranteed. For SCR and TCR, only specific avoidance measures need to be taken to ensure that the minimum value is reduced to 0, it is safe to ‘‘avoid’’ the other ship, and the scope of the avoidance measures at this time is small Based on this principle, the modified ROC model can be taken as follows: CRI = min(udt, utt).
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