# Dynamic Path Planning for Unmanned Surface Vehicles with a Modified Neuronal Genetic Algorithm
## 2 Related Work  
Given the wide range of applications for USVs across different domains, numerous studies have explored path planning using various approaches. In this literature review, we classify the related algorithms into three types: conventional algorithms, deep reinforcement learning, and evolutionary algorithms. The conventional algorithm is used because it is relatively simple and produces quite good results. For the conventional or simple algorithm, some researchers modify A* algorithms. The constrained A* method [8] is an approach used in optimal path planning for USVs in a maritime environment. Another A* modification is the smoothed A* algorithm [9], applied with a new path-smoothing process. The improved D* Lite algorithm [10] was implemented for the path planning of unmanned surface vessels in an unknown environment, and a greedy algorithm [11] with an adaptively rotatable distance sensor was developed to equip an autonomous evacuation boat designed for dynamic flood situations. Apart from researching modified simple algorithms, researchers also conduct studies using deep reinforcement learning (RL) and its modifications. In contrast to traditional algorithms, deep RL-based path planning algorithms offer a novel approach, incorporating advanced artificial intelligence at a high level [7]. RL demonstrates the capability to acquire high performance when operating in unfamiliar environments by learning through interactions and experiences gained from training environments, all without prior information or knowledge [12]. RL research into complete-coverage path planning [13] was conducted to solve the slow convergence speed problem. Other RL studies on cooperative path planning [7] aimed to solve compliance with vehicle motion constraints implemented in computer-based simulations and real-world maritime settings. Then, RL for an intelligent controller [14] was proposed, involving the utilization of an intelligent adaptive PID controller, which was improved through the integration of proximal policy optimization (PPO). This enhancement was made with the aim of attaining a high level of automation for USVs. Then, distributional RL was used in robust USV navigation [12] and learned to capture the uncertainty of action outcomes. A RL method with ANOVA was also implemented for autonomous navigation and obstacle avoidance [15]. While RL has demonstrated promising  
Figure 1: Illustration of how a USV performs path planning in a dynamic environment. results in USV path planning tasks, it is not without its limitations. RL typically demands substantial volumes of training data to acquire effective policies, and as the dataset grows, it necessitates more significant computational resources to process and train effectively [16]. To overcome the shortcomings of the RL approach, nature-based algorithms offer some advantages. Nature-based algorithms can work with limited data and may not require extensive training. These algorithms are easier to implement and require fewer computational resources. In addition, nature-based algorithms can also provide better performance than conventional approaches because nature algorithms involve the learning and optimization scenario. Some studies have been conducted that use nature-based algorithms for USV path planning, such as the improved biological-inspired neural network [17], trajectory-cell-based algorithm [18], bacterial foraging optimization algorithm [19], improved shark-inspired algorithm [20], and plant growth algorithms [21]. Physics-based algorithms also provide methods like the artificial potential field approach [22]. Among the numerous nature-based algorithms available, our choice is to adapt the genetic algorithm (GA). This algorithm is versatile and capable of handling a wide array of optimization scenarios whether the fitness function exhibits continuity or discontinuity, linearity or nonlinearity, or stationarity or nonstationary (changing over time), even when influenced by random noise [23].