# A Fusion Multi-Strategy Marine Predator Algorithm for Mobile Robot Path Planning
## 1 Introduction  
A mobile robot is a kind of mechanical device that can perform work automatically [1]. It can act according to the scheme made by artificial intelligence technology [2]. In harsh environments, mobile robots can replace humans in performing specific tasks. With the continuous improvement of hardware equipment, mobile robots have been widely used in various fields, such as industry, medicine, services, national defense and space exploration [3, 4, 5]. Path planning has always been a hot topic in the field of mobile robot technology research. The level of planning represents the intelligence level of a robot to a certain extent [6]. Path planning aims to find an optimal path from the starting to the end point, and the criteria for this path include the shortest path length, no obstacle collisions in the path, and the best safety [7]. The essence is to obtain the optimal or feasible solution under several constraints [8]. Mobile robot path planning problems can be divided into two categories: static known environments and dynamic unknown environments. Static path planning is named global path planning, while dynamic path planning is named local path planning [9]. The robot has a priori information about the environment in global path planning. In local path planning, the robot only has information about the start point, target point and part of the environment. This paper mainly studies the known static path planning in the global environment. The main steps of path planning are environment modelling, path search and path optimization [10]. Path search is the core of the whole mobile robot path planning problem, and theperformance of the search algorithm directly determines the path planning results. With the development of mobile robot research, several scholars have proposed many effective methods to solve robot path planning problems. [PERSON] et al. proposed an improved artificial potential field and improved the repulsive function combined with the fuzzy inference system to make robots safely and quickly find a collision-free path [11]. [PERSON] et al. combined the method of corner detection in image processing to determine the key location for visual coverage and address the path planning problem by connecting these observation locations [12]. [PERSON] et al. proposed an improved deep reinforcement learning algorithm based on PTZ image information to solve the problems of poor exploration ability in patrol robots and improved reward and punishment functions to optimize the robot path [13]. Although the traditional methods can also achieve better results, their disadvantages are not negligible, such as the problem of unreachable paths in artificial potential field methods, and the problem of excessive computation in image processing and deep reinforcement learning methods. Traditional path planning algorithms have an exponential increase in computational complexity in complex environments, and the algorithms tend to fall into a local minima. Therefore, a computationally simple, efficient, and global search-competent algorithm is needed instead of the original path planning method. In recent years, meta-heuristic algorithms have been favored by scholars and have achieved better results due to their better optimization ability. The meta-heuristic algorithms proposed by imitating the behavioral characteristics or evolutionary behavior of natural biological populations provide new solutions to complex global optimization problems. Meta-heuristic algorithms have high flexibility, robustness, and high operability, which greatly weaken the dependence on mathematical models and have a strong global search capability. [PERSON] et al. conducted a hybridized particle swarm optimization-modified frequency bat algorithm that minimizes distance and follows path smoothness criteria, the method generates an optimal feasible path even in complex dynamic environments [14]. [PERSON] et al.