## 1 Introduction  
A mobile manipulator (MM) is defined as a mobile base with one (or more) traditional robotic arm, thereby combining the advantages of both mobile robots and fixed-base manipulators [1]. A mobile manipulator's ability to traverse a vast area while retaining dexterous manipulation capabilities renders it a versatile tool for a multitude of applications, including rescue operations [2], object transportation [3], fruit harvesting [4], large-scale complex component machining [5], flexible manufacturing [6], and healthcare [7] in different scenarios. A mobile manipulator is frequently a kinematically redundant system, with more than nine degrees of freedom (DOFs). The redundancy of mobile manipulators increases their flexibility when operating in complex environments [8]. However, this also makes the path planning process complex and challenging due to the infinite solutions to the inverse kinematic problem. Consequently, the efficient planning of a safe and reachable path for a mobile manipulator is a topic worthy of further research. Two principal categories of algorithms for robot path planning are search-based and sample-based algorithms. Search-based algorithms, also known as graph search algorithms, such as A* [9], require the creation of a graphical representation of the environment. This is followed by the search for the shortest-distance path or other lowest-cost path from the start to the goal. Some researchers have proposed methods to improve the efficiency of these algorithms [10]. However, the difficulty and complexity of creating graphical representations preclude the applicability of these algorithms to mobile manipulators with high DOFs. Sampling-based algorithms, such as rapidly exploring random trees (RRT) [11], employ a random sampling process to select points and then connect these samples to generate a tree. These methods can be employed in high-dimensional spaces and complex environments. One limitation of these methods is that they cannot guarantee optimal paths and are sensitive to sampling strategies [12]. Furthermore, the efficiency of the algorithm can be significantly impaired in confined or constrained environments. As a result, a number of RRT-based variants have been developed with the aim of enhancing efficiency, including RRT-connect [13], RRT* [14], informed RRT* [15], and EB-RRT [16]. Currently, Artificial Intelligence (AI), a tool for obstacle detection and optimization, has increased the performance of traditional algorithms [17]. When sampling-based algorithms are employed for mobile manipulators, such as those described in [18], the algorithms' dimensionality will be similarly high due to the high DOFs, and the search space will be exceedingly large, resulting in low sampling efficiency. Furthermore, the overall algorithm will be highly complex and time-consuming due to the necessity of complex collision detection for each sample. A novel sampling-based planning algorithm designed for mobile manipulators named XXL was proposed where workspace information is used as a guide through the high-dimensional planning space [19]. However, XXL also has a high computational overhead. One potential solution to this issue is to enhance the sampling strategy [20]. An alternative approach is to initially plan the trajectory in the workspace and subsequently utilize inverse kinematics and dynamic constraints to generate the configuration of the mobile manipulator at each waypoint along the path. The path of the end-effector is initially planned using a sampling-based algorithm. Subsequently, existing trajectory tracking methods can be employed for the mobile manipulator to follow the planned path [21; 22]. These methods are more suitable for open environments, but it is challenging to obtain a valid solution that meets all constraints and does not collide with the environment in a confined space.