## 1 Introduction
Furthermore, the differing kinematic characteristics of the mobile base and the manipulator present an additional challenge in determining the optimal base position and the pose of the manipulator [23; 24]. To avoid the above problems, the mobile base and the manipulator are planned separately using suitable methods. For example, the RRT-connect method can be used for both the mobile base and the manipulator [25], but planning is performed separately. It is also possible to use different methods, e.g., the mobile base is planned using a search-based method and the manipulator is planned using a sampling-based method [26; 27]. These methods improve the efficiency of path planning, but they do not consider the motion coupling between the mobile base and the manipulator, and so they cannot adapt to confined and complex environments nor can they fully exploit the flexibility provided by the redundant degrees of freedom of the mobile manipulator. Therefore, the idea of adaptive dimensionality has been proposed, i.e., planning in high dimensions only when a feasible path cannot be searched in a low dimension [28]; Hierarchical and Adaptive Mobile Manipulator Planner (HAMP) [29] and Optimized Hierarchical Mobile Manipulator Planner (OHMP) [30] have been proposed based on this idea. These methods reduce the computational burden in a high-dimensional space, but still need to plan in high dimensions and suffer from a long, time-consuming search for a feasible path. In addition, similar to the idea of adaptive dimensionality, changing the robot pose only when necessary during the robot's movement is another feasible idea [31; 32]; however, such methods focus on improving the ability of the mobile manipulator to move over a wide range and pass through some obstacles, but still do not solve the planning challenge of performing manipulation tasks in confined spaces. In this paper, we propose a hybrid sampling-based method for the motion planning of pick and place tasks for mobile manipulators. We note that some studies [33; 34] have proposed planning methods for mobile manipulator manipulation tasks, but these methods are applicable to open spaces where collision avoidance is not a very difficult problem. Our approach is dedicated to the problem of collision-free path planning in confined spaces. The proposed approach refers to tunnel-like path planning and mixes different random sampling-based methods. Currently, operations in confined spaces such as pipelines are carried out using complex devices [35]. Mobile manipulators have the potential to be applied to operations in relatively short pipelines using the proposed planning method. The main contributions of our work are as follows: (1) A sampling-based configuration generation method is proposed to rapidly generate a collision-free configuration for a mobile manipulator, including the mobile base pose and manipulator joint states, that satisfies the target configuration of the end-effector. (2) A sampling strategy that samples in the neighborhood of the last configuration of the mobile manipulator is introduced, which avoids the inefficiency of sampling in a large space and increases the probability of obtaining a collision-free sample. (3) An informed guiding approach is introduced to determine the best pose among the samples. The remainder of this paper is organized as follows: Section 2 presents the kinematic model of a mobile manipulator and defines the general problem. Section 3 introduces normal sampling-based algorithms for mobile manipulators, followed by an analysis of their shortcomings when applied in confined spaces. Section 4 introduces our hybrid sampling-based method, which includes sampling-based configuration generation, the sampling strategy, and the informed guiding approach. Section 5 presents the simulation results and conducts a comparative analysis of the different algorithms. Finally, Section 6 concludes the paper by summarizing the findings and providing future perspectives.