## 3 Motivation  
In a household setting, a WMRA can be useful for assisting elderly and disabled individuals. In previous work, we used the beta process autoregressive hidden Markov model to pour water from the bottle to the cup [11], and a laser pointer was used to facilitate the interactions between humans and a WMRA in an indoor environment [12]. We focused on programming by the demonstration approach to improving the intelligence and adaptability of the WMRA [13] and proposed an object-affordance-based implicit interaction technology using a laser pointer [14]. The WMRA with laser interaction was designed to identify the desired intention faster and perform tasks with less limb involvement from the user, as shown in Figure 1.  
Previous research on the WMRA focused on its stationary state or proximity to the target object. However, in real-life household environments, disabled users may need to remotely lock onto a target object using lasers. In such scenarios, the WMRA must travel a certain distance to reach the vicinity of the target object. This presents a new challenge: determining the optimal parking position for the WMRA that allows the robotic arm to capture the target object in a shorter time and with a higher success rate. There are multiple empty spaces near the target object where the WMRA can park, and identifying the best position becomes crucial.  
In response to this challenge, the work of this paper is to obtain the optimal location for wheelchair parking by integrating OGM and IRM. Moreover, various experiments in this paper have also proven that it can effectively address the aforementioned challenges.  
Currently, for executing household tasks, the WMRA often uses separate planning and execution methods for navigation and grasping. The base is mainly responsible for completing the navigation task, while the arm is responsible for completing the grasping task. However, achieving coordination between them is difficult, resulting in coordination being a major issue, as shown in Figure 2.  
To solve the above problems, motion planning methods with process structure are often used, such as the EL-E assistive robot [15], which segments the navigation task into three parts, rough navigation, fine navigation, and final navigation, ultimately bringing the robot to a position 40 cm away from the task target. However, this method does not take into account the robotic arm's capabilities when selecting the parking location for the mobile base. As a result, the chosen parking location may not be suitable for the subsequent robotic arm task. Figure 3a illustrates the specific process.  
Figure 1: Previous work description [14].  
Figure 2: Task description: (**a**) top view of the task scenario; (**b**) task scenario. During the execution of the call elevator task, an optimal parking location must be selected to ensure the effective operation of the robotic arm. The chosen position must satisfy several conditions, including avoiding obstacles and providing an expanded operating range for the arm.  
To improve coordination in mobile manipulation and help the robotic arm better accomplish tasks, the optimal parking location for the mobile base must be found. The commonly used method is the optimization of base placement. However, when obstacles are present near the target object or the target object is on the surface of an obstacle, the optimal base placement may interfere with the obstacle, which would affect the effectiveness of base optimization. Therefore, this article proposes a combination of the OGM and IRM methods to study the problem of optimal base placement and environmental obstacle interference for the WMRA, as shown in Figure 3b.