## 1 Introduction  
Currently, the global population is continuously increasing and care for elderly and disabled individuals is becoming an increasingly greater pressure. In this context, assistive technology for the elderly and disabled is constantly evolving, and various assistive devices are being innovated. Among them, the WMRA combines the electric wheelchair and robotic arm, allowing for indoor movement and various operations in the home environment, effectively reducing care pressure and improving the quality of life for elderly and disabled individuals.  
However, the WMRA still faces many problems during use. The traditional control method for electric wheelchairs and robotic arms is through joystick remote control. When performing various household tasks, all degrees of freedom of the electric wheelchair and robotic arm need to be controlled. Therefore, it is challenging to control the WMRA through a joystick, especially for users with a certain degree of disability. Traditional WMRA operation involves many steps and has low human-machine interaction efficiency, which greatly hinders its application.  
In recent years, many studies have focused on improving the coordination between the mobile platform and arm motion planning. By optimizing the base placement to assist the robotic arm in completing tasks, the coordination between the mobile platform and the arm can be improved. The optimal base placement can be selected by calculating the inverse reachability map of the robotic arm, which evaluates the impact of different base placements on the robotic arm task. However, this method is not applicable when obstacles are near the target task. When the target object is on a wall or tabletop, the optimal base placement may interfere with the obstacle, preventing the mobile platform from reaching the designated position. There are currently many SLAM methods available for detecting and collecting obstacles in the household environment. SLAM uses LiDAR or cameras to collect information and combines it with odometer information to construct an environmental map, finally storing the information on environmental obstacles in the form of OGM. This method allows mobile robots to avoid obstacles based on environmental information and reach the user-specified two-dimensional target point. However, the user-specified target point may not be suitable for subsequent robotic arm task completion, affecting the completion efficiency of the robotic arm and the coordination of the mobile robotic arm.  
To address these issues, this paper proposes a novel approach to optimize the base placement of the WMRA equipped with a robotic arm, by integrating the occupied grid map and inverse reachability map. To evaluate the algorithm's performance, a variety of household tasks were performed. Firstly, the system was constructed, and the human-machine interaction and visual systems were integrated. Secondly, the household environment was collected using the SLAM algorithm and stored using the occupied grid map. Then, the reachable capability of the robotic arm was calculated to generate an optimal distribution of base placements. These positions were then compared and filtered against the household environment information, resulting in the completion of the OGM-IRM optimization method of base placement.