The development of domestic mobile manipulators for unconstrained environments has driven significant research recently. Robot Care Systems has been pioneering in developing a prototype of a mobile manipulator for elderly care. It has a 6 degrees of freedom robotic arm mounted on their flagship robot LEA, a non-holonomic differential drive platform. In order to utilize the navigation and manipulation capabilities of such mobile manipulators, robot placement algorithm that computes a favorable position and orientation of the mobile base is sought, which enables the end effector to reach a desired target. None of the existing approaches perform robot placement while ensuring a high chance of successful planning to target through a short path, while accounting for sensing and actuation errors typical in real world scenarios. This thesis presents a novel robot placement algorithm DeCOWA (Determining Commutation configuration using Optimization and Workspace Analysis) with these characteristics. Since the approach to robot placement is dependent upon the kind of mobile manipulation, a comparative study of sequential and full body methods is performed with respect to criteria important in domestic settings. Sequential mobile manipulation is found to be most suitable, for which a modular mobile manipulation framework encompassing motion planning and robot placement is presented. With sequential mobile manipulation, the ability to successfully reach a target depends upon the kinematic capabilities of the arm. Accordingly, robot placement with DeCOWA determines a favorable location for the arm, and corresponding platform orientation. To find the position of arm’s base, an offline manipulator workspace analysis is performed generating the Inverse Reachability and Planability maps. During online use, these maps are combined into an Inverse Fusion Map that ranks different locations based on the ability of the arm placed there to find a successful and short motion plan to target. This map is filtered to generate a set of feasible locations at the arm’s height. Through a ranked iterative search, a suitable collision free arm location is determined followed by minimization of the platform distance from robot’s current pose. This approach is evaluated against an unbiased random placement of robot near the target using a sample set of twenty scenes mimicking domestic settings. It is found that DeCOWA is able to generate commutation configurations in fraction of a second, that lead to a high planning success rate, a short path length, and account for goal tolerance of navigation. Also, its modularity allows to use several planability metrics, making it useful for domestic application.