# Integrated Motion Planning for Assembly Task with Part Manipulation Using Re-Grasping
## 1 Introduction  
The role of robots in the industry environment is growing with the advancement in the research tools in the robotic industry to improve the production efficiency. The robots are commonly used in assembly line in the industry to effectively perform repetitive assembly operations [1] and tele-manipulation in remote sites [2, 3]. The combinatorial based approaches for assembly planning are widely addressed in literature [4, 5] however, these are used only for assembly sequence planning for the parts to be assembled. Also, there is a recent research trend of using machine leaching based approached in robotic assembly such as learning by demonstration [6] but currently these can handle only simple operations such as inserting peg in a hole, fastening the screw etc. In order to make the robots capable to perform a task autonomously, the planning algorithms are developed (for review [7, 8]). However, the assembling task is quite challenging for robots. One of the recent trends in the robotics research is to make the robots capable of planning and performing the assembly task autonomously. This multi-disciplinarytask includes part recognition, part pose estimation, assembly planning, grasp planning, motion planning of part, and robot etc. For example integrated robotic systems such as [9; 10] were developed to perform manipulation however, the main focus was the development of software architecture. These platforms still lack autonomously handling the complex situations such as performing an assembly operation such as, in [9] the part is grasped by caging it but for assembly task, the planners need to choose a grasp carefully among possibly multiple feasible grasps which not only consider the pose and geometry of current part but also the pre-assembled parts in the assembly. This paper focuses on the integrated grasp and motion planning of robot to perform an assembly task. Motion planning for a robot with high degrees of freedom in the presence of the obstacles in the environment is P-space hard [7]. For this kind of problems probabilistic approaches such as Probabilistic Road Map (PRM), rapidly exploring random tree (RRT), Expansive Spaces Tree (EST), Single Query Bi-directional Probabilistic Road Map (SBL) etc., are widely used to plan collision free path [7]. RRT based approaches are quite successful for last two decades due to their simplicity and reliability [11]. In [12], an RRT-based integrated approach is proposed to plan robot motion along with grasp planning as Grasp-RRT. This integrated approach searches a feasible grasp pose of hand while planning the robot motion. However, Grasp-RRT only considers the grasp problem for individual pieces but does not consider the assembly task constraints. Also, Grasp-RRT finds a grasp in a trial-and error manner. Instead, in this paper, we assume that a set of feasible grasps is already given, and then the algorithm first evaluates these grasps to check whether they can be used at the given assembly step. This results in a subset of the initially given grasps (but still possibly containing more than one grasp). Later on, while planning the robot motion, the planner tries to reach to these grasps using a method similar to Grasp-RRT. Note that the algorithm does not choose a specific grasp prior to planning, but the choice is made during the planning of the robot manipulator. Part manipulation and re-grasping has been addressed in literature for Single and dual-arm robot [13; 14; 15; 16]. In general, the part manipulation is performed in configuration space (_C-Space_) jointly defined for the robot, and the object to be manipulated. The C-space search based part manipulation involves high dimensional C-space search which is quite computationally expensive. On the other hand, we propose a part manipulation approach based on the orientation graph search in the task space.