## 2 The Proposed Approach  
Generally, in kinematic robotic tasks, the end effector of a robot is required to reach a number of task points or to follow a given path with its end effector, presenting a prescribed orientation of the end effector local frame. These points may present task points where an action is executed or present intermediate task points along a curve/path to be followed by the robot's tool in a specific sequence, while the end effector exhibits smooth motion along the path under prescribed acceleration and velocity requirements.  
As such, a means to achieve an increased kinematic performance during task execution, commonly utilized, is the placement of the kinematic task to an optimal location within the robot's workspace [7]. In the case of the method presented, kinematic tasks are described as collections of task points that the end effector must visit and move along under given orientations.  
1. Layout Design  
During this initial stage, the derivation of the kinematic tasks optimal location is computationally derived, having as an input the collection of the task points that compose the task. As such, a Local Coordinate System (LCS) is considered to be placed to the initial task point to be visited, which depicts the orientation and location of the end effector at it. All other task points are therefore referenced to the LCS. Therefore, the inputs for this stage are the position and orientation of the local coordinate systems, relative to the LCS, and the sequence with which they are to visited by the end effector during the execution of the task.  
In order to the better interpolate the motion between the task points, intermediate points are initially determined between each consecutive task points pair. The designer is allowed to set the number of intermediate points to be placed as per task requirements.  
Another process in the computational part involves the determination of the best possible paths between consecutive task frames. Therefore, a task-based kinematic performance index is developed so as to allow the quantification of the required kinematic performance aspect to be optimized during the execution of the task. The process is repeated for each pair of consecutive task frame and is listed as follows:  
* Initially, the possible configurations (postures) of the manipulator at each task and intermediate frame are determined by the solution of the inverse kinematics problem.
* The possible singularity free paths with respect to the end effector's motion between task frames are determined in the manipulator's configuration space.
* The task based index value is evaluated for the derived singular free paths.
* Based on the index value along the path the best path is determined.  
Finally, a Genetic Algorithm is employed to conduct the search for the optimal location of the kinematic task. At the end of the search process the optimal location of the kinematic task is determined.  
2. Experimental Implementation  
The experimental implementation inputs are the determined optimal position and orientation of the LCS with respect to the BCS (Base Coordinate System of the Robot), the referencing of the task frames to the LCS and the sequence with which the end effector approaches the task frames. These are used to setup the task execution to the robotic workcell that will undertake the task execution.