The kinematic (KS) and algorithmic singularities (AS) in controlling robotic manipulators have been investigated intensively because they are not predictable or difficult to avoid. The problem with handling these singularities is an unnecessary performance reduction in the non-singular
region and the difficulty in performance tuning. In this paper, we propose a method of avoiding KS and AS by applying a task reconstruction approach while maximizing the task performance by calculating singularity measures. The proposed method is implemented by removing the component approaching
the singularity calculated by using a singularity measure in real-time. The outstanding feature of the proposed task reconstruction (TR) method is that it is based on a local TR as opposed to the local joint reconstruction of many other approaches. This method has a dynamic task priority assignment
feature which ensures system stability under singular regions due to the change of task priority. The TR method enables us to increase the task controller gain to improve the task performance, whereas this increase can destabilize the system for conventional algorithms in real experiments.
In addition, the physical meaning of tuning parameters is very straightforward. Hence, we can maximize task performance even near the singular region while simultaneously obtaining the singularity-free motion. The advantage of the proposed method is experimentally tested by using a 7-d. o.
f. spatial manipulator and the result shows that the new method improves the performance several times over the existing algorithms.