• Journal of Institute of Control, Robotics and Systems
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• v.10 no.10
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• pp.855-868
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• 2004

There are several types of singularities in controlling robotic manipulators: kinematic singularity, algorithmic singularity, semi-kinematic singularity, semi-algorithmic singularity, and representation singularity. The kinematic and algorithmic singularities have been investigated intensively because they are not predictable or difficult to avoid. The problem with these singularities is an unnecessary performance reduction in non-singular region and the difficulty in performance tuning. Tn this paper, we propose a method of avoiding kinematic and algorithmic singularities 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 singularity measure in real time. The outstanding feature of the proposed task reconstruction method (TR-method) is that it is based on a local task reconstruction as opposed to the local joint reconstruction of many other approaches. And, this method has dynamic task priority assignment feature which ensures the system stability under singular regions owing 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 the 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 the 7-dof spatial manipulator, and the result shows that the new method improves the performance several times over the existing algorithms.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1587-1592
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• 2003

There are three kinds of singularity in controlling redundant manipulators. Kinematic, algorithmic and representation singularities are those. If manipulators fall into any singularity without proper action to avoid it, the control system must go away from our desire, and we can meet a dangerous situation. Hence, we have to deal the singularities very carefully. In this paper, we describe an on-line solution for avoiding the occurrence of both algorithmic and kinematic singularities in task-priority based kinematic controllers of robotic manipulators. Representation singularity can be easily avoided by using proper representation algorithm, so, in this paper, we only consider kinematic and algorithmic singularities. The proposed approach uses a desired task reconstruction and a successive task projection in order to maintain the measure for singularity over a user defined minimum value. It shows a gain in performance and a better task error especially when working in proximity of singular configurations. It is particularly suitable for autonomous systems where an off-line trajectory control scheme is often not applicable. The advantage and performance of the proposed controller is verified by simulation works. And, the experiment with real manipulator is remaining for the future works.

• The Journal of Korea Robotics Society
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• v.13 no.2
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• pp.129-141
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• 2018

This paper proposes a unified framework that overcomes four motion constraints including joint limit, kinematic singularity, algorithmic singularity and obstacles. The proposed framework is based on our previous works which can insert or remove tasks continuously using activation parameters and be applied to avoid joint limit and singularity. Additionally, we develop a method for avoiding obstacles and combine it into the framework to consider four motion constraints simultaneously. The performance of the proposed framework was demonstrated by simulation tests with considering four motion constraints. Results of the simulations verified the framework's effectiveness near joint limit, kinematic singularity, algorithmic singularity and obstacles. We also analyzed sensitivity of our algorithm near singularity when using closed loop inverse kinematics depending on magnitude of gain matrix.

• Proceedings of the KIEE Conference
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• 1992.07a
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• pp.386-390
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• 1992

Algorithmic or kinematic singularities are inevitably a introduced if optimality criteria or augmented kinematic equations are used to resolve the redundancy of almost any manipulator with rotary joints. In this paper, a sufficient condition for a singularity-free optimal solution of the kinematic control of a redundant manipulator is derived and, specifically, algorithmic singularities are analyzed for optimality-based methods. A singularity-free space (SFS) to characterize the performance of a secondary task for a redundant manipulator using the sufficient condition for a redundant manipulator is defined. The SFS is a set of regions classified by the loci of configurations satisfying the inflection condition for manipulability measure in the Configuration space. Using SFS, the topological property of the Configuration space and the invertible workspace without singularities are analyzed.

• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.42-54
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• 2017

Using an inverse of the geometric Jacobian matrix is one of the most popular ways to control robot manipulators, because the Jacobian matrix contains the relationship between joint space velocities and operational space velocities. However, the control algorithm based on Jacobian matrix has algorithmic singularities: The robot manipulator becomes unstable when the Jacobian matrix loses rank. To solve this problem, various methods such as damped and filtered inverse have been proposed, but comparative studies to evaluate the performance of these algorithms are insufficient. Thus, this paper deals with a comparative analysis of six representative singularity avoidance algorithms: Damped Pseudo Inverse, Error Damped Pseudo Inverse, Scaled Jacobian Transpose, Selectively Damped Inverse, Filtered Inverse, and Task Transition Method. Especially, these algorithms are verified through computer simulations with a virtual model of a humanoid robot, THORMANG, in order to evaluate tracking error, computational time, and multiple task performance. With the experimental results, this paper contains a deep discussion about the effectiveness and limitations of each algorithm.