Optimalisasi Ukuran Manipulabilitas Robot Stanford Menggunakan Metode Pseudo-inverse

Robot is one of the most important element in the industrial world which has been growing very rapidly. Stanford robot arm is one of robot that use in industry, it has five degrees of freedom (DOF). Movement of the robot arm in his workspace called manipulability or manipulability measure. More the optimal manipulability measure manipulator, the more movement of the robotic arm will be more flexible in his workspace. The purpose of this research are to get knowledge and learn how to solve inverse kinematics using the Pseudo-Inverse at Stanford robot and to compare the characteristic of redundant and nonredundant manipulability measure optimization at Stanford robot arm. This simulations are used to determine manipulability measure optimization by using Matlab 7.6 software. This research produced that the maximum value of the redundancy manipulabilty measure is 7,391, while the value manipulability measure without redundancy was only 4,207. Simulations with redundancy manipulability measure value is high than the value of manipulability measure without redundancy. It means that manipulability measure at Stanford robot that use the redundancy is more optimal than the Stanford robot that does not use redundancy. Thus proved that for optimize manipulability measure is use redundancy

Manipulability analysis of a snake robot without lateral constraint for head position control

Two dynamic manipulability criteria of a snake robot with sideways slipping are proposed with the application to head trajectory tracking control in mind. The singular posture, which is crucial in head tracking control, is characterized by the manipulability and examined for families of typical robot shapes. Differences in the singular postures from those of the robot with lateral constraints, which have not been clear in previous studies, are clarified in the analysis. In addition to the examination of local properties using the concept of manipulability, we discuss the effect of isotropic friction as a global property. It is well known that, at least empirically, a snake robot needs anisotropy in friction to move by serpentine locomotion if there are no objects for it to push around. From the point of view of integrability, we show one of the necessary conditions for uncontrollability is satisfied if the friction is isotropic

On the manipulability of dual cooperative robots

The definition of manipulability ellipsoids for dual robot systems is given. A suitable kineto-static formulation for dual cooperative robots is adopted which allows for a global task space description of external and internal forces, and relative velocities. The well known concepts of force and velocity manipulability ellipsoids for a single robot are formally extended and the contributions of the two single robots to the cooperative system ellipsoids are illustrated. Duality properties are discussed. A practical case study is developed

Geometry-aware Manipulability Learning, Tracking and Transfer

Body posture influences human and robots performance in manipulation tasks, as appropriate poses facilitate motion or force exertion along different axes. In robotics, manipulability ellipsoids arise as a powerful descriptor to analyze, control and design the robot dexterity as a function of the articulatory joint configuration. This descriptor can be designed according to different task requirements, such as tracking a desired position or apply a specific force. In this context, this paper presents a novel \emph{manipulability transfer} framework, a method that allows robots to learn and reproduce manipulability ellipsoids from expert demonstrations. The proposed learning scheme is built on a tensor-based formulation of a Gaussian mixture model that takes into account that manipulability ellipsoids lie on the manifold of symmetric positive definite matrices. Learning is coupled with a geometry-aware tracking controller allowing robots to follow a desired profile of manipulability ellipsoids. Extensive evaluations in simulation with redundant manipulators, a robotic hand and humanoids agents, as well as an experiment with two real dual-arm systems validate the feasibility of the approach.Comment: Accepted for publication in the Intl. Journal of Robotics Research (IJRR). Website: https://sites.google.com/view/manipulability. Code: https://github.com/NoemieJaquier/Manipulability. 24 pages, 20 figures, 3 tables, 4 appendice

Learning Singularity Avoidance

With the increase in complexity of robotic systems and the rise in non-expert users, it can be assumed that task constraints are not explicitly known. In tasks where avoiding singularity is critical to its success, this paper provides an approach, especially for non-expert users, for the system to learn the constraints contained in a set of demonstrations, such that they can be used to optimise an autonomous controller to avoid singularity, without having to explicitly know the task constraints. The proposed approach avoids singularity, and thereby unpredictable behaviour when carrying out a task, by maximising the learnt manipulability throughout the motion of the constrained system, and is not limited to kinematic systems. Its benefits are demonstrated through comparisons with other control policies which show that the constrained manipulability of a system learnt through demonstration can be used to avoid singularities in cases where these other policies would fail. In the absence of the systems manipulability subject to a tasks constraints, the proposed approach can be used instead to infer these with results showing errors less than 10^-5 in 3DOF simulated systems as well as 10^-2 using a 7DOF real world robotic system

Fast Manipulability Maximization Using Continuous-Time Trajectory Optimization

A significant challenge in manipulation motion planning is to ensure agility in the face of unpredictable changes during task execution. This requires the identification and possible modification of suitable joint-space trajectories, since the joint velocities required to achieve a specific endeffector motion vary with manipulator configuration. For a given manipulator configuration, the joint space-to-task space velocity mapping is characterized by a quantity known as the manipulability index. In contrast to previous control-based approaches, we examine the maximization of manipulability during planning as a way of achieving adaptable and safe joint space-to-task space motion mappings in various scenarios. By representing the manipulator trajectory as a continuous-time Gaussian process (GP), we are able to leverage recent advances in trajectory optimization to maximize the manipulability index during trajectory generation. Moreover, the sparsity of our chosen representation reduces the typically large computational cost associated with maximizing manipulability when additional constraints exist. Results from simulation studies and experiments with a real manipulator demonstrate increases in manipulability, while maintaining smooth trajectories with more dexterous (and therefore more agile) arm configurations.Comment: In Proceedings of the IEEE International Conference on Intelligent Robots and Systems (IROS'19), Macau, China, Nov. 4-8, 201

Handling robot constraints within a Set-Based Multi-Task Priority Inverse Kinematics Framework

Set-Based Multi-Task Priority is a recent framework to handle inverse kinematics for redundant structures. Both equality tasks, i.e., control objectives to be driven to a desired value, and set-bases tasks, i.e., control objectives to be satisfied with a set/range of values can be addressed in a rigorous manner within a priority framework. In addition, optimization tasks, driven by the gradient of a proper function, may be considered as well, usually as lower priority tasks. In this paper the proper design of the tasks, their priority and the use of a Set-Based Multi-Task Priority framework is proposed in order to handle several constraints simultaneously in real-time. It is shown that safety related tasks such as, e.g., joint limits or kinematic singularity, may be properly handled by consider them both at an higher priority as set-based task and at a lower within a proper optimization functional. Experimental results on a 7DOF Jaco$^2