5,392 research outputs found
On-line Joint Limit Avoidance for Torque Controlled Robots by Joint Space Parametrization
This paper proposes control laws ensuring the stabilization of a time-varying
desired joint trajectory, as well as joint limit avoidance, in the case of
fully-actuated manipulators. The key idea is to perform a parametrization of
the feasible joint space in terms of exogenous states. It follows that the
control of these states allows for joint limit avoidance. One of the main
outcomes of this paper is that position terms in control laws are replaced by
parametrized terms, where joint limits must be avoided. Stability and
convergence of time-varying reference trajectories obtained with the proposed
method are demonstrated to be in the sense of Lyapunov. The introduced control
laws are verified by carrying out experiments on two degrees-of-freedom of the
humanoid robot iCub.Comment: 8 pages, 4 figures. Submitted to the 2016 IEEE-RAS International
Conference on Humanoid Robot
Parametric motion control of robotic arms: A biologically based approach using neural networks
A neural network based system is presented which is able to generate point-to-point movements of robotic manipulators. The foundation of this approach is the use of prototypical control torque signals which are defined by a set of parameters. The parameter set is used for scaling and shaping of these prototypical torque signals to effect a desired outcome of the system. This approach is based on neurophysiological findings that the central nervous system stores generalized cognitive representations of movements called synergies, schemas, or motor programs. It has been proposed that these motor programs may be stored as torque-time functions in central pattern generators which can be scaled with appropriate time and magnitude parameters. The central pattern generators use these parameters to generate stereotypical torque-time profiles, which are then sent to the joint actuators. Hence, only a small number of parameters need to be determined for each point-to-point movement instead of the entire torque-time trajectory. This same principle is implemented for controlling the joint torques of robotic manipulators where a neural network is used to identify the relationship between the task requirements and the torque parameters. Movements are specified by the initial robot position in joint coordinates and the desired final end-effector position in Cartesian coordinates. This information is provided to the neural network which calculates six torque parameters for a two-link system. The prototypical torque profiles (one per joint) are then scaled by those parameters. After appropriate training of the network, our parametric control design allowed the reproduction of a trained set of movements with relatively high accuracy, and the production of previously untrained movements with comparable accuracy. We conclude that our approach was successful in discriminating between trained movements and in generalizing to untrained movements
Fuzzy logic control of telerobot manipulators
Telerobot systems for advanced applications will require manipulators with redundant 'degrees of freedom' (DOF) that are capable of adapting manipulator configurations to avoid obstacles while achieving the user specified goal. Conventional methods for control of manipulators (based on solution of the inverse kinematics) cannot be easily extended to these situations. Fuzzy logic control offers a possible solution to these needs. A current research program at SRI developed a fuzzy logic controller for a redundant, 4 DOF, planar manipulator. The manipulator end point trajectory can be specified by either a computer program (robot mode) or by manual input (teleoperator). The approach used expresses end-point error and the location of manipulator joints as fuzzy variables. Joint motions are determined by a fuzzy rule set without requiring solution of the inverse kinematics. Additional rules for sensor data, obstacle avoidance and preferred manipulator configuration, e.g., 'righty' or 'lefty', are easily accommodated. The procedure used to generate the fuzzy rules can be extended to higher DOF systems
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