Cartesian-space control and dextrous manipulation for multi-fingered tendon-driven hand

2014 ◽  
Author(s):  
Taylor Niehues ◽  
Julia Badger ◽  
Myron Diftler ◽  
Ashish D. Deshpande
Keyword(s):  
Dextrous Manipulation ◽  
Cartesian Space

Position regulation in Cartesian space of a class of inextensible soft continuum manipulators with pneumatic actuation

Mechatronics ◽  
2021 ◽  
Vol 76 ◽  
pp. 102573
Author(s):  
Enrico Franco ◽  
Arnau Garriga Casanovas ◽  
Jacky Tang ◽  
Ferdinando Rodriguez y Baena ◽  
Alessandro Astolfi
Keyword(s):  
Pneumatic Actuation ◽  
Cartesian Space ◽  
Continuum Manipulators ◽  
Position Regulation

Evaluation and Representation of the Theoretical Orientation Workspace of the Gough–Stewart Platform

2009 ◽  
Vol 1 (2) ◽  
Author(s):  
Qimi Jiang ◽  
Clément M. Gosselin
Keyword(s):  
Theoretical Orientation ◽  
Robotic Manipulators ◽  
Stewart Platform ◽  
Closed Region ◽  
Leg Length ◽  
Fixed Frame ◽  
Cartesian Space ◽  
Orientation Workspace

The evaluation and representation of the orientation workspace of robotic manipulators is a challenging task. This work focuses on the determination of the theoretical orientation workspace of the Gough–Stewart platform with given leg length ranges [ρimin,ρimax]. By use of the roll-pitch-yaw angles (ϕ,θ,ψ), the theoretical orientation workspace at a prescribed position P0 can be defined by up to 12 workspace surfaces. The defined orientation workspace is a closed region in the 3D orientation Cartesian space Oϕθψ. As all rotations R(x,ϕ), R(y,θ), and R(z,ψ) take place with respect to the fixed frame, any point of the defined orientation workspace provides a clear measure for the platform to, respectively, rotate in order around the (x,y,z) axes of the fixed frame. An algorithm is presented to compute the size (volume) of the theoretical orientation workspace and intersectional curves of the workspace surfaces. The defined theoretical orientation workspace can be applied to determine a singularity-free orientation workspace.

An Algorithm for Generation of Coordinated Robot Trajectories in Cartesian Space

2013 ◽  
Vol 196 ◽  
pp. 169-180 ◽  
Author(s):  
Adam Słota
Keyword(s):  
Virtual Environment ◽  
Relative Position ◽  
Accuracy Analysis ◽  
Simulation Environment ◽  
Coordinated Motion ◽  
Generation Algorithm ◽  
Cartesian Space ◽  
Position And Orientation ◽  
End Effectors ◽  
Trajectory Generation Algorithm

In the paper a trajectory generation algorithm for two robots’ coordinated motion is presented. Two instances of the algorithm, each for one robot, run in the same time and calculate trajectories’ position and orientation coordinates. Initial and end robots’ end-effectors poses are defined and values of linear and angular speeds are programmed. To minimize relative position and orientation errors an idea of corrective motion is introduced. Trajectory coordinates are calculated as the sum of programmed and corrective motion. The algorithm was implemented in a simulation environment and results of simulation are presented. Static accuracy analysis for general case and stability verification for fixed values of robots’ parameters are described. Finally, an outline of proposed procedure of building a virtual environment for reachability verification and collision checking is presented.

scholarly journals Real Evaluations Tractability using Continuous Goal-Directed Actions in Smart City Applications

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3818
Author(s):  
Raul Fernandez-Fernandez ◽  
Juan Victores ◽  
David Estevez ◽  
Carlos Balaguer
Keyword(s):  
Particle Swarm Optimization ◽  
Smart City ◽  
Robot Programming ◽  
Complex Environments ◽  
Swarm Optimization ◽  
Geometrical Characteristics ◽  
Cartesian Space ◽  
Simulated Environment ◽  
Velocity Constraints ◽  
Fitness Inheritance

One of the most important challenges of Smart City Applications is to adapt the system to interact with non-expert users. Robot imitation frameworks aim to simplify and reduce times of robot programming by allowing users to program directly through action demonstrations. In classical robot imitation frameworks, actions are modelled using joint or Cartesian space trajectories. They accurately describe actions where geometrical characteristics are relevant, such as fixed trajectories from one pose to another. Other features, such as visual ones, are not always well represented with these pure geometrical approaches. Continuous Goal-Directed Actions (CGDA) is an alternative to these conventional methods, as it encodes actions as changes of any selected feature that can be extracted from the environment. As a consequence of this, the robot joint trajectories for execution must be fully computed to comply with this feature-agnostic encoding. This is achieved using Evolutionary Algorithms (EA), which usually requires too many evaluations to perform this evolution step in the actual robot. The current strategies involve performing evaluations in a simulated environment, transferring only the final joint trajectory to the actual robot. Smart City applications involve working in highly dynamic and complex environments, where having a precise model is not always achievable. Our goal is to study the tractability of performing these evaluations directly in a real-world scenario. Two different approaches to reduce the number of evaluations using EA, are proposed and compared. In the first approach, Particle Swarm Optimization (PSO)-based methods have been studied and compared within the CGDA framework: naïve PSO, Fitness Inheritance PSO (FI-PSO), and Adaptive Fuzzy Fitness Granulation with PSO (AFFG-PSO). The second approach studied the introduction of geometrical and velocity constraints within the CGDA framework. The effects of both approaches were analyzed and compared in the “wax” and “paint” actions, two CGDA commonly studied use cases. Results from this paper depict an important reduction in the number of required evaluations.

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