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A Forward / Inverse Motor Controller for Cognitive Robotics

  • Vishwanathan Mohan
  • Pietro Morasso
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4131)

Abstract

Before making a movement aimed at achieving a task, human beings either run a mental process that attempts to find a feasible course of action (at the same time, it must be compatible with a number of internal and external constraints and near-optimal according to some criterion) or select it from a repertoire of previously learned actions, according to the parameters of the task. If neither reasoning process succeeds, a typical backup strategy is to look for a tool that might allow the operator to match all the task constraints. A cognitive robot should support a similar reasoning system. A central element of this architecture is a coupled pair of controllers: FMC (forward motor controller: it maps tentative trajectories in the joint space into the corresponding trajectories of the end-effector variables in the workspace) and IMC (inverse motor controller: it maps desired trajectories of the end-effector into feasible trajectories in the joint space). The proposed FMC/IMC architecture operates with any degree of redundancy and can deal with geometric constraints (range of motion in the joint space, internal and external constraints in the workspace) and effort-related constraints (range of torque of the actuators, etc.). It operates by alternating two basic operations: 1) relaxation in the configuration space (for reaching a target pose); 2) relaxation in the null space of the kinematic transformation (for producing the required interaction force). The failure of either relaxation can trigger a higher level of reasoning. For both elements of the architecture we propose a closed-form solution and a solution based on ANNs.

Keywords

Joint Space Null Space External Constraint Internal Constraint Joint Limit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Vishwanathan Mohan
    • 1
    • 2
  • Pietro Morasso
    • 1
  1. 1.Neurolab, DISTUniversity of GenovaGenovaItaly
  2. 2.Doctoral School on Humanoid TechnologiesItalian Institute of TechnologyGenovaItaly

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