A Forward / Inverse Motor Controller for Cognitive Robotics
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.
KeywordsJoint Space Null Space External Constraint Internal Constraint Joint Limit
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