When first posed with the problem 15 × 15, we may generate the answer by applying a set of rules, e.g breaking the problem down into (10 + 5) × 15 and solving the subcomponents of these simpler multiplications first [2]. But after having solved this problem several times, we simply recall that the answer to 15 × 15 is 225. This distinction between generation and recall can also be applied to motor planning [2], as described in the next two sections.

This work presents a multimodal teleoperation framework that makes use of novel tools and techniques, such as: nonlinear teleoperators control, for ensuring position tracking in the presence of variable time-delays; relational positioning, for increasing operator performance on precise movement execution by visually and haptically displaying geometric constraints; and augmented reality, for visually combining real and virtual information in a compelling way. Experimental evidence is presented that validates the aptitude of the different components of the proposed framework.

One of the key factors that affect the success and efficiency of sampling-based path planners is the obtention of samples in the more relevant regions of the workspace. This is known as importance sampling, and different approaches have already been proposed in this direction. This paper proposes a novel method to bias sampling by means of geometric constraints that reduces the sampling space to sets of lower dimensional submanifolds. These constraints may be imposed by the kinematic structure of the actuation system, by the task specification, or provided by a human user as an intuitive way to include problem knowledge to the planner. The method has been implemented and tested on a probabilistic roadmap planner giving promising results. A variant using a deterministic sampling source is also reported.

This paper presents a framework that enables an operator to haptically and visually interact with a simulated dynamic environment subject to virtual holonomic constraints. The framework combines a geometric constraint solver with a constrained dynamics simulation engine that controls an admittance-type haptic display. This system takes on relevant issues in the context of assisted teleoperated tasks, from providing an intuitive interface for creating and combining virtual constraints, to haptically displaying rigid motion constraints in simulated environments subject to desired inertial dynamics. Two experiments carried out using the Cobotic Hand Controller haptic display are presented.

This paper presents a relational positioning methodology that allows to restrict totally or partially the movements of an object by specifying its allowed positions in terms of a set of intuitive geometric constraints. In order to derive these positions, ageometric constraint solvermust be used. To this end, positioning mobile with respect to fixed (PMF), a geometric constraint solver for the relational positioning of rigid objects in free space is introduced. The solver exploits the fact that, in a set of geometric constraints, the rotational component can often be separated from the translational one and solved independently. PMF may be used as an interface for specifying offline-programmed robot tasks, as well as for assisting the execution of teleoperated tasks requiring constrained movements. Examples describing both the solver's operation and typical applications are discussed.