Extending the iTaSC Constraint-based Robot Task Specification Framework to Time-Independent Trajectories and User-Configurable Task Horizons

In constraint-based programming, robot tasks are specified and solved as optimization problems with sets of constraints and one or multiple objective functions. In our previous work, we presented (i) a generic modeling approach for geometrically complex robot tasks, including the modeling of parametric uncertainty, in order to allow the robot task programmer to specify the optimization problem without explicitly writing down the different (possibly numerous and involved) constraint equations, and (ii) methods for solving these optimization problem online in the instantaneous case (reactive control), and offline in the non-instantaneous case (trajectory planning). This paper has two contributions. First, it extends our framework to include task constraints (e.g. tracking a curve) that are not given as explicit functions of time. These constraints are highly relevant in practice, for example to facilitate time-optimal path planning combined with other constraints. Second, it extends our framework to user-configurable task horizons when solving the optimization problem, to allow task programmers to make a trade-off between computational speed and (global) task optimality. Both of these novel framework extensions are illustrated by a time-optimal laser tracing experiment.