The need to combine task planning and motion planning in robotics is well understood. The
task planner generates a plan to solve the problem while the motion planner executes the actions
of the problem. The previous framework is applied in many state machines that solve
complex problems. But in this project we want to present an interface that communicates the
task planner layer and the motion planner layer, and updates the geometric information of the
environment to inform the task planner. This framework allows to solve complex tasks with
basic information of the goal, and replan whenever the motion could not be executed. All the
information of the problems is modelled as logical predicates.
The objective of this project is to generate a generic model of the environment, with a set of
feasible motions of the robot, and use this interface to solve many different planning problems
involving those actions, by just giving simple goals. The result is to make the robot more autonomous
and allow that any user could use it by giving simple orders.
Moreover this project presents the different frameworks and algorithms used to simulate
those actions in the robot such as: Sequential Quadratic Programming optimization, Rapidly
Random Exploring Tree (RRT) or SBPL global planning. It also shows an introduction to PDDL
language used to model the problem and the actions, and the Fast-Froward (FF) solver that is
the responsible to translate the problem as a graph and solve it.
Finally we test it on different experiments in simulation, by using the TIAGo platform of PAL
robotics. The results are promising and allow to dream in service robots solving complex tasks
simply computing and modelling basic actions
