Control-Aware Motion Planning for Task-Constrained Aerial Manipulation

This letter presents a new method to address the problem of task-constrained motion planning for aerial manipulators. We propose a control-aware planner based on the paradigm of tight coupling between planning and control. Such paradigm is especially useful in aerial manipulation, where the separation between planning and control is not advisable. The proposed sampling based motion planner uses a controller composed of a second-order inverse kinematics algorithm and a dynamic tracker, as a local planner, thus allowing a more natural consideration of the closed-loop system dynamics. For task-constrained motions, this method let us to sample directly in the reduced and more relevant task space, predict the behavior of the controller avoiding motions that bring to singularities or large tracking errors, and guarantee the correct execution of the maneuver. The method is tested in simulation for a multidirectional-thrust vehicle endowed with a 2-DoF manipulator. The proposed approach is very general, and could be applied to ground and underwater robotic systems to perform manipulation or inspection tasks.