  Planning motions to grasp an object in cluttered and uncertain environments
is a challenging task, particularly when a collision-free trajectory does not
exist and objects obstructing the way are required to be carefully grasped and
moved out. This paper takes a different approach and proposes to address this
problem by using a randomized physics-based motion planner that permits
robot-object and object-object interactions. The main idea is to avoid an
explicit high-level reasoning of the task by providing the motion planner with
a physics engine to evaluate possible complex multi-body dynamical
interactions. The approach is able to solve the problem in complex scenarios,
also considering uncertainty in the objects pose and in the contact dynamics.
The work enhances the state validity checker, the control sampler and the tree
exploration strategy of a kinodynamic motion planner called KPIECE. The
enhanced algorithm, called p-KPIECE, has been validated in simulation and with
real experiments. The results have been compared with an ontological
physics-based motion planner and with task and motion planning approaches,
resulting in a significant improvement in terms of planning time, success rate
and quality of the solution path.
