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Physics-based motion planning is a challenging task, since it requires thecomputation of the robot motions while allowing possible interactions with(some of) the obstacles in the environment. Kinodynamic motion plannersequipped with a dynamic engine acting as state propagator are usually used forthat purpose. The difficulties arise in the setting of the adequate forces forthe interactions and because these interactions may change the pose of themanipulatable obstacles, thus either facilitating or preventing the finding ofa solution path. The use of knowledge can alleviate the stated difficulties.This paper proposes the use of an enhanced state propagator composed of adynamic engine and a low-level geometric reasoning process that is used todetermine how to interact with the objects, i.e. from where and with whichforces. The proposal, called \k{appa}-PMP can be used with any kinodynamicplanner, thus giving rise to e.g. \k{appa}-RRT. The approach also includes apreprocessing step that infers from a semantic abstract knowledge described interms of an ontology the manipulation knowledge required by the reasoningprocess. The proposed approach has been validated with several examplesinvolving an holonomic mobile robot, a robot with differential constraints anda serial manipulator, and benchmarked using several state-of-the artkinodynamic planners. The results showed a significant difference in the powerconsumption with respect to simple physics-based planning, an improvement inthe success rate and in the quality of the solution paths.

$\kappa$-PMP: Enhancing Physics-based Motion Planners with Knowledge-based Reasoning