Barbara Frank

	home		publications		links

Motion Planning in Environments with Deformable Objects

Abstract

The ability to plan their own motions and to reliably execute them is an important precondition for truly autonomous robots. In our work, we consider the problem of motion planning for robots in environments with non-rigid obstacles such as curtains or plants. We combine probabilistic roadmap planning with a physical simulation of object deformations to determine a path that optimizes the trade-off between the deformation cost and the distance to be traveled. Our approach utilizes Finite Element theory for computing the deformation cost. Since the high computational requirements of the corresponding simulation prevent this method from being applicable online, we approximate a deformation cost function for each object in a preprocessing step. This cost function allows us to estimate the deformation costs of arbitrary paths through the objects. It is used to evaluate the trajectories generated by the roadmap planner online.

Motion Planning for a wheeled Robot

		In this simulated planning example, the deformation of rubber ducks is more expensive than the deformation of curtains. Accordingly, the robot chooses a path that avoids the rubber ducks. Query Time: 0.13s.

		This real world experiment demonstrates how our robot Albert navigates in an environment with deformable curtains. Furthermore, it shows how the robot performs a basic collision avoidance and is able to distinguish between collisions with the curtain and impending collisions with dynamic obstacles. Query Time: 0.1s.

Motion Planning for a Manipulation Robot

Planning Example 1: The robot has to move its endeffector to the target position behind the deformable teddy bear (indicated in red). The left image shows the shortest path to the goal while the right image shows the computed path that trades off path and deformation cost. The video illustrates the execution of these paths in our deformation simulation.

Planning Example 2: The robot has to move its endeffector from its current position to the target position left of the deformable teddy bear (indicated in red). The left image shows the shortest path to the goal while the right image shows the computed path that trades off path and deformation cost. The video illustrates the execution of these paths in our deformation simulation.