Optimal, sampling-based manipulation planning

When robots perform manipulation tasks, they need to determine their own movement, as well as how to grasp and release an object. Reasoning about the motion of the robot and the object simultaneously leads to a multi-modal planning problem in a high-dimensional configuration space. In this paper we propose an asymptotically optimal manipulation planner. Our approach extends optimal sampling-based roadmap planners to efficiently explore the configuration space of the robot and the object. We prove probabilistic completeness and global, asymptotic optimality. Extensive simulations of a typical pick-and-place scenario show that our approach significantly outperforms a (nonoptimal) state-of-the-art approach. We implemented our planner on a real manipulator and were able to compute high quality solutions in less than a second.

Authors

• Philipp S. Schmitt
• Werner Neubauer
• Wendelin Feiten
• Kai M. Wurm
• Georg von Wichert
• Wolfram Burgard

Keywords

• Planning
• Nickel
• Manipulators
• Kinematics
• Probabilistic logic
• Buildings
• Manipulation Planning
• Manipulation Tasks
• Configuration Space
• Robot Motion
• Planning Problem
• Asymptotic Optimality
• Heuristic
• Single Object
• Path Planning
• Nearest Neighbor Search
• Robot Manipulator
• Contact Conditions
• Collision Detection
• Static Environment
• Inverse Kinematics
• Random Configuration
• Rejection Sampling
• Joint Configuration
• Robot Configuration
• Path Segment
• Rapidly-exploring Random Tree
• Benchmark Tasks