Efficient motion planning for manipulation robots in environments with deformable objects

The ability to plan their own motions and to reliably execute them is an important precondition for autonomous robots. In this paper, we consider the problem of planning the motion of a mobile manipulation robot in the presence of deformable objects. Our approach combines probabilistic roadmap planning with a physical deformation simulation system. Since the physical deformation simulation is computationally demanding, we use efficient Gaussian process regression to estimate the deformation cost for individual objects based on training examples. We generate the training data by employing a simulation system in a preprocessing step. Consequently, no simulations are needed during runtime. We implemented and tested our approach on a mobile manipulation robot. Our experiments show that the robot is able to accurately predict and thus consider the deformation cost its manipulator introduces to the environment during motion planning. Simultaneously, the computation time is substantially reduced compared to a system that employs physical simulations online.

Authors

• Barbara Frank
• Cyrill Stachniss
• Nichola Abdo
• Wolfram Burgard

Keywords

• Trajectory
• Robots
• Planning
• Deformable models
• Computational modeling
• Collision avoidance
• Training
• Path Planning
• Robot Manipulator
• Deformable Objects
• Robots In Environments
• Training Data
• Gaussian Process
• Kriging
• Objective Presentation
• Training Examples
• Mobile Robot
• Computational Demands
• Physical Simulation
• Simulated Deformation
• Mobile Manipulator
• Hyperparameters
• Finite Element Method
• Parametrized
• Regression Problem
• Translational Motion
• Regression Techniques
• Configuration Space
• Static Environment
• Covariance Function
• Trajectory Planning
• Gaussian Process Model
• Simulation Engine
• Collision Detection
• Robot Movement
• Trajectory Length
• Nodes In Space