Using Gaussian process regression for efficient motion planning in environments with deformable objects

摘要

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 in the environment. Our approach combines probabilistic roadmap planning with a deformation simulation system. Since the physical deformation simulation is computationally demanding, we use an efficient variant of 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 performs physical simulations online. 1