STOMP: Stochastic Trajectory Optimization for Motion Planning
We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on generating noisy trajectories to explore the space around an initial (possibly infeasible) trajectory, which are then combined to produced an updated trajectory with lower cost. A cost function based on a combination of obstacle and smoothness cost is optimized in each iteration. No gradient information is required for the particular optimization algorithm that we use and so general costs for which derivatives may not be available (e.g. costs corresponding to constraints and motor torques) can be included in the cost function. We demonstrate the approach both in simulation and on a mobile manipulation system for unconstrained and constrained tasks. We experimentally show that the stochastic nature of STOMP allows it to overcome local minima that gradient-based methods like CHOMP can get stuck in.
Mrinal Kalakrishnan Sachin Chitta Evangelos Theodorou Peter Pastor Stefan Schaal
CLMC Laboratory, University of Southern California, Los Angeles, USA Willow Garage Inc.,Menlo Park,CA 94025,USA
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
4569-4574
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）