Trajectory planning for optimal robot catching in real-time
Many real-world tasks require fast planning of highly dynamic movements for their execution in realtime. The success often hinges on quickly finding one of the few plans that can achieve the task at all. A further challenge is to quickly find a plan which optimizes a desired cost. In this paper, we will discuss this problem in the context of catching small flying targets efficiently. This can be formulated as a non-linear optimization problem where the desired trajectory is encoded by an adequate parametric representation. The optimizer generates an energy-optimal trajectory by efficiently using the robot kinematic redundancy while taking into account maximal joint motion, collision avoidance and local minima. To enable the resulting method to work in real-time, examples of the global planner are generalized using nearest neighbour approaches, Support Vector Machines and Gaussian process regression, which are compared in this context. Evaluations indicate that the presented method is highly efficient in complex tasks such as ball-catching.
Roberto Lampariello Duy Nguyen-Tuong Claudio Castellini Gerd Hirzinger Jan Peters
Institute of Robotics and Mechatronics (DLR),82234 Weβling,Germany Max Planck Institute for Biological Cybernetics,72076 T(u)bingen,Germany
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
3719-3726
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）