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Trajectory Planning for Optimal Robot Catching in Real-Time
Many real-world tasks require fast planning of highly dynamic movements for their execution in real-time. 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.
@inproceedings{7048, title = {Trajectory Planning for Optimal Robot Catching in Real-Time}, journal = {Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2011)}, abstract = {Many real-world tasks require fast planning of highly dynamic movements for their execution in real-time. 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.}, pages = {3719-3726 }, publisher = {IEEE}, organization = {Max-Planck-Gesellschaft}, school = {Biologische Kybernetik}, address = {Piscataway, NJ, USA}, month = may, year = {2011}, slug = {7048}, author = {Lampariello, R. and Nguyen-Tuong, D. and Castellini, C. and Hirzinger, G. and Peters, J.}, month_numeric = {5} }