Kinematically optimal catching a flying ball with a hand-arm-system

Abstract

Abstract-A robotic ball-catching system built from a multipurpose 7-DOF lightweight arm (DLR-LWR-III) and a 12 DOF four-fingered hand (DLR-Hand-II) is presented. Other than in previous work a mechatronically complex dexterous hand is used for grasping the ball and the decision of where, when and how to catch the ball, while obeying joint, speed and work cell limits, is formulated as an unified nonlinear optimization problem with nonlinear constraints. Three different objective functions are implemented, leading to significantly different robot movements. The high computational demands of an online realtime optimization are met by parallel computation on distributed computing resources (a cluster with 32 CPU cores). The system achieves a catch rate of > 80% and is regularly shown as a live demo at our institute.

Catching flying balls and preparing coffee: Humanoid Rollin'Justin performs dynamic and sensitive tasks

“…Based on the continuously improving trajectory predictions a realtime path planner, running on an external cluster with 32 cores, decides where, when, and in which configuration to kinematical optimally catch the ball by repeatedly solving a nonlinear optimization problem including simple collision avoidance. In [8] we described a previous version of the planner for only one arm and simpler collision avoidance geometries. The resulting joint paths are executed by the arms, torso and mobile platform.…”

Estimating the non-linear dynamics of free-flying objects

“…A body of work has been devoted to autonomous control of fast movements such as catching [3][4][5][6][7][8][9][10], dynamic re-grasping (throwing an object up and catching it) [11], hitting flying objects [12,13] and juggling [14][15][16]. Most approaches assumed a known model of the dynamics of motion and considered solely modeling the translational object motion.…”

“…For instance, Hong and Slotine [4] and Riley and Atkeson [7] model the trajectories of a flying ball as a parabola, and subsequently recursively estimate the ball's trajectory through least squares optimization. Frese et al [6], Bauml et al [10] and Park et al [9] also assume a parabolic form for the ball trajectories which they use in conjunction with an Extended Kalman filter [17] for on-line re-estimation. Ribnick et al [18] and Herrejon et al [19] estimate 3D trajectories directly from monocular image sequences based on the ballistic ball movement equation.…”

Learning throwing and catching skills

“…The position of the ball on 1 Instead of employing the concept of a catching plane, more advanced criteria like "minimal movement of the end-effector" could be employed to determine the catching point [4]. the frames is determined by a GPU-based blob tracker [12].…”

“…Catching and throwing in robotics has previously been studied by [1][2][3][4][5] and many others. This video focuses on learning, generalizing, and combining skills.…”