Online optimal trajectory generation for robot table tennis

Koc, Okan; Maeda, Guilherme; Peters, Jan

doi:10.1016/j.robot.2018.03.012

Cited by 40 publications

(10 citation statements)

References 24 publications

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“…Calculation of trajectories in table tennis mostly concentrates on individual cases without statistical analysis [18]. New interest for the fast calculation of table-tennis trajectories is motivated by the research on robots [19,20] and for the programming of computer games [21]. The large modeling database established by this procedure was analyzed in this paper using statistical methods to identify, in more detail, the major differences for the different cases and their major dependencies.…”

Section: Averagesmentioning

confidence: 99%

Statistical Analysis of Table-Tennis Ball Trajectories

et al. 2018

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In this work, the equations of motion for table-tennis balls were numerically solved on graphics processing units (GPUs) using Compute Unified Device Architecture (CUDA) for systematical statistical studies of the impact of ball size and weight, as well as of net height, on the distribution functions of successful strokes. Half a billion different initial conditions involving hitting location, initial spin, and velocities were analyzed to reach sufficient statistical significance for the different cases. In this paper, an advanced statistical analysis of the database generated by the simulation is presented.

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Section: Averagesmentioning

confidence: 99%

Statistical Analysis of Table-Tennis Ball Trajectories

et al. 2018

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“…However, the task of how to embed the new state variables into the dynamics is still a challenge remaining to be solved. Considering the transformation in (18), the relationship among σ , θ and q can expressed with an implicit function as below [i] (q, θ, σ ) = 0 (21)…”

Section: A Handling the Inequality Constraintsmentioning

confidence: 99%

“…However, the study of trajectory planning for variable stiffness actuated robot is still ongoing. Furthermore, considering a task specific scenario, like a ping-pong playing task [18], a gymnastics-like task [19], a ball-throwing task [6] or a hand-shake task, it is the absence of priori knowledge about how to modulate the stiffness to achieve target performance of these tasks. Obviously, monitoring the arm stiffness variation performance of the human arm while executing a predefined task is one straight and exprimental way to obtain the control law of stiffness.…”

Section: Introductionmentioning

confidence: 99%

Time-Energy Optimal Trajectory Planning for Variable Stiffness Actuated Robot

Chen

Kong

2019

IEEE Access

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A variable stiffness actuator is inspired by the human motor control and is the most popular actuator used to exploit the human performance and human-like motion. However, these actuators are typically highly non-linear and redundant not only in their kinematics but also in their dynamics due to their capability to modulate their stiffness and positions simultaneously. It is not trivial to generate the trajectory for a strongly non-linear and redundant dynamic system equipped with variable stiffness actuators. In this paper, a trajectory planning method for a variable stiffness actuated robot via a time-energy optimal control policy is proposed. The simulation studies demonstrate the effectiveness of the trajectory planning method through the case studies of a two degree of freedom variable stiffness actuated robot. Furthermore, the results show that the proposed method could be able to generate the motion which is similar to the human arm motion strategy.

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“…Dinh et al 20 combine sequential action control 25 with indirect optimization. In numerical optimization frameworks, it is also possible to generate trajectories for hybrid dynamic systems such as the table tennis robot in Koç et al 21 While solving the individual optimization problems with sequential quadratic programming requires over 1s computation time, they precompute a lookup table from a fixed initial posture that can be used online. However, for a more general trajectory generation problem, this approach is infeasible.…”

Section: Related Workmentioning

confidence: 99%

An Online Trajectory Generator on SE(3) for Human–Robot Collaboration

Huber¹,

Wollherr²

2019

Robotica

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Summary With the increasing demand for humans and robots to collaborate in a joint workspace, it is essential that robots react and adapt instantaneously to unforeseen events to ensure safety. Constraining robot dynamics directly on SE(3), that is, the group of 3D translation and rotation, is essential to comply with the emerging Human–Robot Collaboration (HRC) safety standard ISO/TS 15066. We argue that limiting coordinate-independent magnitudes of physical dynamic quantities at the same time allows more intuitive constraint definitions. We present the first real-time capable online trajectory generator that constrains translational and rotational magnitude values of 3D translation and 3D rotation dynamics in a singularity-free formulation. Simulations as well as experiments on a hardware platform show the utility in HRC contexts.

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Online optimal trajectory generation for robot table tennis

Cited by 40 publications

References 24 publications

Statistical Analysis of Table-Tennis Ball Trajectories

Statistical Analysis of Table-Tennis Ball Trajectories

Time-Energy Optimal Trajectory Planning for Variable Stiffness Actuated Robot

An Online Trajectory Generator on SE(3) for Human–Robot Collaboration

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