Throwing motion generation using nonlinear optimization on a 6-degree-of-freedom robot manipulator

Lombai, Ferenc; Szederkényi, Gábor

doi:10.1109/icmech.2009.4957138

Cited by 20 publications

(8 citation statements)

References 6 publications

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“…Serial robots with rotary axis are frequently used [3,4,5,6,7]. In [7], for instance, a robot is described which is capable of throwing balls into a moving basket with a success rate of 99%.…”

Section: State-of-the-artmentioning

confidence: 99%

Automated throwing and capturing of cylinder-shaped objects

Frank

Janoske

Mittnacht

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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A new approach for transportation of objects within production systems by automated throwing and capturing is investigated. This paper presents an implementation, consisting of a throwing robot and a capturing robot. The throwing robot uses a linear and the capturing robot a rotary axis. The throwing robot is capable of throwing cylinder-shaped objects onto a target point with high precision. The capturing robot there smoothly grips the cylinders during flight by means of a rotational movement. In order to synchronize the capturing robot and the cylinder's pose and velocity, its trajectory has to be modeled as well as the motion sequences of both robots. The throwing and capturing tasks are performed by the robots automatically without the use of any external sensor system.

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Section: State-of-the-artmentioning

confidence: 99%

Automated throwing and capturing of cylinder-shaped objects

Frank

Janoske

Mittnacht

et al. 2012

2012 IEEE International Conference on Robotics and Automation

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“…The results of this technique are evaluated experimentally. Lombai and al [4] consider trajectory planning of six degrees of freedom (d.o.f) manipulator, for a throwing motion. The trajectories are off-line generated for every joint (cubic spline interpolation), with a constrained nonlinear optimization approach, taking into consideration limitations of the joint actuators, and the amperage limit of the whole structure.…”

Section: Introductionmentioning

confidence: 99%

Control modes validation for generalized point to point task executions application: Robuter/UML mobile manipulator

Akli

Abdelfetah

Bouzouia

2012

2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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This paper presents implementation of time optimal generated trajectories, on a mobile manipulator. The executed task involves a generalized point to point trajectory generation, connecting initial and final imposed configurations.The trajectories are off-line generated considering kinematic constraints. Trajectory tracking is not guaranteed due to the controllers implemented on the real robot. Inherent changes on the environment surrounding the robot can also occurred. We propose in this paper to insert modules to the existing controller blocks of both manipulator and mobile platform allowing switching between velocity control mode (existing control mode) and position control mode. That allows reaching the final configuration with minimum error, or obstacle avoidance for the sudden environment changing.

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“…Before proceeding with the technical content of the paper, we mention here for completeness some relevant works on throwing motion of robots. Lombai and Szederkényi [8], Kato et al [9], Sato et al [10] and Senoo et al [11] focus on algorithms for dynamic motion planning and control for given robots. It is also, of course, possible to model human-like throwing motion in detail [12].…”

Section: Introductionmentioning

confidence: 99%

An engineering-design oriented exploration of human excellence in throwing

Dharmadhikari¹,

Chatterjee

2018

Sādhanā

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Humans are fast throwers, and their bodies differ correspondingly from those of other hominids. One might ask why humans evolved to throw fast while others did not; whether the design of a fast thrower is unique or special and whether indeed humans remain fast within broader comparison sets of non-hominid throwers. As a non-hominid comparison set, we consider a random population of five-link robots with simplified joint angle and torque constraints. We generate 20,000 such robot models and sequentially optimize their throwing motion. Since good initial guesses are needed for each optimization, the robots are first arranged in distance-minimizing sequences in design parameter space. Each robot's optimal throw then serves as an initial guess for the next one in sequence. Multiple traversals of these sequences, and random perturbations, are used to avoid local optima. Subsequently, regression models are used to predict throwing performance as a function of robot design parameters. From these regression models, the dominant heuristic predictor of fast throwing is found to be a long and light last link. Direct optimization of the robot design leads to much faster throwers, also with long and light last links. In striking contrast, the human arm has two equally long intermediate links of significant mass. Nevertheless, a somewhat human-like arm within the same robot set is found to be a good thrower. On combining several throwing criteria to obtain a single figure of merit, the human-like arm lies in the 96th percentile of the population. Since our human-like arm is a crude approximation of an actual human arm, we suggest that fast throwing by human-like robot arms is not inherently difficult from a mechanical point of view.

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Throwing motion generation using nonlinear optimization on a 6-degree-of-freedom robot manipulator

Cited by 20 publications

References 6 publications

Automated throwing and capturing of cylinder-shaped objects

Automated throwing and capturing of cylinder-shaped objects

Control modes validation for generalized point to point task executions application: Robuter/UML mobile manipulator

An engineering-design oriented exploration of human excellence in throwing

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