Uploaded May 2018 | Updated September 2026, 3 weeks ago
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod N.7
Authors: Mathiesen, Simon; Sørensen, Lars Carøe; Kraft, Dirk; Ellekilde, Lars-Peter
Title: Optimisation of Trap Design for Vibratory Bowl Feeders
Abstract:
Vibratory bowl feeders (VBFs) are a widely used option for industrial part feeding, but their design is still largely manual. A subtask of VBF design is determining an optimal parameter set for the passive devices, called traps, which the VBF uses to ensure correct part orientation. This paper proposes a fast and robust strategy for optimising traps, which makes use of dynamic simulation to efficiently evaluate the performance of parameter sets. The optimisation strategy is based on textit{Bayesian Optimisation} and selects new parameter sets to evaluate, using a modified Upper Confidence Bound with regression by textit{Kernel Density Estimation} as function estimator. The optimisation is run for four different traps with an industrial part and the best parameter sets are tested for robustness in simulation. The traps are then combined to create two sequences performing orientation of the parts and the designs are prototyped and tested on a real VBF.
ICRA 2018 Spotlight Video
Interactive Session Wed AM Pod N.7
Authors: Mathiesen, Simon; Sørensen, Lars Carøe; Kraft, Dirk; Ellekilde, Lars-Peter
Title: Optimisation of Trap Design for Vibratory Bowl Feeders
Abstract:
Vibratory bowl feeders (VBFs) are a widely used option for industrial part feeding, but their design is still largely manual. A subtask of VBF design is determining an optimal parameter set for the passive devices, called traps, which the VBF uses to ensure correct part orientation. This paper proposes a fast and robust strategy for optimising traps, which makes use of dynamic simulation to efficiently evaluate the performance of parameter sets. The optimisation strategy is based on textit{Bayesian Optimisation} and selects new parameter sets to evaluate, using a modified Upper Confidence Bound with regression by textit{Kernel Density Estimation} as function estimator. The optimisation is run for four different traps with an industrial part and the best parameter sets are tested for robustness in simulation. The traps are then combined to create two sequences performing orientation of the parts and the designs are prototyped and tested on a real VBF.
