Automatic Parameter Learning for Easy Instruction of Industrial Collaborative Robots

TY - GEN

T1 - Automatic Parameter Learning for Easy Instruction of Industrial Collaborative Robots

AU - Sørensen, Lars Carøe

AU - Andersen, Rasmus Skovgaard

AU - Schou, Casper

AU - Kraft, Dirk

PY - 2018/4

Y1 - 2018/4

N2 - The manufacturing industry faces challenges in meeting requirements of flexibility, product variability and small batch sizes. Automation of high mix, low volume productions requires faster (re)configuration of manufacturing equipment. These demands are to some extend accommodated by collaborative robots. Certain actions can still be hard or impossible to manually adjust due to inherent process uncertainties. This paper proposes a generic iteratively learning approach based on Bayesian Optimisation to efficiently search for the optimal set of process parameters. The approach takes into account the process uncertainties by iteratively making a statistical founded choice on the next parameter-set to examine only based on the prior binomial outcomes. Moreover, our function estimator uses Wilson Score to make proper estimates on the success probability and the associated uncertain measure of sparsely sampled regions. The function estimator also generalises the experiment outcomes to the neighbour region through kernel smoothing by integrating Kernel Density Estimation. Our approach is applied to a real industrial task with significant process uncertainties, where sufficiently robust process parameters cannot intuitively be chosen. Using our approach, a collaborative robot automatically finds a reliable solution.

AB - The manufacturing industry faces challenges in meeting requirements of flexibility, product variability and small batch sizes. Automation of high mix, low volume productions requires faster (re)configuration of manufacturing equipment. These demands are to some extend accommodated by collaborative robots. Certain actions can still be hard or impossible to manually adjust due to inherent process uncertainties. This paper proposes a generic iteratively learning approach based on Bayesian Optimisation to efficiently search for the optimal set of process parameters. The approach takes into account the process uncertainties by iteratively making a statistical founded choice on the next parameter-set to examine only based on the prior binomial outcomes. Moreover, our function estimator uses Wilson Score to make proper estimates on the success probability and the associated uncertain measure of sparsely sampled regions. The function estimator also generalises the experiment outcomes to the neighbour region through kernel smoothing by integrating Kernel Density Estimation. Our approach is applied to a real industrial task with significant process uncertainties, where sufficiently robust process parameters cannot intuitively be chosen. Using our approach, a collaborative robot automatically finds a reliable solution.

KW - Industrial assembly

KW - Parameter optimisation

UR - http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8352157&isnumber=8352140

U2 - 10.1109/ICIT.2018.8352157

DO - 10.1109/ICIT.2018.8352157

M3 - Article in proceeding

VL - 2018-February

SP - 87

EP - 92

BT - Proceedings - 2018 IEEE International Conference on Industrial Technology, ICIT 2018

PB - IEEE

ER -