A systematic approach for minimizing physical experiments to identify optimal trajectory parameters for robots

Ariyan M. Kabir, Joshua D. Langsfeld, Cunbo Zhuang, Krishnanand N. Kaipa, Satyandra K. Gupta

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Citations (Scopus)

Abstract

Use of robots is rising in process applications where robots need to interact with parts using tools. Representative examples can be cleaning, polishing, grinding, etc. These tasks can be non-repetitive in nature and the physics-based models of the task performances are unknown for new materials and tools. In order to reduce operation cost and time, the robot needs to identify and optimize the trajectory parameters. The trajectory parameters that influence the performance can be speed, force, torque, stiffness, etc. Building physics-based models may not be feasible for every new task, material, and tool profile as it will require conducting a large number of experiments. We have developed a method that identifies the right set of parameters to optimize the task objective and meet performance constraints. The algorithm makes decisions based on uncertainty in the surrogate model of the task performance. It intelligently samples the parameter space and selects a point for experimentation from the sampled set by determining its probability to be optimum among the set. The iterative process leads to rapid convergence to the optimal point with a small number of experiments. We benchmarked our method against other optimization methods on synthetic problems. The method has been validated by conducting physical experiments on a robotic cleaning problem. The algorithm is general enough to be applied to any optimization problem involving black box constraints.

Original languageEnglish
Title of host publicationICRA 2017 - IEEE International Conference on Robotics and Automation
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages351-357
Number of pages7
ISBN (Electronic)9781509046331
DOIs
Publication statusPublished - 21 Jul 2017
Externally publishedYes
Event2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore
Duration: 29 May 20173 Jun 2017

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/TerritorySingapore
CitySingapore
Period29/05/173/06/17

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