Programming complex robot tasks by prediction: experimental results

Author

Dixon, Kevin R. ; Khosla, Pradeep K.

Author_Institution

Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA, USA

Volume

4

fYear

2003

fDate

27-31 Oct. 2003

Firstpage

3150

Abstract

One of the main obstacles to automating production is the time needed to program the robot. Decreasing the programming time would increase the appeal of automation in many industries. In this paper we analyze the performance of a Predictive Robot Programming (PRP) system on complex, real-world robotic tasks. The PRP system attempts to decrease programming time by predicting the waypoints of a robot program based on previous examples of user behavior. We show that the PRP system is able to generate a large percentage of useful and highly accurate predictions, resulting in a potentially great amount of time saved.

Keywords

automatic programming; hidden Markov models; industrial robots; industries; production; robot programming; automating production; hidden Markov models; industries; predictive robot programming system; programming time; real world robotic tasks; Automata; Costs; Hidden Markov models; Manipulators; Predictive models; Production; Robot programming; Robotics and automation; Service robots; Stochastic resonance;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on

Print_ISBN

0-7803-7860-1

Type

conf

DOI

10.1109/IROS.2003.1249641

Filename

1249641