Title :
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
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;
Conference_Titel :
Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003 IEEE/RSJ International Conference on
Print_ISBN :
0-7803-7860-1
DOI :
10.1109/IROS.2003.1249641
