Programming complex robot tasks by prediction: experimental results
نویسندگان
چکیده
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.
منابع مشابه
Predictive Robot Programming: Theoretical and Experimental Analysis
As the capabilities of manipulator robots increase, they are performing more complex tasks. The cumbersome nature of conventional programming methods limits robotic automation due to the lengthy programming time. We present a novel method for reducing the time needed to program a manipulator robot: Predictive Robot Programming (PRP). The PRP system constructs a statistical model of the user by ...
متن کاملBankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach
In this paper, a new Dynamic Geometric Genetic Programming (DGGP) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. Financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms’ impending failure for investors, creditors, borrowing firms, and governments. By the time, several methods have been attempted in...
متن کاملOptimal discrete-time control of robot manipulators in repetitive tasks
Optimal discrete-time control of linear systems has been presented already. There are some difficulties to design an optimal discrete-time control of robot manipulator since the robot manipulator is highly nonlinear and uncertain. This paper presents a novel robust optimal discrete-time control of electrically driven robot manipulators for performing repetitive tasks. The robot performs repetit...
متن کاملUnsupervised Model-Based Prediction of User Actions
In this paper we derive algorithms that construct simple continuous-density hidden Markov models based on unlabeled observations of user actions. These models are then be used to predict future actions of the user. The algorithms were designed for domains where user-generated training data are costly to obtain and, consequently, the number of free parameters must be kept low. More specifically,...
متن کاملDynamic Modeling and Construction of a New Two-Wheeled Mobile Manipulator: Self-balancing and Climbing
Designing the self-balancing two-wheeled mobile robots and reducing undesired vibrations are of great importance. For this purpose, the majority of researches are focused on application of relatively complex control approaches without improving the robot structure. Therefore, in this paper we introduce a new two-wheeled mobile robot which, despite its relative simple structure, fulfills the req...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2003