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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Sugimoto, M. Kurashige, K. |
| Copyright Year | 2015 |
| Description | Author affiliation: Dept. of Comput. Sci. & Syst. Eng., Muroran Inst. of Technol., Muroran, Japan (Kurashige, K.) || Coll. of Inf. & Syst., Muroran Inst. of Technol., Muroran, Japan (Sugimoto, M.) |
| Abstract | For a robot that works in a dynamic environment, the ability to autonomously cope with the changes in the environment, is important. In this paper, we propose an approach to predict the changes of the state and action of the robot. Further, to extend this approach, we will attempt to apply the action to be taken in the future, to the current action. This method predicts the robot state and action for the distant future using the state that the robot adopts repeatedly. By using this method, we can predict the actions that the robot will take in the future. In addition, we consider that the state and the action of the robot will change continuously and mutually. In this paper, we propose a method that predicts the state and the action each time the robot decides to perform an action. In particular, in this paper, we will focus on how to define the weight coefficients, using the characteristics of the future prediction results. By using this method, we will obtain the compensatory current action. This paper presents the results of our study and discusses methods that allow the robot to decide its desirable behavior quickly, using state prediction and optimal control methods. |
| Starting Page | 544 |
| Ending Page | 551 |
| File Size | 736835 |
| Page Count | 8 |
| File Format | |
| e-ISBN | 9781479974924 |
| DOI | 10.1109/CEC.2015.7256937 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-05-25 |
| Publisher Place | Japan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robots Iron Immune system Wheels |
| Content Type | Text |
| Resource Type | Article |
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