ProceedingsIEEE Computer Society Press - Computer integrated manufacturing systems |
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Page 1
To solve the optimization problem, we used an objective function consisting of a
goal term, a smoothness term and a collision term. In this paper, we propose a
theoretical method using reinforcement learning for adjusting weight parameters
in ...
To solve the optimization problem, we used an objective function consisting of a
goal term, a smoothness term and a collision term. In this paper, we propose a
theoretical method using reinforcement learning for adjusting weight parameters
in ...
Page 6
6 Conclusions and Future Work This paper presented a theoretical method using
reinforcement learning for adjusting the weight parameters in the objective
function that was used in motion planning[3]. In our learning algorithm, a value ...
6 Conclusions and Future Work This paper presented a theoretical method using
reinforcement learning for adjusting the weight parameters in the objective
function that was used in motion planning[3]. In our learning algorithm, a value ...
Page 389
Behaviors (states) mean how the system wprks. When any machine performs
human desired functions, it operates with changes in its location, shape,
attributes, or the relations between its parts. The change of its states represents
its behavior.
Behaviors (states) mean how the system wprks. When any machine performs
human desired functions, it operates with changes in its location, shape,
attributes, or the relations between its parts. The change of its states represents
its behavior.
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Contents
Monday May 282001 | 1 |
Session M1B Assembly Planning Room | 31 |
Session MIC Production Systeml RoomC | 61 |
Copyright | |
15 other sections not shown
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Common terms and phrases
4th IEEE International algorithm analysis applied approach Assembly and Task assembly model assembly planning assembly process assembly sequences assembly system assembly task automatic B-spline Bayesian network calculated camera collision components computed configuration constraints contact point coordinate cost defined deformable described developed disassembly sequences dynamic Engineering environment evaluation feasible Figure finger flexible Fukuoka function genetic algorithm geometric grasp holons IEEE International Symposium information entropy Japan liaison Lie algebra machine manipulation matrix mechanical method mobile robot module motion planning node object obstacles obtained operation optimal parameters path planning Petri nets Planning Soft Research precedence graph problem proposed Radon transform represented Robotics and Automation rotation scheduling Screw Theory selected self-organizing map sensor shown in Fig simulation Soft Research Park solutions specification station step structure subassembly Symposium on Assembly Task Planning Soft tion tool trajectory vector velocity
Bibliographic information
| Title | Proceedings |
| Author | IEEE International Symposium on Assembly and Task Planning |
| Contributors | IEEE Robotics and Automation Society, IEEE Xplore (Online service) |
| Publisher | IEEE Computer Society Press |
| Original from | the University of Michigan |
| Digitized | 11 Jan 2008 |
| Export Citation | BiBTeX EndNote RefMan |