ProceedingsIEEE Computer Society Press, 1995 - Computer integrated manufacturing systems |
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Page 72
IEEE International Symposium on Assembly and Task Planning. for k = 1 to rri2 fil
}lk = random.manhattan-path.with(<^i,j(l)); 0\,],k = <i>\,} * T>,],k; » = max», „ ||*.,.,,fc
(l)-£(|| ; return(<r); end In reality, we use genetic algorithms to solve the above ...
IEEE International Symposium on Assembly and Task Planning. for k = 1 to rri2 fil
}lk = random.manhattan-path.with(<^i,j(l)); 0\,],k = <i>\,} * T>,],k; » = max», „ ||*.,.,,fc
(l)-£(|| ; return(<r); end In reality, we use genetic algorithms to solve the above ...
Page 221
We thus decided to apply here a Grouping Genetic Algorithm, an advanced
optimization technique developed in [Falkenauer and Delchambre, 92], This is a
heavily modified version of the Genetic Algorithm, one of the best optimization ...
We thus decided to apply here a Grouping Genetic Algorithm, an advanced
optimization technique developed in [Falkenauer and Delchambre, 92], This is a
heavily modified version of the Genetic Algorithm, one of the best optimization ...
Page 222
The Optimization Process The Genetic Algorithm is an optimization method
based on improvement of a solution. This means that a feasible solution to the
line design problem is constructed at the outset. This initial solution is usually not
a very ...
The Optimization Process The Genetic Algorithm is an optimization method
based on improvement of a solution. This means that a feasible solution to the
line design problem is constructed at the outset. This initial solution is usually not
a very ...
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Contents
A New Approach for the Specification of Assembly Systems | 9 |
Plan Representation and Generation for Manufacturing Tasks | 22 |
Lessons Learned from a Second Generation Assembly Planning System | 41 |
Copyright | |
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Common terms and phrases
algorithm analysis applied approach Artificial Intelligence assembly model assembly operations assembly planning assembly sequences assembly task camera cell clearance collision common ontology components Computer Conf Conference on Robotics configuration space constraints convex coordinates corresponding Cspace decomposition defined described disassembly domain edge ellipsoid Engineering equation example execution fixels fixture function geometric global goal graph grasp gripper handler IEEE implemented initial input intersection knowledge representation machine manipulator Manufacturing Systems mating mechanical method motion planning moving nodes object obstacles octree ontology optimal orientation paper parameters path path planning performance Petri nets planner polygon position problem Proc process planning rendezvous-point represent representation robot motion Robotics and Automation scheduling sensor shown in Figure simulation snap fastener solution strategy structure subassemblies subgoal task planning ternary operations tion tool trajectory transition uncertainty vector voxels workcell workpiece
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, 1995 |
| Original from | the University of Michigan |
| Digitized | 11 Jan 2008 |
| Export Citation | BiBTeX EndNote RefMan |