Abstract
Automation of photogrammetric tasks by means of manipulator robots is a complex problem. It involves planning and controlling many aspects that reflect on the overall system performance in terms of precision and efficiency. This paper deals with the problem of task distribution for a multiple manipulator work cell with the goal of obtaining highly accurate object measurements. Task distribution is separated into two independent combinatorial optimization problems: activity assignment and tour planning. These problems are solved simultaneously by an optimization method based on genetic algorithms. This method implements a series of restriction-based heuristics in order to utilize a simple genetic representation similar to random keys. Experiments that validate the effectiveness of our approach are presented.
| Original language | English |
|---|---|
| Pages (from-to) | 3235-3240 |
| Number of pages | 6 |
| Journal | Proceedings of the IEEE International Conference on Systems, Man and Cybernetics |
| Volume | 5 |
| DOIs | |
| State | Published - 2001 |
Keywords
- Computational photogrammetry
- Genetic algorithms
- Multiple robots
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