Automatic Path Planning of Industrial Robots Comparing Sampling-based and Computational Intelligence Methods
| Autor: | Jonghwa Kim, Lars-Christian Larsen, Alfons Schuster, Michael Kupke |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Engineering evolutionary algorithm Speedup sampling-based algorithm Property (programming) business.industry Process (engineering) Sampling (statistics) Computational intelligence 02 engineering and technology Plan (drawing) Industrial engineering Industrial and Manufacturing Engineering 020901 industrial engineering & automation ddc:670 Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Artificial intelligence Motion planning business path planning |
| Zdroj: | Procedia Manufacturing. 11:241-248 |
| ISSN: | 2351-9789 |
| DOI: | 10.1016/j.promfg.2017.07.237 |
| Popis: | In times of industry 4.0 a production facility should be “smart”. One result of that property could be that it is easier to reconfigure plants for different products which is, in times of a high rate of variant diversity, a very important point. Nowadays in typical robot based plants, a huge part of time from the commissioning process is needed for the programming of collision free paths. This mainly includes the teach-in or offline programming (OLP) and the optimization of the paths. To speed up this process significantly, an automatic and intelligent planning system is necessary. In this work we present a system which can plan paths for industrial robots. We compare widely used sampling-based methods like PRM or RRT with Computational Intelligence (CI) based methods like genetic algorithms. |
| Databáze: | OpenAIRE |
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