Knowledge-enabled parameterization of whole-body control strategies for compliant service robots
| Autor: | Michael Beetz, Alexander Dietrich, Daniel Leidner, Alin Albu-Schaffer |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
0209 industrial biotechnology
Service (systems architecture) Process modeling Whole-body control business.industry Computer science Window (computing) 02 engineering and technology Workspace Task (project management) 020901 industrial engineering & automation Artificial Intelligence Human–computer interaction 0202 electrical engineering electronic engineering information engineering Robot Humanoid robots 020201 artificial intelligence & image processing Artificial intelligence Mobile manipulation business Set (psychology) Task knowledge AI reasoning methods Humanoid robot |
| Zdroj: | Autonomous Robots. 40:519-536 |
| ISSN: | 1573-7527 0929-5593 |
| DOI: | 10.1007/s10514-015-9523-3 |
| Popis: | Compliant manipulation is one of the grand challenges for autonomous robots. Many household chores in human environments, such as cleaning the floor or wiping windows, rely on this principle. At the same time these tasks often require whole-body motions to cover a larger workspace. The performance of the actual task itself is thereby dependent on a large number of parameters that have to be taken into account. To tackle this issue we propose to utilize low-level compliant whole-body control strategies parameterized by high-level hybrid reasoning mechanisms. We categorize compliant wiping actions in order to determine relevant control parameters. According to these parameters we set up process models for each identified wiping action and implement generalized control strategies based on human task knowledge. We evaluate our approach experimentally on three whole-body manipulation tasks, namely scrubbing a mug with a sponge, skimming a window with a window wiper and bi-manually collecting the shards of a broken mug with a broom. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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