Plastic Grabber: Underwater autonomous vehicle simulation for plastic objects retrieval using genetic programming
Kasparaviciute, G.; Nielsen, S.A.; Boruah, D.; Nordin, P.; Dancu, A. (2019). Plastic Grabber: Underwater autonomous vehicle simulation for plastic objects retrieval using genetic programming, in: Abramowicz, W. et al. Business Information Systems Workshops: BIS 2018 International Workshops
Berlin, Germany, July 18–20, 2018: Revised Papers. Lecture Notes in Business Information Processing, 339: pp. 527-533. https://dx.doi.org/10.1007/978-3-030-04849-5_46
In: Abramowicz, W.; Paschke, A. (Ed.) (2019). Business Information Systems Workshops: BIS 2018 International Workshops, Berlin, Germany, July 18–20, 2018: Revised Papers. Lecture Notes in Business Information Processing, 339. Springer: Cham. ISBN 978-3-030-04848-8. XII, 708 pp. https://dx.doi.org/10.1007/978-3-030-04849-5, meer
In: Lecture Notes in Business Information Processing. Springer: Cham. ISSN 1865-1348; e-ISSN 1865-1356, meer
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| Beschikbaar in | Auteurs |
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Documenttype: Congresbijdrage
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| Author keywords |
Underwater autonomous vehicle, Plastic collector, Genetic programming |
| Auteurs | | Top |
- Kasparaviciute, G.
- Nielsen, S.A.
- Boruah, D.
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| Abstract |
We propose a path planning solution using genetic programming for an autonomous underwater vehicle. Developed in ROS Simulator that is able to roam in an environment, identify a plastic object, such as bottles, grab it and retrieve it to the home base. This involves the use of a multi-objective fitness function as well as reinforcement learning, both required for the genetic programming to assess the model’s behaviour. The fitness function includes not only the objective of grabbing the object but also the efficient use of stored energy. Sensors used by the robot include a depth image camera, claw and range sensors that are all simulated in ROS. |
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