Name
Title | ||
|---|---|---|
Plastic Grabber - Underwater Autonomous Vehicle Simulation for Plastic Objects Retrieval Using Genetic Programming. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-030-04849-5_46 | BIS |
Field | DocType | Citations |
Motion planning,Computer science,Knowledge management,Fitness function,Real-time computing,Genetic programming,Plastic object,Robot,Underwater vehicle,Reinforcement learning,Underwater | Conference | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Kasparaviciute | 1 | 9 | 1.29 |
| Stig Anton Nielsen | 2 | 4 | 1.54 |
| Dhruv Boruah | 3 | 0 | 0.34 |
| Peter Nordin | 4 | 704 | 95.40 |
| Alexandru Dancu | 5 | 50 | 8.33 |