Name
Title | ||
|---|---|---|
Modular Neural Network and Classical Reinforcement Learning for Autonomous Robot Navigation: Inhibiting Undesirable Behaviors |
Abstract | ||
|---|---|---|
Classical reinforcement learning mechanisms and a modular neural network are unified to conceive an intelligent autonomous system for mobile robot navigation. The conception aims at inhibiting two common navigation deficiencies: generation of unsuitable cyclic trajectories and ineffectiveness in risky configurations. Different design apparatuses are considered to compose a system to tackle with these navigation difficulties, for instance: 1) neuron parameter to simultaneously memorize neuron activities and function as a learning factor, 2) reinforcement learning mechanisms to adjust neuron parameters (not only synapse weights), and 3) a inner-triggered reinforcement. Simulation results show that the proposed system circumvents difficulties caused by specific environment configurations, improving the relation between collisions and captures. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/IJCNN.2006.246723 | Vancouver, BC |
Keywords | Field | DocType |
mobile robots,neurocontrollers,path planning,autonomous robot navigation,inner-triggered reinforcement,intelligent autonomous system,mobile robot navigation,modular neural network,reinforcement learning | Motion planning,Computer science,Modular neural network,Artificial intelligence,Autonomous system (mathematics),Mobile robot navigation,Artificial neural network,Reinforcement,Mobile robot,Reinforcement learning | Conference |
ISSN | ISBN | Citations |
2161-4393 | 0-7803-9490-9 | 11 |
PageRank | References | Authors |
0.81 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Eric A. Antonelo | 1 | 19 | 1.32 |
| Albert-Jan Baerveldt | 2 | 115 | 12.41 |
| Thorsteinn Rögnvaldsson | 3 | 164 | 24.42 |
| Mauricio Figueiredo | 4 | 22 | 3.33 |