Comparison of a map- vs. camera-based user interface in a multi-robot navigation task

2004 ◽  
Author(s):  
B. Trouvain ◽  
C. Schlick ◽  
M. Mevert
Keyword(s):  
User Interface ◽  
Robot Navigation ◽  
Navigation Task ◽  
Multi Robot

Combination of Recurrent Neural Network and Deep Learning for Robot Navigation Task in Off-Road Environment

Robotica ◽  
2019 ◽  
Vol 38 (8) ◽  
pp. 1450-1462
Author(s):  
Farinaz Alamiyan-Harandi ◽  
Vali Derhami ◽  
Fatemeh Jamshidi
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Recurrent Neural Network ◽  
Robot Navigation ◽  
Recurrent Network ◽  
Navigation Task ◽  
Sensory Data ◽  
Uneven Terrain ◽  
Target Network ◽  
Fully Connected

SUMMARYThis paper tackles the challenge of the necessity of using the sequence of past environment states as the controller’s inputs in a vision-based robot navigation task. In this task, a robot has to follow a given trajectory without falling in pits and missing its balance in uneven terrain, when the only sensory input is the raw image captured by a camera. The robot should distinguish big pits from small holes to decide between avoiding and passing over. In non-Markov processes such as the abovementioned task, the decision is done using past sensory data to ensure admissible performance. Applying images as sensory inputs naturally causes the curse of dimensionality difficulty. On the other hand, using sequences of past images intensifies this difficulty. In this paper, a new framework called recurrent deep learning (RDL) with combination of deep learning (DL) and recurrent neural network is proposed to cope with the above challenge. At first, the proper features are extracted from the raw image using DL. Then, these represented features plus some expert-defined features are used as the inputs of a fully connected recurrent network (as target network) to generate command control of the robot. To evaluate the proposed RDL framework, some experiments are established on WEBOTS and MATLAB co-simulation platform. The simulation results demonstrate the proposed framework outperforms the conventional controller based on DL for the navigation task in the uneven terrains.

Multi-Robot Navigation Using Market-Based Optimization

2016 ◽  
pp. 337-367
Author(s):  
Rainer Palm ◽  
Abdelbaki Bouguerra ◽  
Muhammad Abdullah
Keyword(s):  
Robot Navigation ◽  
Multi Robot

AN ACTION GENERATION MODEL BY USING TIME SERIES PREDICTION AND ITS APPLICATION TO ROBOT NAVIGATION

2009 ◽  
Vol 19 (02) ◽  
pp. 105-113 ◽  
Author(s):  
MANABU GOUKO ◽  
KOJI ITO
Keyword(s):  
Time Series ◽  
Robot Navigation ◽  
Time Series Prediction ◽  
Sensory Information ◽  
Generation Model ◽  
Navigation Task ◽  
Motor Commands ◽  
Motor Primitive ◽  
Current Action ◽  
Task Simulation

This paper proposes an action generation model which consists of many motor primitive modules. The motor primitive modules output motor commands based on sensory information. Complicated behavior is generated by sequentially switching the modules. The model also has a prediction unit. This unit predicts which module will be used for current action generation. We have confirmed the effectiveness of the model by applying it to a robot navigation task simulation, and have investigated the influence of the prediction on the action generation.

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