scholarly journals Extending the Limits of the Random Exploration Graph for Efficient Autonomous Exploration in Unknown Environments

2019 ◽  
Author(s):  
Alfredo Toriz Palacios ◽  
Abraham Sánchez López
Keyword(s):  
Autonomous Exploration ◽  
Unknown Environments

scholarly journals Autonomous Exploration of Mobile Robots via Deep Reinforcement Learning Based on Spatiotemporal Information on Graph

2021 ◽  
Vol 11 (18) ◽  
pp. 8299
Author(s):  
Zhiwen Zhang ◽  
Chenghao Shi ◽  
Pengming Zhu ◽  
Zhiwen Zeng ◽  
Hui Zhang
Keyword(s):  
Reinforcement Learning ◽  
Mobile Robots ◽  
Information Entropy ◽  
Target Point ◽  
Hybrid Features ◽  
Convolutional Network ◽  
Autonomous Exploration ◽  
Spatial Feature ◽  
Unknown Environments ◽  
Temporal Feature

In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical trajectories, frontier waypoints, landmarks, and obstacles. Meanwhile, to take full advantage of the spatiotemporal feature and historical information in the autonomous exploration task, we propose a novel network called Spatiotemporal Neural Network on Graph (Graph-STNN). Specifically, the proposed Graph-STNN extracts the spatial feature using graph convolutional network (GCN) and the temporal feature using temporal convolutional network (TCN). Then, gated recurrent unit (GRU) is performed to synthesize the spatial feature, the temporal feature, and the historical state information into the current state feature. Combined with DRL, our Graph-STNN helps estimation of the optimal target point through extracted hybrid features. The simulation experiment shows that our approach is more effective than the GCN-based approach and the information entropy-based approach. Moreover, Graph-STNN also performs better generalization ability than GCN-based, information entropy-based, and random methods. Finally, we validate our approach on the simulation platform Stage with the actual robot model.

scholarly journals An End-to-End Deep Reinforcement Learning-Based Intelligent Agent Capable of Autonomous Exploration in Unknown Environments

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3575 ◽  
Author(s):  
Amir Ramezani Dooraki ◽  
Deok-Jin Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Intelligent Agent ◽  
Social Robots ◽  
Fusion Method ◽  
Autonomous Exploration ◽  
Unknown Environments ◽  
Real Robot ◽  
Different Shapes ◽  
High Degree

In recent years, machine learning (and as a result artificial intelligence) has experienced considerable progress. As a result, robots in different shapes and with different purposes have found their ways into our everyday life. These robots, which have been developed with the goal of human companionship, are here to help us in our everyday and routine life. These robots are different to the previous family of robots that were used in factories and static environments. These new robots are social robots that need to be able to adapt to our environment by themselves and to learn from their own experiences. In this paper, we contribute to the creation of robots with a high degree of autonomy, which is a must for social robots. We try to create an algorithm capable of autonomous exploration in and adaptation to unknown environments and implement it in a simulated robot. We go further than a simulation and implement our algorithm in a real robot, in which our sensor fusion method is able to overcome real-world noise and perform robust exploration.

Autonomous exploration and mapping of unknown environments

2012 ◽  
Author(s):  
Jason Owens ◽  
Phil Osteen ◽  
MaryAnne Fields
Keyword(s):  
Autonomous Exploration ◽  
Unknown Environments

scholarly journals Voronoi-Based Multi-Robot Autonomous Exploration in Unknown Environments via Deep Reinforcement Learning

2020 ◽  
pp. 1-1
Author(s):  
Junyan Hu ◽  
Hanlin Niu ◽  
Joaquin Carrasco ◽  
Barry Lennox ◽  
Farshad Arvin
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Exploration ◽  
Unknown Environments ◽  
Multi Robot

Controlling an Autonomous Agent for Exploring Unknown Environments using Switching Prelearned Modules

2018 ◽  
Vol 138 (2) ◽  
pp. 157-164
Author(s):  
Takahito Hata ◽  
Masanori Suganuma ◽  
Tomoharu Nagao
Keyword(s):  
Autonomous Agent ◽  
Unknown Environments

scholarly journals Semantic Active Visual Search System Based on Text Information for Large and Unknown Environments

2021 ◽  
Vol 101 (2) ◽  
Author(s):  
Mathias Mantelli ◽  
Diego Pittol ◽  
Renan Maffei ◽  
Jim Torresen ◽  
Edson Prestes ◽  
...  
Keyword(s):  
Visual Search ◽  
Search System ◽  
Unknown Environments ◽  
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