Improving indoor navigation of autonomous robots by an explicit representation of doors

An adaptive localization system for outdoor/indoor navigation for autonomous robots

10.1117/12.668520 ◽

2006 ◽

Cited By ~ 4

Author(s):

E. B. Pacis ◽

B. Sights ◽

G. Ahuja ◽

G. Kogut ◽

H. R. Everett

Keyword(s):

Autonomous Robots ◽

Indoor Navigation ◽

Localization System

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Fast Transfer Navigation for Autonomous Robots

Journal of Robotics ◽

10.1155/2021/3028319 ◽

2021 ◽

Vol 2021 ◽

pp. 1-7

Author(s):

Chen Wang ◽

Xudong Li ◽

Xiaolin Tao ◽

Kai Ling ◽

Quhui Liu ◽

...

Keyword(s):

Reinforcement Learning ◽

Indoor Environment ◽

Autonomous Robots ◽

Indoor Navigation ◽

Robotic Navigation ◽

Navigation Algorithm ◽

Proposed Model ◽

Interior Environment

Navigation technology enables indoor robots to arrive at their destinations safely. Generally, the varieties of the interior environment contribute to the difficulty of robotic navigation and hurt their performance. This paper proposes a transfer navigation algorithm and improves its generalization by leveraging deep reinforcement learning and a self-attention module. To simulate the unfurnished indoor environment, we build the virtual indoor navigation (VIN) environment to compare our model and its competitors. In the VIN environment, our method outperforms other algorithms by adapting to an unseen indoor environment. The code of the proposed model and the virtual indoor navigation environment will be released.

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3D Exploration and Navigation with Optimal-RRT Planners for Ground Robots in Indoor Incidents

Sensors ◽

10.3390/s20010220 ◽

2019 ◽

Vol 20 (1) ◽

pp. 220 ◽

Cited By ~ 3

Author(s):

Noé Pérez-Higueras ◽

Alberto Jardón ◽

Ángel Rodríguez ◽

Carlos Balaguer

Keyword(s):

Autonomous Navigation ◽

Sensory Input ◽

Autonomous Robots ◽

Point Clouds ◽

Explicit Representation ◽

Search And Rescue ◽

Planning System ◽

3D Environments ◽

3D Scenarios ◽

3D Exploration

Navigation and exploration in 3D environments is still a challenging task for autonomous robots that move on the ground. Robots for Search and Rescue missions must deal with unstructured and very complex scenarios. This paper presents a path planning system for navigation and exploration of ground robots in such situations. We use (unordered) point clouds as the main sensory input without building any explicit representation of the environment from them. These 3D points are employed as space samples by an Optimal-RRTplanner (RRT * ) to compute safe and efficient paths. The use of an objective function for path construction and the natural exploratory behaviour of the RRT * planner make it appropriate for the tasks. The approach is evaluated in different simulations showing the viability of autonomous navigation and exploration in complex 3D scenarios.

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