Robust 3D map building for a mobile robot moving on the floor

Hwang, Yo-Seop; Lee, Jang-Myung

doi:10.1109/aim.2015.7222733

Cited by 6 publications

(2 citation statements)

References 14 publications

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“…The OAECD can be properly applied in a static environment. Although a single-sensed map may generate noise, it can sense multiple times for the same environment in a short period and remove the noise on the map by the moving average method [20] or the median method [21]. Based on these methods, one can plan the route.…”

Section: Modified Opposite Angle-based Exact Cell Decomposition (Oaecmentioning

confidence: 99%

Expanded Douglas–Peucker Polygonal Approximation and Opposite Angle-Based Exact Cell Decomposition for Path Planning with Curvilinear Obstacles

Jung

Kang

et al. 2019

Applied Sciences

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The Expanded Douglas–Peucker (EDP) polygonal approximation algorithm and its application method for the Opposite Angle-Based Exact Cell Decomposition (OAECD) are proposed for the mobile robot path-planning problem with curvilinear obstacles. The performance of the proposed algorithm is compared with the existing Douglas–Peucker (DP) polygonal approximation and vertical cell decomposition algorithm. The experimental results show that the path generated by the OAECD algorithm with EDP approximation appears much more natural and efficient than the path generated by the vertical cell decomposition algorithm with DP approximation.

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Section: Modified Opposite Angle-based Exact Cell Decomposition (Oaecmentioning

confidence: 99%

Expanded Douglas–Peucker Polygonal Approximation and Opposite Angle-Based Exact Cell Decomposition for Path Planning with Curvilinear Obstacles

Jung

Kang

et al. 2019

Applied Sciences

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“…A flying robot can be considered to search a 3D environment with complex structure safely and build a 3D map for the environment by a 2D or 3D range finder sensor. It can be implemented in many fields, such as indoor 3D structure scanning, cave exploration and tunnel rescue; see [171,58,59].…”

Section: Future Workmentioning

confidence: 99%

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

2017

Preprint

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Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building.In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. The computer simulations and experiments confirm the performance of the presented method. Furthermore, considering the implementations of small-sized flying robots in industry, the presented navigation algorithm is extended into 3D environments. In the presented method, a time-of-flight camera network is used to detect the static and moving obstacles. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target and avoids any obstacles in the workspace.Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building.

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