Topological mapping and navigation based on visual sensor network

Yuan, Wenbo; Cao, Zhiqiang; Tan, Min; Liu, Jingyu

doi:10.1109/icma.2014.6885954

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…To overcome this drawback, we will formulate the AGV working space depending on a real‐world map. Three types of map formulation methods are popular in the logistic navigation, namely feature map [ 12 ] , topological map [ 13 ] and raster map [ 14 ] . For the small‐scale space considering obstacles, the raster map has more advantages in path planning [ 15 ] .…”

Section: Problem Statementmentioning

confidence: 99%

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

Wen

Daichuan

Liu

et al. 2021

Chinese J of Electronics

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We propose an integrated path planning method for multiple automated guided vehicles performing logistics delivery within a real‐world warehouse environment considering obstacles. By applying it on each vehicle, this proposed method enables the vehicle the vehicles have the capabilities for autonomous path planning. The path planning consists of three parts, K‐means algorithm based task points clustering, genetic algorithm based task points ordering, and the probabilistic road map based best path search. Vehicle conflict resolution is depending on implementing the probabilistic road map construction considering the realistic map with obstacles. The simulations result validate that the clustering and ordering are necessary for the path planning, both the path planning time and the Automated guided vehicles (AGVs) running time can be dramatically reduced.

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Section: Problem Statementmentioning

confidence: 99%

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

Wen

Daichuan

Liu

et al. 2021

Chinese J of Electronics

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“…Furthermore, a practical nonholonomic industrial mobile robot model is considered in our method and dynamic environments with moving obstacles are supposed in our method, unlike other path planning algorithms e.g. [198,202].…”

Section: Contributionsmentioning

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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An End-to-End Deep Reinforcement Learning Method for UAV Autonomous Motion Planning

Cui

Dong

et al. 2022

2022 7th International Conference on Robotics and Automation Engineering (ICRAE)

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Topological mapping and navigation based on visual sensor network

Cited by 4 publications

References 14 publications

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

A Novel Integrated Path Planning Algorithm for Warehouse AGVs

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

An End-to-End Deep Reinforcement Learning Method for UAV Autonomous Motion Planning

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