A novel navigation system for an autonomous mobile robot in an uncertain environment

Yong, Wu; He, Hongmei

doi:10.1017/s0263574721000497

Cited by 2 publications

(1 citation statement)

References 35 publications

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“…The authors have created a navigation framework that uses an evolutionary threshold filtering technique to identify barriers in a sliding window, categories the identified barriers with a tree structure, heuristically forecasts future collisions, and uses a simplified Morphin method to select the best path [7]. Gao and Tian [8] have evaluated the refined SA mixed optimization approach and the artificial neural network and implemented it to wheeled robot motion planning in an unknown environment.…”

Section: Introductionmentioning

confidence: 99%

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

Kashyap

Parhi²,

Kumar³

2022

Robotica

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The given article emphasizes the development and modeling of a hybrid navigational controller to optimize the path length and time taken. The proposed navigational controller is developed by hybridizing the metaheuristic moth–flame optimization (MFO) approach and the reinforcement learning (RL) approach. Input parameters like obstacle and target locations are fed to the MFO controller that implements a proper navigational direction selection. It forwards to the RL controller, which exercises further refinement of the output turning angle around obstacles. The collaboration of the global MFO approach with the local-based RL approach helps to optimize the path traversed by the humanoid robot in an unknown environment. The major breakthrough in this article is the utilization of humanoid robots for navigation purposes between various checkpoints. The humanoid robots are placed in a cluttered environment and assigned specific target positions to complete the assigned tasks. In the case of a multi-humanoid robot system, to avoid self-collision, it requires a Petri-Net controller to be configured in the navigation system to prevent deadlock situations and enhance the smooth completion of tasks without inter-collision among the humanoid robots. Simulations and real-time experiments are undertaken using different controllers involving single- and multi-humanoid robot systems. The robustness of the proposed controller is also validated in dynamic environment. Comparisons are carried with an established navigational controller in a similar environmental setup, which proves the proposed hybrid controller to be robust and efficient.

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Section: Introductionmentioning

confidence: 99%

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

Kashyap

Parhi²,

Kumar³

2022

Robotica

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An effective point cloud registration method for three-dimensional reconstruction of pressure piping

Zhang,

Guan,

Wang

et al. 2024

Robotica

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At present, industrial scenes with sparse features and weak textures are widely encountered, and the three-dimensional reconstruction of such scenes is a recognized problem. Pressure pipelines have a wide range of applications in fields such as petroleum engineering, chemical engineering, and hydropower station engineering. However, there is no mature solution for the three-dimensional reconstruction of pressure pipes. The main reason is that the typical scenes in which pressure pipes are found also have relatively few features and textures. Traditional three-dimensional reconstruction algorithms based on feature extraction are largely ineffective for such scenes that are lacking in features. In view of the above problems, this paper proposes an improved interframe registration algorithm based on point cloud fitting with cylinder axis vector constraints. By incorporating geometric feature parameters of a cylindrical pressure pipeline, specifically the axis vector of the cylinder, to constrain the traditional iterative closest point algorithm, the accuracy of point cloud registration can be improved in scenarios lacking features and textures, and some environmental uncertainties can be overcome. Finally, using actual laser point cloud data collected from pressure pipelines, the proposed fitting-based point cloud registration algorithm with cylinder axis vector constraints is tested. The experimental results show that under the same conditions, compared with other open-source point cloud registration algorithms, the proposed method can achieve higher registration accuracy. Moreover, integrating this algorithm into an open-source three-dimensional reconstruction algorithm framework can lead to better reconstruction results.

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A novel navigation system for an autonomous mobile robot in an uncertain environment

Cited by 2 publications

References 35 publications

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

Navigation for multi-humanoid using MFO-aided reinforcement learning approach

An effective point cloud registration method for three-dimensional reconstruction of pressure piping

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