Autonomous navigation research for mobile robot

Cai, Jianchao; Yu, Ruihong; Cheng, Lina

doi:10.1109/wcica.2012.6357893

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…In particular, the project defines a method to solve navigation issues in an unknown environment which is based on reinforcement and operant conditioning learning. The strategy establishes a simulation of sensor data feedback entropy which influences the tendency degree [3]. The Navigation trajectory based on a bionic autonomous learning strategy has quick learning velocity and accurate navigation ability.…”

Section: B Autonomous Navigation Research For Mobile Robotmentioning

confidence: 99%

Object Detection Algorithms for Autonomous Navigation Wheelchairs in Hospital Environment

Ouardi,

A. Jawawi

2024

Int J Innov Comp

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Under the overwhelming situation medical facilities struggle to provide sufficient personnel to assist the continuous traffic. Therefore, considering alternative solutions to help nurses transport patients is relevant. An autonomous navigation wheelchair driven by object detection can become a substitute for transporting patients between facilities. The objective of this research is to study the potential of object detection algorithms in facilitating the autonomous navigation of patients within hospitals. For an object detection model to be a standalone technology for driving the autonomous wheelchair it has to satisfy the standards of the domain and display adequate readiness for deployment. Precisely, conduct viability experimentation based on Efficiency, Safety, and Reliability on a You Only Learn One Representation (YoloR) model trained for the research purpose. The paper finds YoloR as an appropriate model with potential for deployment. Both in terms of Reliability and Safety, the algorithm fits the designated criteria. However, the Autonomous Wheelchair system is a real-time system with strict execution time requirements that YoloR on its own does not reach. The latter is further confirmed by the proposed testing method.

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Section: B Autonomous Navigation Research For Mobile Robotmentioning

confidence: 99%

Object Detection Algorithms for Autonomous Navigation Wheelchairs in Hospital Environment

Ouardi,

A. Jawawi

2024

Int J Innov Comp

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Path planning of a mobile robot in a free-space environment using Q-learning

Jiang

Xin

2018

Prog Artif Intell

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This paper proposes an improved Q-learning algorithm for the path planning of a mobile robot in a free-space environment. Existing Q-learning methods for path planning focus on the mesh routing environment; therefore, new methods must be developed for free-space environments in which robots move continuously. For the free-space environment, we construct fuzzified state variables for dividing the continuous space to avoid the curse of dimensionality. The state variables include the distances to the target point and obstacles and the heading of the robot. Based on the defined state variables, we propose an integrated learning strategy on the basis of the space allocation to accelerate the convergence during the learning process. Simulation experiments show that the path planning of mobile robots can be realized quickly, and the probability of obstacle collisions can be reduced. The results of the experiments also demonstrate the considerable advantages of the proposed learning algorithm compared to two commonly used methods.

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A Hybrid Reactive Navigation Strategy for a Non-holonomic Mobile Robot in Cluttered Environments

Zhang

2019

2019 Chinese Control Conference (CCC)

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Autonomous navigation research for mobile robot

Cited by 3 publications

References 7 publications

Object Detection Algorithms for Autonomous Navigation Wheelchairs in Hospital Environment

Object Detection Algorithms for Autonomous Navigation Wheelchairs in Hospital Environment

Path planning of a mobile robot in a free-space environment using Q-learning

A Hybrid Reactive Navigation Strategy for a Non-holonomic Mobile Robot in Cluttered Environments

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