Development of a Vision-Based Unmanned Ground Vehicle for Mapping and Tennis Ball Collection: A Fuzzy Logic Approach

Latifinavid, Masoud; Azizi, Aydin

doi:10.3390/fi15020084

Cited by 16 publications

(3 citation statements)

References 20 publications

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“…Currently, the use of only force sensors restricts the robot to a limited perception scenario. Therefore, by adding cameras, radar, and other sensors, we can increase the robot's perception capabilities [31]. By integrating neural networks, reinforcement learning, and other methods, it may even be possible to achieve autonomous driving [9].…”

Section: Discussionmentioning

confidence: 99%

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

Wu,

Ren,

Cheng

et al. 2023

Biomimetics

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Developing a human bionic manipulator to achieve certain humanoid behavioral skills is a rising problem. Enabling robots to operate the steering wheel to drive the vehicle is a challenging task. To address the problem, this work designs a novel 7-DOF (degree of freedom) humanoid manipulator based on the arm structure of human bionics. The 3-2-2 structural arrangement of the motors and the structural modifications at the wrist allow the manipulator to act more similar to a man. Meanwhile, to manipulate the steering wheel stably and compliantly, an admittance control approach is firstly applied for this case. Considering that the system parameters vary in configuration, we further introduce parameter fuzzification for admittance control. Designed simulations were carried out in Coppeliasim to verify the proposed control approach. As the result shows, the improved method could realize compliant force control under extreme configurations. It demonstrates that the humanoid manipulator can twist the steering wheel stably even in extreme configurations. It is the first exploration to operate a steering wheel similar to a human with a manipulator by using admittance control.

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Section: Discussionmentioning

confidence: 99%

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

Wu,

Ren,

Cheng

et al. 2023

Biomimetics

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“…Not specified or different algorithms used for object identification without specific focus on YOLO advancements (3)…”

Section: Yolov8 Demonstrates Superior Object Identification Accuracy ...mentioning

confidence: 99%

“…These robots, exemplified by models such as ARMAR III and PR2 Care O Bot, are specifically designed to operate effectively within hospital premises (2) . Their ability to offer physical and medical support to patients underscores the importance of enabling them to navigate safely and efficiently through these environments (3) .…”

Section: Introductionmentioning

confidence: 99%

Yolo Based Vision-Aided Obstacles Navigation and Avoidance with Path Planning using Sarsa Algorithm for Biped Robot in an Uncertain Hospital Environment

Duhan,

Panwar

2024

IJST

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Background: This paper presents a method devised to ensure the secure navigation of biped robots within hospital environments by meticulously identifying an optimal collision-free path from initial to final positions while mitigating potential damage from undetected small objects. Methods: Implementation of a modified SARSA algorithm facilitates the seamless movement of biped robots amidst unknown environments replete with static obstacles. Extensive evaluation of several YOLO algorithms is conducted to ascertain accurate vision-based obstacle detection within hospital images. Subsequent utilization of the SARSA algorithm enables the planning of obstacle-free paths within the identified hospital setting. Findings: Within the evaluated YOLO algorithms, Yolov8 emerges as the pinnacle, showcasing unparalleled accuracy in object identification and refining bounding box precision for robot navigation within complex hospital environments. The SARSA-based path planning ensures the creation of collision-free routes, affirming safe traversal for the biped robot. Particularly noteworthy is Yolov8's exceptional precision in detecting minute objects, significantly reducing collision risks. Novelty: This research marks a significant stride in advancing human-like path planning for biped robots maneuvering through intricate hospital settings. The emphasis on accurate object identification stands as a linchpin for guaranteeing the robot's secure traversal. Significance: The integration of Yolov8 substantially augments the biped robot's capacity to precisely detect small objects, thereby mitigating collision risks and potential damage. Moreover, the successful application of the SARSA algorithm in planning obstacle-free paths within complex hospital environments holds promise for augmenting real-world robot navigation, especially in sensitive environments like hospitals.

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Design of position controller for dicing saw chuck table based on improved LADRC technology

Liang,

Xie,

Lan

et al. 2024

Microsyst Technol

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Development of a Vision-Based Unmanned Ground Vehicle for Mapping and Tennis Ball Collection: A Fuzzy Logic Approach

Cited by 16 publications

References 20 publications

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

An Admittance Control Method Based on Parameters Fuzzification for Humanoid Steering Wheel Manipulation

Yolo Based Vision-Aided Obstacles Navigation and Avoidance with Path Planning using Sarsa Algorithm for Biped Robot in an Uncertain Hospital Environment

Design of position controller for dicing saw chuck table based on improved LADRC technology

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