Modular Open Hardware Omnidirectional Platform for Mobile Robot Research

Marín, Leonardo

doi:10.1109/ccra.2018.8588120

Cited by 4 publications

(2 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For all these experiments, since the trials and algorithms were simulated, the only equipment needed was a computer running Ubuntu 18 with ROS Melodic, the computer was a server with an Intel Xeon Silver 4114 2.2 GHz. To simulate the environment, a software called GAZEBO 11.0.0 release was used to simulate the test environment, while a robot named TurtleBot 3 Burger was the one selected to be simulated in this work, because of its 2D LiDAR sensor and its differential driving mode, but other configurations can be used, such as a mecanum omnidirectional robot [35].…”

Section: Generalitiesmentioning

confidence: 99%

2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

Trejos

Rincón

Bolaños

et al. 2022

Sensors

View full text Add to dashboard Cite

The present work proposes a method to characterize, calibrate, and compare, any 2D SLAM algorithm, providing strong statistical evidence, based on descriptive and inferential statistics to bring confidence levels about overall behavior of the algorithms and their comparisons. This work focuses on characterize, calibrate, and compare Cartographer, Gmapping, HECTOR-SLAM, KARTO-SLAM, and RTAB-Map SLAM algorithms. There were four metrics in place: pose error, map accuracy, CPU usage, and memory usage; from these four metrics, to characterize them, Plackett–Burman and factorial experiments were performed, and enhancement after characterization and calibration was granted using hypothesis tests, in addition to the central limit theorem.

show abstract

Section: Generalitiesmentioning

confidence: 99%

2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

Trejos

Rincón

Bolaños

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…The development of small differential robots with advanced sensors and microcontrollers has made it possible to implement the RRT algorithm for navigation in unknown environments [16]. The combination of sensors such as position sensors, distance sensors, motor encoders, gyroscopes, and cameras provide the robot with the necessary information to navigate in its environment [17].…”

Section: Introductionmentioning

confidence: 99%

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

Sarmiento¹,

Jacinto²,

Ariza³

2023

IJACSA

View full text Add to dashboard Cite

This paper proposes the use of a small differential robot with two DC motors, controlled by an ESP32 microcontroller, that implements the Rapidly Exploring Random Trees algorithm to navigate from an origin point to a destination point in an unknown but observable environment. The motivation behind this research is to explore the use of a low-cost, versatile and efficient robotic platform for autonomous navigation in complex environments. This work presents a practical and costeffective solution that can be easily replicated and implemented in various scenarios such as search and rescue, surveillance, and industrial automation. The proposed robotic platform is equipped with a set of sensors and actuators that allow it to observe the environment, estimate its position, and move through it. The Rapidly Exploring Random Trees algorithm is implemented to generate a path from an origin to a destination point, avoiding obstacles and adjusting the robot's motion accordingly. The implementation of this algorithm enables the robot to navigate through complex environments with high efficiency and reliability, making it a suitable solution for a wide range of applications. The results obtained through simulations and experiments show that the proposed robotic platform and algorithm achieve high performance and accuracy in autonomous navigation, even in complex environments.

show abstract

VFH+D: An Improvement on the VFH+ Algorithm for Dynamic Obstacle Avoidance and Local Planning

Díaz

Marín

2020

IFAC-PapersOnLine

View full text Add to dashboard Cite

Modular Open Hardware Omnidirectional Platform for Mobile Robot Research

Cited by 4 publications

References 11 publications

2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

2D SLAM Algorithms Characterization, Calibration, and Comparison Considering Pose Error, Map Accuracy as Well as CPU and Memory Usage

Rapidly Exploring Random Trees for Autonomous Navigation in Observable and Uncertain Environments

VFH+D: An Improvement on the VFH+ Algorithm for Dynamic Obstacle Avoidance and Local Planning

Contact Info

Product

Resources

About