SLAM using AD* Algorithm with Absolute Odometry

Hanagi, Rohan Ravindra; Gurav, Omkar Shivling; Khandekar, Shridhar

doi:10.1109/i2ct51068.2021.9418118

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Hector SLAM is a SLAM algorithm based on scanning matching, which ensures the accuracy of raster maps through bilinear interpolation and uses the Gauss-Newton iterative method to solve the optimal matching between scanned data and maps [10] for map construction and location. Hector SLAM does not require odometer data for localization, but relies heavily on high resolution and high scanning frequency lidar.…”

Section: Hector Slammentioning

confidence: 99%

Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS

Zhao

Liu

2022

Sensors

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Aiming at the problems of low mapping accuracy, slow path planning efficiency, and high radar frequency requirements in the process of mobile robot mapping and navigation in an indoor environment, this paper proposes a four-wheel drive adaptive robot positioning and navigation system based on ROS. By comparing and analyzing the mapping effects of various 2D-SLAM algorithms (Gmapping, Karto SLAM, and Hector SLAM), the Karto SLAM algorithm is used for map building. By comparing the Dijkstra algorithm with the A* algorithm, the A* algorithm is used for heuristic searches, which improves the efficiency of path planning. The DWA algorithm is used for local path planning, and real-time path planning is carried out by combining sensor data, which have a good obstacle avoidance performance. The mathematical model of four-wheel adaptive robot sliding steering was established, and the URDF model of the mobile robot was established under a ROS system. The map environment was built in Gazebo, and the simulation experiment was carried out by integrating lidar and odometer data, so as to realize the functions of mobile robot scanning mapping and autonomous obstacle avoidance navigation. The communication between the ROS system and STM32 is realized, the packaging of the ROS chassis node is completed, and the ROS chassis node has the function of receiving speed commands and feeding back odometer data and TF transformation, and the slip rate of the four-wheel robot in situ steering is successfully measured, making the chassis pose more accurate. Simulation tests and experimental verification show that the system has a high precision in environment map building and can achieve accurate navigation tasks.

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Section: Hector Slammentioning

confidence: 99%

Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS

Zhao

Liu

2022

Sensors

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Developing a Machine Learning Algorithm for Service Robots in Industrial Applications

Kulaç

Engin

2023

Machines

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Robots, which have mostly been effective in areas such as industrial, agricultural, and production facilities, have started to take a place in the service sector, as their technologies have become lower in cost and more easily accessible. This situation has attracted the attention of companies and researchers and has accelerated studies on this subject. In this study, an algorithm was developed for the autonomous mobile robot to serve in industrial areas. In line with this study, it was ensured that the autonomous mobile robot mapped the working environment, determined the working station in this environment, and then carried out transport operations between these working stations in accordance with a given work order. After the mobile robot fulfilled the work order, it went into a waiting state until a new work order was received. For the mobile robot to save energy, it was ensured that it waited close to the point where the work order came in the most, by means of machine learning in the waiting position. The developed algorithms were designed using the NI LabVIEW environment and then simulated in the RobotinoSIM environment and physically tested using the Robotino autonomous mobile robot platform. The experimental results showed that mapping and location reporting using an RGB camera, odometry, and a QR code eliminated permanent location errors, and the robot completed 50 work orders with 100% accuracy.

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SLAM using AD* Algorithm with Absolute Odometry

Cited by 2 publications

References 7 publications

Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS

Research and Implementation of Autonomous Navigation for Mobile Robots Based on SLAM Algorithm under ROS

Developing a Machine Learning Algorithm for Service Robots in Industrial Applications

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