Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain

Phunopas, Amornphun; Inoue, Satoshi

doi:10.2991/jrnal.2018.4.4.4

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…The following is an illustration and equation of an omnidirectional robot with a fourwheel configuration. In robot kinematic, the terms forward kinematic and inverse kinematic are known, forward kinematic is a method for determining the orientation or distance of a robot based on wheel speed, while inverse kinematic is the opposite of forward kinematic, which is a method for determining the value of the velocity of each wheel from the reference distance and orientation to be addressed [9][10][11][12][13]. Equation (4) of robot kinematic equations are as follows.…”

Section: Eulerdometrymentioning

confidence: 99%

“…So that some robot developers often combine two or more categories of combining the two groups [2]. In studies the most commonly used categories are odometry [9][10][11][12][13], inertial measurement [7][8], magnetic compass [7][8], and map matching [23][24][25][26][27][28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

“…Omnidirectional robot is a type of mobile robot that can move in any direction without changing the orientation of the robot, with the wheels used are omni wheels. Therefore, odometry calculations refer to the omnidirectional robot kinematics depending on the number of wheels and the type of omni wheel used in the robot [9][10][11][12][13]. Meanwhile, the method of Inertial Measurement and magnetic compass can be said to have the same characteristics because there are so many integrated circuits (IC) of the Inertial Measurement Unit (IMU) sensor, there are two components of the method, namely Accelerometer, Gyroscope, and Magnetometer [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

Fikri

Anifah

2021

Jurnal Teknologi

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The main problem from autonomous robot for navigation is how the robot able to recognize the surrounding environment and know this position. These problems make this research weakness and become a challenge for further research. Therefore, this research focuses on designing a mapping and positioning system using Simultaneous Localization and Mapping (SLAM) method which is implemented on an omnidirectional robot using a LiDAR sensor. The proposes of this research are mapping system using the google cartographer algorithm combined with the eulerdometry method, eulerdometry is a combination of odometry and euler orientation from IMU sensor, while the positioning system uses the Adaptive Monte Carlo Localization (AMCL) method combined with the eulerdometry method. Testing is carried out by testing the system five times from each system, besides that testing is also carried out at each stage, testing on each sensor used such as the IMU and LiDAR sensors, and testing on system integration, including the eulerdometry method, mapping system and positioning system. The results on the mapping system showed optimal results, even though there was still noise in the results of the maps created, while the positioning system test got an average RMSE value from each map created of 278.55 mm on the x-axis, 207.37 mm on the y-axis, and 4.28o on the orientation robot.

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Section: Eulerdometrymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

Fikri

Anifah

2021

Jurnal Teknologi

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“…Using the coordinate system and by defining angular velocity of each wheel,

and the velocity vector of the robot

, the kinematics of the OWMR [ 43 , 44 ] is given:…”

Section: A Bio-inspired Controller Design For An Omni-wheel Mobilementioning

confidence: 99%

Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot

Kim

Suh

Han

2020

Sensors

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This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles.

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“…Omnidirectional robots are much preferred over the conventional ones due to the fact that such robots are robust, able to maneuver sharp curves, do pivot rotations, furthermore, there is no need to a dedicated steering mechanisms which reduce the mechanical complexities [ 5 , 6 , 7 , 8 ]. In the literature one can see several recent studies related to the driving mechanism design and/or the reduction in the wheel slippage and vibration for the omnidirectional robots.…”

Section: Introductionmentioning

confidence: 99%

Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers

Batayneh

Aburmaileh

2020

Sensors

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The last decade observed a significant research effort directed towards maneuverability and safety of mobile robots such as smart wheelchairs. The conventional electric wheelchair can be equipped with motorized omnidirectional wheels and several sensors serving as inputs for the controller to achieve smooth, safe, and reliable maneuverability. This work uses the decentralized algorithm to control the motion of omnidirectional wheelchairs. In the body frame of the omnidirectional wheeled wheelchair there are three separated independent components of motion including rotational motion, horizontal motion, and vertical motion, which can be controlled separately. So, each component can have its different sub-controller with a minimum tracking error. The present work aims to enhance the mobility of wheelchair users by utilizing an application to control the motion of their attained/unattained smart wheelchairs, especially in narrow places and at hard detours such as 90˚ corners and U-turns, which improves the quality of life of disabled users by facilitating their wheelchairs’ maneuverability. Two approaches of artificial intelligent-based controllers (PD-Fuzzy-P and GA-PID controllers) are designed to optimally enhance the maneuverability of the system. MATLAB software is used to simulate the system and calculate the Mean Error (ME) and Mean Square Error (MSE) for various scenarios in both approaches, the results showed that the PD-Fuzzy-P controller has a faster convergence in trajectory tracking than the GA-PID controller. Therefore, the proposed system can find its application in many areas including transporting locomotor-based disabled individuals and geriatric people as well as automated guided vehicles.

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Motion Improvement of Four-Wheeled Omnidirectional Mobile Robots for Indoor Terrain

Cited by 9 publications

References 24 publications

Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

Mapping and Positioning System on Omnidirectional Robot Using Simultaneous Localization and Mapping (Slam) Method Based on Lidar

Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot

Decentralized Motion Control for Omnidirectional Wheelchair Tracking Error Elimination Using PD-Fuzzy-P and GA-PID Controllers

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