This paper presents the development of an omniwheeled mobile robot based on inverse kinematics and odometry for local and indoor navigation purposes, such as for automatic warehousing in industry or healthcare environment. The robot uses four-wheeled diagonal configuration to conform directional angles of 1 =45°, 2=135°, 3=225°, and 4=315°. Inverse kinematics is utilized to drive the robot to a point with specific trajectory and heading angle. Internal wheeledencoders mounted in each DC-motors are used to read the angular speed and position. This research utilizes odometry technique to estimate the robot's position relative to the initial position. In order to develop a more precise odometry result, we combine the use of wheeled-encoders and an IMU. In order to maintain robot's position relative to the desired position, a PID control is applied to the algorithm. The result of the tests show that the developed omni-wheeled mobile robot is capable of performing locomotion to the desired position and to follow a controlled trajectory by maintaining a minimum error relative to the referenced trajectory.
Place recognition algorithm based-on visual sensor is crucial to be developed especially for an application of indoor robot navigation in which a Ground Positioning System (GPS) is not reliable to be utilized. This research compares the approach of place recognition of using learned-features from a model of Convolutional Neural Network (CNN) against conventional methods, such as Bag of Words (BoW) with SIFT features and Histogram of Oriented Uniform Patterns (HOUP) with its Local Binary Patterns (LBP). This research finding shows that the performance of our approach of using learned-features with transfer learning method from pre-trained CNN AlexNet is better than the conventional methods based-on handcrafted-features such as BoW and HOUP.
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