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This study aims to achieve the trajectory-tracking of an autonomous differential drive mobile robot (ADDMR) in the presence of friction torques using the proposed Fuzzy Sliding-Mode Control (FSMC). First, the complete model of the ADDMR is established including kinematic, dynamic and actuator models. Then, the desired trajectory is planned and generated via Bézier curve combined with cubic time mapping. The localization of the ADDMR is performed using the Monte Carlo Localization (MCL) technique and LiDAR point cloud data. Subsequently, the proposed FSMC utilizes nonlinear sliding surfaces inferred based on fuzzy logic to asymptotically attain the pose convergence. To investigate the feasibility and robustness of the proposed FSMC, simulations and experiments are conducted under different operation conditions. The results show that the proposed FSMC using LiDAR data in the feedback leads to excellent control performance even in the presence of friction torques. It was also shown through simulations and experiments that the established ADDMR model fits the actual ADDMR model very well.

Nowadays, an omnidirectional conveyor system has been introduced as a new means of package transportation. The aim of this study is to achieve trajectory-tracking and collision avoidance of multiple packages which has not been done before on an omnidirectional conveyor platform. Despite the kinematic similarity to omnidirectional mobile robots which have built-in sensors to measure velocities and accelerations, this system only measures the position of the transported package via external sensors which makes it a unique and intriguing area of research. To tackle this challenge, this study employs the proposed Fuzzy Sliding-mode Tracking Control (FSTC) and Fuzzy Inter-package Collision Avoidance (FICA) schemes. The FSTC has been enhanced with fuzzy sliding surfaces that take tracking errors as control inputs and linear forces as control outputs. Furthermore, using a fuzzified package distance and collision angle as inputs, the inference engine of FICA is designed to generate the deflection angle and force gain as outputs. Additionally, the conveyor platform is modeled and built by a multiple modular omnidirectional wheel system including the conveyor and actuator dynamics. To determine its desired motion trajectory, a planned 4th-order Bèzier curve is utilized. Then, to assess the effectiveness and robustness of the proposed methods, simulations are conducted under diverse conditions. The results indicate that the package position converges within a finite time frame, highlighting the superior trajectory-tracking capabilities of FSTC in spite of any disturbances injected into position feedback signals. Meanwhile, the proposed FICA has demonstrated its effectiveness by enabling the package to navigate through the omnidirectional conveyor platform while avoiding both stationary and moving obstacles.

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The paper proposes a Marker Detection Method for Estimating the Angle and Distance of Underwater Remotely Operated Vehicle (ROV) to Buoyant Boat. To keep the ROV aligned with the boat, a marker and visual recognition system are designed. The marker is placed facing down under the boat and a method is developed to recognize the angle and distance of the marker from a facing up camera on the ROV. By considering space, payload, heat dissipation, and buoyancy in a micro class ROV, there are limited options for computing power that can be utilized. This challenge demands a lightweight visual recognition technique for small computers. The proposed method consists of two steps. The marker designing step explains how the marker is constructed of simple components. The marker recognizing step is based on image processing that uses threshold and blob filtering. They are blob size and blob circularity filters which are used to eliminate unwanted information. The real-time orientation and distance estimation by using one camera are the superiority of this method. The proposed method has been tested by using an 11x11 cm2 marker size. The detection rate of the marker is 90% and can be detected up to 120 cm from the camera. The marker can be tilted up to 50° and still has an 80% detection rate. The method can estimate marker rotation angle accurately with a 1.75° average error. The method can estimate the distance between the marker and camera with a -0.62 cm average error. The blob filter is also proven to be superior to a regular dilating and eroding method.