Current Issue : January-March Volume : 2024 Issue Number : 1 Articles : 5 Articles
Underwater robot is a type of robot specially designed and configured to operate underwater, especially in seas, rivers, and lakes. The main purpose of underwater robots is to perform various tasks and missions underwater, which are often difficult or dangerous for humans to carry out. The method used to design the underwater robot is with a smartphone control system. The robot can move according to the user's commands by being connected through a internet network, allowing the robot and smartphone to be interconnected. This underwater robot is equipped with a camera and two lamps, and the images captured by the camera are displayed on a personal computer. Meanwhile, the lights are controlled by the user using a smartphone. The research results show that the underwater robot can be controlled to a depth of 0.5 meters with an average button press speed of 1 second in floating conditions and 2.9 seconds at a depth of 15 centimeters. The application of this robot can take photos, display good images, and monitor activities underwater. In addition, the captured image from underwater robot are also good and the image produced are clearly visible both on land and in the water....
This paper presents the design and synthesis of a dynamic output feedback neural network controller for a non-holonomic mobile robot. First, the dynamic model of a non-holonomic mobile robot is presented, in which these constraints are considered for the mathematical derivation of a feasible representation of this kind of robot. Then, two control strategies are provided based on kinematic control for this kind of robot. The first control strategy is based on driftless control; this means that considering that the velocity vector of the mobile robot is orthogonal to its restriction, a dynamic output feedback and neural network controller is designed so that the control action would be zero only when the velocity of the mobile robot is zero. The Lyapunov stability theorem is implemented in order to find a suitable control law. Then, another control strategy is designed for trajectory-tracking purposes, in which similar to the driftless controller, a kinematic control scheme is provided that is suitable to implement in more sophisticated hardware. In both control strategies, a dynamic control law is provided along with a feedforward neural network controller, so in this way, by the Lyapunov theory, the stability and convergence to the origin of the mobile robot position coordinates are ensured. Finally, two numerical experiments are presented in order to validate the theoretical results synthesized in this research study. Discussions and conclusions are provided in order to analyze the results found in this research study....
This paper examines the challenges associated with the efficient planning and operation of an E‑grocery delivery system using Autonomous Delivery Robots (ADR) during unforeseen events. The primary objective is to minimize unfulfilled customer demands rather than focusing solely on cost reduction, considering the humanitarian aspect. To address this, a two‑echelon vehicle routing problem is formulated, taking into account stochastic service times and demands. Two models, namely a deterministic model and a chance‑constraint model, are employed to solve this problem. The results demonstrate that the chance‑constraint model significantly reduces unmet demands compared to the deterministic model, particularly when the delivery deadline has a broad time window and the ADR/van speed ratio is moderate....
In recent years, multi-robot systems have been widely used in different fields. In order to ensure that each robot in the system can reach the target point and complete the task correctly, the path planning of the multi-robot system is significant. The path planning of the multi-robot system can effectively ensure that the robot selects the appropriate target and completes the task, and during the planning, it can ensure that the task completion time of the total system is the shortest or the total cost is the lowest. Experiments are carried out without considering the kinematics and physical properties of the robot itself, and it is assumed that each robot can accurately know its own position information. In this paper, we propose an A* algorithm for target trajectory planning that integrates multiple trajectories and minimizes running costs by task assignment through a linear programming model. At the same time, by adjusting the planning method of the estimated cost, the error caused by the difference between the nonlinear path and the straight line distance is reduced. After experiments, the effectiveness of the linear programming model and the difference in computation time and running time between A* and RRT* algorithms are demonstrated. What’s more, A* outperforms RRT* in completion time, while RRT* has a shorter computation time....
The traditional two-wheeled self-balancing robot can travel quickly in a flat road environment, and it is easy to destabilize and capsize when passing through a bumpy road. To improve the passing ability of a two-wheeled robot, a new wheel-legged two-wheeled robot is developed. A seven-link leg structure is proposed through the comprehensive design of mechanism configuration, which decouples the balanced motion and leg motion of the robot. Based on the Euler–Lagrange method, the dynamic model of the system is obtained by applying the nonholonomic dynamic Routh equation in the generalized coordinate system. The robot’s state space is divided according to the robot’s height, and the Riccati equation is solved in real-time by the linear quadratic regulator (LQR) method to complete the balance and motion control of the robot. The robot leg motion control is achieved based on the active disturbance rejection control (ADRC) way. A robot simulation model is built on Recurdyn to verify the algorithm’s feasibility, and then an experimental prototype is built to demonstrate the algorithm’s effectiveness. The experimental results show that the control method based on LQR and ADRC can make the robot pass through the bumpy road....
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