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Introduction Machine Vision is an essential component of modern robotics, enabling robots to perceive and interpret their environment through cameras and sensors. This ability is crucial for performing tasks such as object recognition, navigation, quality inspection, and interaction with humans. At MHTECHIN, we integrate advanced machine vision technologies into our robotic systems to enhance…
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Introduction Inverse Kinematics (IK) is a fundamental concept in robotics, particularly in the control of robotic arms, mobile manipulators, and other articulated robotic systems. IK enables robots to calculate the joint angles or positions required to achieve a desired end-effector position and orientation in space. At MHTECHIN, we leverage advanced Inverse Kinematics algorithms to…
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Introduction Proportional-Integral-Derivative (PID) controllers are one of the most fundamental and widely used feedback control mechanisms in robotics. A PID controller works by adjusting the control inputs to a system in a manner that reduces errors between the desired output (setpoint) and the actual output of the system. In robotics, PID controllers are used…
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Introduction Simultaneous Localization and Mapping (SLAM) is a crucial technique in robotics, enabling a robot to map an environment while simultaneously determining its position within that map. SLAM plays a vital role in autonomous robots, especially in applications where global positioning systems (GPS) are unavailable or unreliable, such as indoor navigation, autonomous vehicles, and…
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Introduction Path planning is a critical component in robotics, enabling robots to navigate efficiently from a starting point to a goal while avoiding obstacles and optimizing various criteria, such as time or energy. At MHTECHIN, we implement and optimize a variety of path planning algorithms, including widely used ones like A* (A-star) and RRT…
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Introduction In the field of robotics, understanding the kinematics and dynamics of robots is essential for developing precise and effective robotic systems. Kinematics refers to the study of motion without considering the forces that cause it, while dynamics takes these forces into account. At MHTECHIN, we apply principles of kinematics and dynamics to design,…
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Introduction Swarm robotics is a field of robotics that focuses on the coordination of multiple autonomous robots working together to complete a task. Inspired by the behavior of social organisms like ants, bees, or flocks of birds, swarm robotics leverages decentralized systems where each robot performs a simple task but contributes to the overall…
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Introduction Deep Reinforcement Learning (DRL) is a subset of machine learning where an agent learns to make decisions by interacting with its environment, receiving feedback through rewards or penalties, and optimizing its actions to maximize long-term rewards. In robotics, DRL has shown tremendous potential in enabling machines to learn complex tasks autonomously, with minimal…
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Introduction In deep learning, data is often represented in multidimensional structures known as tensors. These high-dimensional data structures arise in various applications, including computer vision, natural language processing, and recommendation systems. Tensor decomposition is a powerful mathematical tool used to break down these high-dimensional tensors into lower-dimensional components, facilitating better analysis and efficient computations.…
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Introduction In deep learning, one of the most important hyperparameters that significantly affects the performance and convergence of a model is the learning rate. Choosing the right learning rate is critical; if it’s too high, the model may overshoot the optimal solution, and if it’s too low, training can be slow and stuck in…