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…

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,…

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…

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…

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.…

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…

Introduction Hierarchical Neural Networks (HNNs) are a powerful class of deep learning models designed to capture the complex, multi-level structures inherent in data. They are particularly effective in tasks where data can be organized in a hierarchical structure, such as natural language processing, image segmentation, and multi-scale pattern recognition. At MHTECHIN, we specialize in…

Graph Neural Networks (GNNs) have emerged as a powerful class of models designed to work with graph-structured data. These models have revolutionized fields such as social network analysis, drug discovery, and recommendation systems, where data relationships are better represented in the form of nodes (entities) and edges (relationships). At MHTECHIN, we specialize in applying…

In the world of deep learning, ensuring model robustness against adversarial attacks is critical for deploying AI systems in real-world applications. Adversarial attacks involve subtly perturbing input data to deceive models into making incorrect predictions, posing significant security and reliability risks. Adversarial training has emerged as a leading defense mechanism to mitigate these threats…

Introduction In deep learning (DL), data is the cornerstone of success. Managing the flow of data—from collection to preprocessing, storage, and feeding it to the model—is crucial for building efficient and scalable AI systems. A well-designed data pipeline ensures that data is processed accurately, efficiently, and in a timely manner. At MHTECHIN, we specialize…