Acrome blog

The Virtual Pivot Point (VPP) is an essential feature for precise motion control in robotic applications. Integrating VPP capabilities with Stewart Platforms enables the easier development of complex systems.

In this project, we investigate the application of PID control and Q-learning algorithms to the Ball Balancing Table (BBT) in order to solve a maze. An open-source tool called the BBT makes it possible to experiment with control systems directly. The device can guide a ball through a maze by adjusting the tilt of the table, imparting important knowledge about reinforcement learning, control theory, and feedback systems. The table's orientation is adjusted by using a PID controller in response to real-time feedback from the ball's position. A matrix of 0s and 1s with open paths and walls, respectively, is used to depict the maze. The system associates movements with the Q-learning reinforcement learning algorithm to learn how to move the ball through the maze.

This project used an LLM (Large Language Model) to control a robot made with SMD (Smart Motion Devices) components by interpreting natural language commands and converting them into precise robot actions. The sequence is as follows: 1) Command Processing: The LLM interprets user commands 2) Function Calling: Maps commands to robot functions 3) Action Sequencing: Organizes and executes complex tasks 4) Robot's Motion: Executed via Smart Motion Devices high-level APIs. A Raspberry Pi manages the robot’s control, and a user interface created with Streamlit allows for natural language command input.

The goal of this project is to use a Flask API to control a robot that was constructed using Smart Motion Devices (SMD) from ACROME. The Raspberry Pi-based solution enables remote robot control via a web interface or mobile device by using Flask to parse HTTP requests and translate them into motor control commands. SMD offers accurate motor control, and Flask makes it possible for a thin and effective control interface. Three main commands are rotation, radial movement, and linear movement. The API is RESTful and communicates via common HTTP methods like POST. Applications such as smart homes, robotics research, and industrial automation can benefit from the system. Future developments might include improved security for crucial use cases and AI integration for autonomy.

This article explores the significance of AI object tracking and highlights the potential of an open-source mobile robot project using MobileNet-SSD library and ACROME SMD products. The integration of AI and SMD products exemplifies the revolutionary impact of AI-supported object tracking in robotics.

This blog delves into the world of stepper motors. It covers information about their operation, types, and the details of the stepper motor drivers. Acrome's SMD driver modules for step motor control are highlighted in the context.

This blog post describes the basics of the 5-bar parallel robots -aka- five bar linkage and how to build one using ACROME's Smart Motion Devices and DC brushed motors. Sample Python codes are provided.

This article provides information on building a 4-DoF robotic arm in a do-it-yourself fashion. The bill of materials information and links to CAD files of the linkage and mounting parts are given. The article recommends using Smart Motion Devices for controlling the actuators, as this will make things easier and cleaner in cabling.

This blog post is an introduction on building Autonomous Mobile Robots. Acrome's Autonomus Mobile Robot is given as an example system with its bill of materials (BOM) list, example code and further development topics of SLAM, AI and ROS.

An introductory article about robotic manipulators. The article focuses on robotic manipulators, especially the serial robotic arms, and provides information on how to build such a robotic arm with a high-level list of requirements.