AI, ML, and DL for Advanced Robotics: A Review
AI, ML, and DL for Advanced Robotics
AI, ML, and DL are bringing some major transformations in the field of advanced robotics. From performing simple tasks to complex navigation and manipulation, these technologies have enabled the development of robots. These have increased the potential of robots to become more intelligent and effective in their functions. AI, ML, and DL are used for the development of collaborative robots that can along with humans adapt to the changing environment. Some of the wide applications of AI, ML, and DL in the field of advanced robotics include object recognition, natural language processing, autonomous navigation, and predictive maintenance.
Here are some examples of the uses of AI, ML and DL for Advanced robotic systems:
Object Detection and Recognition
Object detection and recognition are one of the essential tasks in robotics. With the use of deep learning object detection and recognition are enabled by training neural networks with massive amounts of labeled data, so robots can identify objects with increased accuracy.
Predictive maintenance
A predictive maintenance approach is a maintenance approach that uses AI and ML to detect potential issues that are likely to occur in the future. By analyzing data from sensors, predictive maintenance algorithms can make a prediction when a robot’s components may fail and require repairing or certain replacements.
Gesture and Speech Recognition
One of the important applications of AI and ML is the recognition of speech and gesture. For example: Robots like Pepper can recognize and respond to human gestures and speech. This makes them compatible to be used for customer service or in the healthcare sector
Robotic Surgery
AI and ML are revolutionizing the field of robotic surgery. Robotic surgeons can assist human surgeons with the help of advanced algorithms to perform complex operations by reducing the risk of complications. This helps the human surgeons to perform operations with accuracy and greater precision.
Robotics manufacturing
AI and ML optimize robotic operations with increased efficiency and accuracy. It is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding.
There are various applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in the modification of advanced robotics. Here are some of the performance data of these methods in advanced robotics:
Object Recognition
Object recognition is essential for autonomous navigation and manipulation in robotics. Deep learning techniques as in Convolutional Neural Networks (CNN) have achieved impressive results in object recognition.
Motion Planning
Motion planning is another important task in robotics. Motion planning refers to finding an obstruction-free path for a robot to move freely from one point to another. Reinforcement Learning is a machine learning technique that helps to achieve impressive results in motion planning.
Control
Control involves regulating the movement of robots. Deep Reinforcement Learning is used to achieve impressive results in the task of controlling. For example: the Proximal Policy Optimization (PPO) algorithm has been used to train robotic arms to hold and move objects with proper control.
Localization
Machine Learning techniques such as Support Vector Machines (SVM) and Random Forests have been used to perform and achieve impressive results in localization tasks. Localization refers to determining the position of a robot in the environment. For example, a Random Forest-based method achieved maximum accuracy in a robot localization task.
Object Detection
Object detection is the way of detecting and localizing objects in an image. Deep Learning techniques such as Faster R-CNN and YOLO have achieved impressive results in object detection tasks.
With AI in robotics, robots can make decisions based on real time. With many programming languages used in Robotics, such as Python, C++, MATLAB, and ROS (Robot Operating System), libraries and tools have made it easier to incorporate AI, ML, and DL into robotic systems. One significant example is Tesla’s Autopilot system uses AI and ML to enable semi-autonomous driving and to recognize and respond to traffic conditions.
