Artificial intelligence, machine learning and deep learning in advanced robotics

 

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are all important technologies in the field of robotics. The term artificial intelligence (AI) describes a machine's capacity to carry out operations that ordinarily require human intellect, such as speech recognition, understanding of natural language, and decision-making. Robots can detect and interact with their surroundings, make judgments, and carry out difficult tasks with the aid of AI. A branch of AI known as "machine learning" uses algorithms to give robots the ability to learn from data and get better over time. It's possible to program robots to carry out certain jobs in robotics, such as grasping, object identification, and path planning. Artificial neural networks are used in deep learning, a type of machine learning (ML), to help computers learn from massive volumes of data. DL has been particularly useful in robotics for tasks such as image and speech recognition, natural language processing, and object detection. Together, these technologies have enabled the development of robots that can perform a wide range of tasks, from simple pick-and-place operations to complex manipulation and navigation in unstructured environments. The application of AI, ML, and DL in robotics has the potential to transform the field, enabling robots to become more intelligent, autonomous, and effective in a wide range of applications. Robotics is a rapidly evolving field, and the use of AI, ML, and DL is likely to continue to play a key role in shaping the future of robotics.

 

In advanced robotic systems, AI is used to create robots that can perceive, reason, and act autonomously in complex environments. Machine Learning is used to enable robots to learn from their experiences and improve their performance over time. Deep Learning is used to solve specific problems that are difficult to solve with traditional Machine Learning techniques, such as image and speech recognition. By combining these technologies, advanced robotics systems can be designed to perform complex tasks that were once thought impossible. The relationship between them are inclusive in terms of analysis and modification of advanced robotic systems. These are just a few examples of how AI, ML, and DL are used in robotics. Here are some examples of how they are used in different robotic systems as,

Object Detection and Recognition: -  

Object detection and recognition are critical tasks in robotics that have become possible thanks to deep learning. By training neural networks with massive amounts of labeled data, robots can identify and classify objects in their environment with high accuracy.

 

Predictive Maintenance: -  

Predictive maintenance is a maintenance approach that uses AI and ML to detect potential issues before they occur. By analyzing data from sensors and other sources, predictive maintenance algorithms can predict when a robot's components may fail, allowing for proactive repairs or replacements.

 

Gesture and Speech Recognition: -  

Gesture and speech recognition are also important applications of AI and ML in robotics. For example, robots like Pepper can recognize and respond to human gestures and speech, making them useful in a variety of contexts such as customer service or healthcare.

Robotic Surgery: -  

Robotic surgery is a field where AI and ML are revolutionizing the way operations are performed. By using advanced algorithms, robotic surgeons can assist human surgeons during complex procedures, reducing the risk of complications and improving outcomes. Surgical robots use AI, ML, and DL to aid surgeons in performing complex operations with greater precision and accuracy.

Medical applications: - 

DL techniques are particularly useful in analyzing medical images due to their ability to recognize patterns and features that are not easily identifiable by humans. This can help doctors to identify subtle changes in the images that may indicate the presence of disease. Machine learning models used in drug delivery for infectious disease. Ensemble algorithm, decision trees and random forest, instance based algorithms and artificial neural network are used to enhance drug delivery of infectious diseases.

Military robotics: - 

Robotics is used in military operations for tasks such as reconnaissance, surveillance, and bomb disposal. AI and ML algorithms are used to analyze data and make decisions based on the information gathered.

Agriculture: - 

AI and ML are being used to develop robots that can autonomously navigate and manage crops, increasing efficiency and reducing labor costs. Robotics is used to automate tasks in agriculture, such as planting, harvesting, and spraying. AI and ML algorithms are used to optimize the farming operations, such as predicting weather patterns, optimizing water usage, and monitoring crop health.

Service robotics: - 

Robotics is used to provide services to humans, such as cleaning, food delivery, and customer service. AI and ML algorithms are used to enable robots to interact with humans and understand their needs and preferences.

 

Autonomous driving: - 

AI and ML are used to help cars navigate roads and make driving decisions on their own. For example, self-driving cars use computer vision to detect and recognize objects on the road, and ML algorithms to learn and adapt to new situations and road conditions. For instance, robots like self-driving cars use AI to detect obstacles and predict traffic movements. Meanwhile, ML algorithms use data from sensors, cameras, and GPS to make navigation decisions.

 

Robotics manufacturing: - 

Robotics is used to automate tasks in manufacturing plants, such as assembly line tasks, painting, and welding. AI and ML algorithms are used to optimize the robotic operations, such as improving the efficiency and accuracy of movements.

 

There are various applications of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) in analysis and modification of advanced robotics. Some of the performance data of these methods in advanced robotics are discussed below:

Object Recognition: - 

Object recognition is a crucial task in robotics, and it is essential for autonomous navigation and manipulation. Deep learning techniques such as Convolutional Neural Networks (CNN) have achieved impressive results in object recognition.

 

Motion Planning: - 

Motion planning is a key task in robotics that involves finding a collision-free path for a robot to move from one point to another. Reinforcement Learning (RL) is a powerful machine learning technique that has been used to achieve impressive results in motion planning. For example, the Deep Deterministic Policy Gradient (DDPG) algorithm has been used to generate smooth and efficient paths for robotic manipulators.

 

Control: - 

Control is another important task in robotics, and it involves regulating the movement of robots. Deep Reinforcement Learning (DRL) has been used to achieve impressive results in control tasks. For example, the Proximal Policy Optimization (PPO) algorithm has been used to train a robotic arm to grasp and move objects.

 

Localization: - 

Localization is the process of determining the position of a robot in its environment. Machine Learning techniques such as Support Vector Machines (SVM) and Random Forests have been used to achieve impressive results in localization tasks. For example, a Random Forest-based method achieved an accuracy of 98.8% in a robot localization task.

 

Object Detection: - 

Object detection is the process 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.

AI in robotics can be used to enable robots to recognize objects, navigate complex environments, and even make decisions based on real-time data. ML can be used to teach robots to learn from experience and adapt to changing situations. DL can be used to enable robots to perform complex tasks that would otherwise be impossible using traditional programming methods. There are many programming languages used in Robotics, such as Python, C++, MATLAB, and ROS (Robot Operating System). These programming languages have various libraries and tools that make it easier to incorporate AI, ML, and DL into robotic systems. For example, Tensor Flow and Porch are popular deep learning frameworks which can be used in robotics programming applications. Tesla machines use AI, ML, and DL in a variety of ways. For example, Tesla's Autopilot system uses AI and ML to enable semi-autonomous driving, and to recognize and respond to traffic conditions. Tesla's manufacturing processes also use AI and ML to optimize production efficiency and quality.

 

CNC machining is a crucial technology in the development and maintenance of advanced robotics, allowing for the creation of highly precise and complex parts and components that are essential for the performance and reliability of robots. CNC machining is used in the maintenance and repair of robots. When a robot component fails, it is often necessary to create a replacement part that fits precisely and functions correctly. CNC machining makes it possible to quickly produce replacement parts that meet the required specifications, reducing downtime and ensuring the robot is back in operation as soon as possible.

 

To evaluate and enhance CNC machining in virtual environments, Soori et al. proposed virtual machining approaches . To examine and improve efficiency in the process of component manufacture using welding processes, Soori et al.  proposed an overview of recent advancements in friction stir welding techniques. Soori and Asamel  investigated the utilization of virtual machining technologies to lessen residual stress and deflection error during turbine blade five-axis milling operations. In order to evaluate and lower the cutting temperature during milling operations of difficult-to-cut components, Soori and Asmael  developed applications of virtualized machining systems. To enhance surface qualities during five-axis milling operations of turbine blades, Soori et al.  presented an enhanced virtual machining technique. In order to minimize deflection error during five-axis milling procedures of impeller blades, Soori and Asmael  developed virtual milling procedures. In order to analyze and enhance the parameter optimization approach of machining operations, Soori and Asmael offered a synopsis of current advances from published works. In order to increase energy usage effectiveness, data quality and availability throughout the supply chain, and precision and reliability during the component production process, Dastres et al.  conducted a research of RFID-based wireless manufacturing systems. In order to increase efficiency and added value in component production processes utilizing CNC machining operations, Soori et al.  examined machine learning and artificial intelligence in CNC machine tools. To measure and reduce residual stress during machining operations, Soori and Arezoo [provided a review in the subject. Soori and Arezoo described optimal machining settings utilizing the Taguchi optimization technique to reduce surface integrity and residual stress during grinding operations of Inconel 718. Soori and Arezoo investigated several tool wear prediction techniques to lengthen cutting tool life during machining processes. In order to increase efficiency in the component production process, Soori and Asmael studied computer assisted process planning. In order to provide decision - making support systems for data warehouse operations, Dastres and Soori discussed advancements in web-based decision support systems. In order to develop the implementation of artificial neural networks in performance enhancement of engineering products, Dastres and Soori  presented a review of recent research and uses of artificial neural networks in a variety of disciplines, including risk analysis systems, drone control, welding quality analysis, and computer quality analysis. To minimize cutting tool wear in drilling operations, application of virtual machining system is developed by Soori and Arezoo . To enhance quality of prodcued parts using abrasive water jet machining, residual stress and surface roughness are minimized by Soori and Arezoo . Dastres and Soori  discussed using information and communication technology in environmental conservation to lessen the impact of technological progress on natural disasters. Dastres and Soori  proposed the secure socket layer in order to improve network and data online security. In order to create the methodology of decision support systems by assessing and recommending the gaps between presented methodologies, Dastres and Soori  analyze the advancements in web-based decision support systems. Dastres and Soori  provided an assessment of current developments in network threats in order to improve security measures in networks. Dastres and Soori  analyze image processing and analysis systems to expand the possibilities of image processing systems in many applications.

 

AI, ML and DL are transforming the field of advanced robotics by enabling the development of intelligent machines that can perform complex tasks with high accuracy and efficiency. A review in recent development of AI, ML and DL in advanced robotics system is presented and different applications of the systems in modifications of robots are also discussed in the study. The gaps between the published research works in the applications of AI, ML and DL in advanced robotics system are also suggested as future research works in the interesting research field. As a result, performances of advanced robots in different applications can be analyzed and modified by reviewing the applications of AI, ML and DL in advanced robotics system in the study. Thus, accuracy as well as productivity in applications of advanced robots can be enhanced.

 

2. Advantages of AI, ML and DL applications in advanced robotics:-

AI (Artificial Intelligence), ML (Machine Learning), and DL (Deep Learning) applications have brought about significant advancements in the field of robotics. Some of these advantages of AI, ML and DL applications in advanced robotics include:

Automation:- 

AI, ML, and DL can automate many repetitive and mundane tasks in robotics, freeing up human resources to focus on more complex tasks. 

Enhanced accuracy:- 

These technologies can improve the accuracy and precision of robotic systems, reducing errors and improving overall performance.

 

Adaptability:- 

AI-powered robots can adapt to changing environments and tasks, making them highly versatile and useful in a range of industries and applications.

 

Predictive Maintenance:- 

Machine learning algorithms can help robots to predict when maintenance or repairs are required, leading to reduced downtime and cost savings .

 

Improved Decision Making:- 

AI and ML algorithms can analyze large amounts of data and make informed decisions based on that data, allowing robots to make better decisions and take appropriate actions.

 

Improved efficiency:- 

By optimizing processes and reducing waste, AI, ML, and DL can improve the overall efficiency of robotics systems, resulting in cost savings and increased productivity.

 

Better decision-making:- 

AI, ML, and DL can enable robots to make better decisions based on data analysis and pattern recognition, leading to improved performance and outcomes.

 

Adaptability:- 

These technologies can enable robots to adapt to changing environments and situations, making them more versatile and capable of handling a wider range of tasks.

 

Increased safety:- 

By automating hazardous or dangerous tasks, AI, ML, and DL can improve safety in the workplace, reducing the risk of accidents and injuries.

 

Cost Reduction:- 

The implementation of AI and ML applications in advanced robotics can significantly reduce costs associated with labor and maintenance.

 

Improved Decision-making:- 

By using AI and ML algorithms, robots can make informed decisions based on data analysis, resulting in better overall performance.

  

Overall, the use of AI, ML, and DL in robotics has the potential to revolutionize the field and unlock new levels of performance, efficiency, and safety.

 

3. Challenges of AI, ML and DL in robotics applications

While these technologies offer many benefits, they also pose significant challenges. One of the biggest challenges is the need for large amounts of high-quality data to train AI and ML algorithms. However, data collection, labeling, and annotation can be expensive and time-consuming, and the data may be noisy or biased, which can affect the accuracy and reliability of the models. This can be particularly challenging in robotics, where data can be difficult to obtain and may be subject to noise and uncertainty. In addition, robotics applications often require real-time processing, which can be computationally expensive and may require specialized hardware. Furthermore, in order to analyze massive volumes of data, build models, and make predictions in real-time, AI/ML/DL systems need a lot of processing power. This can be difficult in robotics applications since robots are constrained by energy and computing power limitations.

 

Robotics applications often require robots to operate in dynamic and changing environments which need adaptability in operations. AI/ML/DL models must be designed to adapt to new situations and learn from experience, which can be challenging. Another challenge is the need for robots to be able to operate safely and effectively in a wide range of environments . As robots become more autonomous and interact with humans, ensuring their safety becomes a critical challenge. AI/ML/DL algorithms must be designed to prevent accidents, detect and respond to potential hazards, and avoid collisions with humans and other objects .This requires the development of robust AI and ML algorithms that can handle unpredictable situations and adapt to changing conditions. It also requires the development of sensors and other hardware that can provide accurate and reliable data about the robot's surroundings .In addition, there are ethical and societal challenges associated with the use of AI and robotics. For example, there are concerns about the impact of automation on jobs and the potential for AI systems to be biased or to perpetuate existing inequalities. There are also concerns about the potential for robots to be used for harmful purposes, such as military applications or surveillance .

 

Overall, while AI, ML, and DL offer many opportunities for robotics, there are also significant challenges that must be addressed in order to realize their full potential. Researchers and engineers in this field must work to develop robust algorithms, hardware, and ethical frameworks that can support the safe and effective use of these technologies.

 

4. Applications of AI, ML and DL in advanced industrial robots

There are many potential applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in advanced manufacturing robots. AI and ML can be used to analyze production data and optimize production planning. AI and ML can be used to perform quality control checks on manufactured products. AI algorithms can identify defects in products and alert the production team to make necessary adjustments in real-time. This helps manufacturers to identify and eliminate bottlenecks, reduce waste, and increase productivity. Some of these applications include:

Quality Control:- 

AI, ML, and DL algorithms can be used to monitor the manufacturing process in real-time and identify defects or anomalies in the products being produced. This can help improve the quality of the products and reduce the need for human intervention in the quality control process.

 

Predictive Maintenance:- 

When industrial equipment is predicted to fail, maintenance may be carried out before a breakdown happens thanks to the usage of AI and ML. By doing so, downtime may be decreased and overall productivity can rise.

 

Autonomous Robots:- 

Advanced manufacturing robots can be equipped with AI and ML algorithms that enable them to operate autonomously. This can be particularly useful in situations where human intervention is not practical or safe, such as in hazardous environments or in situations where precision is critical.

 

Assembly robots:- 

AI, ML, and DL technologies are enabling robots during assembly process to work smarter, faster, and more efficiently than ever before, and are helping manufacturers to improve quality, reduce costs, and increase productivity. AI can be used to control and optimize robotic assembly processes. It can enable robots to adapt to changing conditions, work collaboratively with human operators, and learn from past experiences to improve future performance. Also, AI can be used to improve the safety of assembly robots by monitoring their movements and identifying potential hazards. This can help to prevent accidents and reduce the risk of injury to workers. Moreover, AI can be used to optimize the workflow of assembly robots, by analyzing data on the production process and identifying areas where efficiency can be improved.

 

Process Optimization:- 

AI, ML, and DL can be employed to determine the most effective way to make a product in order to improve the manufacturing process. This can save waste and boost overall effectiveness.

 

Supply Chain Optimization:- 

AI and ML can be used to optimize the supply chain by predicting demand and ensuring that the right materials are available at the right time. This can help reduce inventory costs and improve overall efficiency

 

Collaborative Robots:- 

AI and ML can be used to enable robots to work alongside human workers in a collaborative environment. This can help improve productivity and safety by allowing robots to perform repetitive or dangerous tasks while humans focus on more complex tasks.

 

AI, ML, and DL have a wide range of applications in advanced manufacturing, including in robotics and automated guided vehicles (AGVs) . The technologies are essential for optimizing the performance of advanced manufacturing robots and AGVs, allowing them to work more efficiently, accurately, and safely in a variety of settings. Some examples of these applications include:

Object detection and recognition:- AI and ML algorithms can be used to identify and recognize different objects in a manufacturing environment. This can be useful for robots and AGVs to navigate and interact with their surrounding.

 

Real-time decision making:- 

AI algorithms can enable robots and AGVs to make real-time decisions based on sensor data, allowing them to adapt to changing conditions in a manufacturing environment.

 

Path optimization:- 

AI algorithms can be used to optimize the path that a robot or AGV takes through a manufacturing facility, reducing travel time and increasing efficiency .

 

 

Application of AI in DL in advanced manufacturing process and robots. This flowchart explains a crucial idea from the viewpoint of system requirements when assessing the applicability of any AI technology to guarantee that overall objectives are satisfied and sub-optimization is avoided.

 

 

Overall, by increasing productivity, cutting costs, and raising product quality, the employment of AI, ML, and DL in advanced industrial robots has the potential to completely transform the manufacturing sector.

 

5. Applications of AI, ML and DL in advanced transportation systems

Artificial intelligence (AI), machine learning (ML), and deep learning (DL) are increasingly being used in advanced transportation systems to improve safety, efficiency, and convenience. Here are some of the most notable applications of these technologies in transportation:

Intelligent Transportation Systems (ITS):- 

AI-based ITS can help improve traffic flow, reduce congestion, and enhance safety on roads. ML algorithms can analyze traffic patterns and optimize signal timings at intersections, while DL algorithms can identify potential hazards and alert drivers in real-time.

 

Traffic Management:- 

AI, ML, and DL techniques are used to monitor and analyze traffic patterns. This helps in optimizing traffic flow and reducing congestion. Applications of AI in intelligent traffic management. Smart cameras and traffic lights which are controlled by using the AI can monitor and analyze traffic patterns in order to increase the performances of traffic management systems.

  

Autonomous Vehicles:-  

AI, ML, and DL are essential components of autonomous vehicles. These technologies enable vehicles to perceive and interpret their surroundings, make decisions based on data, and navigate roads safely without human intervention.

 

Intelligent Transportation Systems (ITS):- 

AI, ML, and DL algorithms are used to develop ITS. ITS includes technologies like smart traffic signals, electronic toll collection systems, and intelligent parking systems, which help in optimizing the transportation system.

 

Predictive Maintenance:- 

ML algorithms can analyze data from sensors installed on vehicles and predict when maintenance is needed, allowing for proactive repairs and reducing downtime. This can be especially useful in large fleets of vehicles, such as those used in public transportation.

 

Smart Parking:- 

AI-based parking systems can help drivers find available parking spots quickly and reduce congestion in busy areas. ML algorithms can analyze parking data to optimize parking space usage, while DL algorithms can recognize license plates and enforce parking regulations.

 

Route Optimization:- 

ML algorithms can optimize delivery routes for logistics companies, reducing travel time, and improving fuel efficiency. This can result in cost savings and a reduced environmental footprint.

 

Road Safety:- 

AI, ML, and DL can be used to improve road safety by analyzing traffic patterns and identifying areas prone to accidents. Algorithms can be used to predict and prevent accidents by alerting drivers of potential hazards and suggesting safer routes.

 

Intelligent Public Transportation:- 

AI and ML can be used to optimize public transportation schedules and routes, providing passengers with more convenient and efficient services. DL algorithms can also be used to monitor passenger behavior and detect potential safety issues.

 

 

Overall, AI, ML, and DL are becoming increasingly important in the development and operation of advanced transportation systems, helping to improve efficiency, safety, and sustainability. The applications of AI, ML, and DL in advanced transportation systems have the potential to revolutionize the way we travel, making transportation safer, more efficient, and more sustainable.