Artificial Intelligence with PHP
  • Getting Started
    • Introduction
    • Audience
    • How to Read This Book
    • Glossary
    • Contributors
    • Resources
    • Changelog
  • Artificial Intelligence
    • Introduction
    • Overview of AI
      • History of AI
      • How Does AI Work?
      • Structure of AI
      • Will AI Take Over the World?
      • Types of AI
        • Limited Memory AI
        • Reactive AI
        • Theory of Mind AI
        • Self-Aware AI
    • AI Capabilities in PHP
      • Introduction to LLM Agents PHP SDK
      • Overview of AI Libraries in PHP
    • AI Agents
      • Introduction to AI Agents
      • Structure of AI Agent
      • Components of AI Agents
      • Types of AI Agents
      • AI Agent Architecture
      • AI Agent Environment
      • Application of Agents in AI
      • Challenges in AI Agent Development
      • Future of AI Agents
      • Turing Test in AI
      • LLM AI Agents
        • Introduction to LLM AI Agents
        • Implementation in PHP
          • Sales Analyst Agent
          • Site Status Checker Agent
    • Theoretical Foundations of AI
      • Introduction to Theoretical Foundations of AI
      • Problem Solving in AI
        • Introduction
        • Types of Search Algorithms
          • Comparison of Search Algorithms
          • Informed (Heuristic) Search
            • Global Search
              • Beam Search
              • Greedy Search
              • Iterative Deepening A* Search
              • A* Search
                • A* Graph Search
                • A* Graph vs A* Tree Search
                • A* Tree Search
            • Local Search
              • Hill Climbing Algorithm
                • Introduction
                • Best Practices and Optimization
                • Practical Applications
                • Implementation in PHP
              • Simulated Annealing Search
              • Local Beam Search
              • Genetic Algorithms
              • Tabu Search
          • Uninformed (Blind) Search
            • Global Search
              • Bidirectional Search (BDS)
              • Breadth-First Search (BFS)
              • Depth-First Search (DFS)
              • Iterative Deepening Depth-First Search (IDDFS)
              • Uniform Cost Search (UCS)
            • Local Search
              • Depth-Limited Search (DLS)
              • Random Walk Search (RWS)
          • Adversarial Search
          • Means-Ends Analysis
      • Knowledge & Uncertainty in AI
        • Knowledge-Based Agents
        • Knowledge Representation
          • Introduction
          • Approaches to KR in AI
          • The KR Cycle in AI
          • Types of Knowledge in AI
          • KR Techniques
            • Logical Representation
            • Semantic Network Representation
            • Frame Representation
            • Production Rules
        • Reasoning in AI
        • Uncertain Knowledge Representation
        • The Wumpus World
        • Applications and Challenges
      • Cybernetics and AI
      • Philosophical and Ethical Foundations of AI
    • Mathematics for AI
      • Computational Theory in AI
      • Logic and Reasoning
        • Classification of Logics
        • Formal Logic
          • Propositional Logic
            • Basics of Propositional Logic
            • Implementation in PHP
          • Predicate Logic
            • Basics of Predicate Logic
            • Implementation in PHP
          • Second-order and Higher-order Logic
          • Modal Logic
          • Temporal Logic
        • Informal Logic
        • Semi-formal Logic
      • Set Theory and Discrete Mathematics
      • Decision Making in AI
    • Key Application of AI
      • AI in Astronomy
      • AI in Agriculture
      • AI in Automotive Industry
      • AI in Data Security
      • AI in Dating
      • AI in E-commerce
      • AI in Education
      • AI in Entertainment
      • AI in Finance
      • AI in Gaming
      • AI in Healthcare
      • AI in Robotics
      • AI in Social Media
      • AI in Software Development
      • AI in Adult Entertainment
      • AI in Criminal Justice
      • AI in Criminal World
      • AI in Military Domain
      • AI in Terrorist Activities
      • AI in Transforming Our World
      • AI in Travel and Transport
    • Practice
  • Machine Learning
    • Introduction
    • Overview of ML
      • History of ML
        • Origins and Early Concepts
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Terms and Principles
      • Machine Learning Life Cycle
      • Problems and Challenges
    • ML Capabilities in PHP
      • Overview of ML Libraries in PHP
      • Configuring an Environment for PHP
        • Direct Installation
        • Using Docker
        • Additional Notes
      • Introduction to PHP-ML
      • Introduction to Rubix ML
    • Mathematics for ML
      • Linear Algebra
        • Scalars
          • Definition and Operations
          • Scalars with PHP
        • Vectors
          • Definition and Operations
          • Vectors in Machine Learning
          • Vectors with PHP
        • Matrices
          • Definition and Types
          • Matrix Operations
          • Determinant of a Matrix
          • Inverse Matrices
          • Cofactor Matrices
          • Adjugate Matrices
          • Matrices in Machine Learning
          • Matrices with PHP
        • Tensors
          • Definition of Tensors
          • Tensor Properties
            • Tensor Types
            • Tensor Dimension
            • Tensor Rank
            • Tensor Shape
          • Tensor Operations
          • Practical Applications
          • Tensors in Machine Learning
          • Tensors with PHP
        • Linear Transformations
          • Introduction
          • LT with PHP
          • LT Role in Neural Networks
        • Eigenvalues and Eigenvectors
        • Norms and Distances
        • Linear Algebra in Optimization
      • Calculus
      • Probability and Statistics
      • Information Theory
      • Optimization Techniques
      • Graph Theory and Networks
      • Discrete Mathematics and Combinatorics
      • Advanced Topics
    • Data Fundamentals
      • Data Types and Formats
        • Data Types
        • Structured Data Formats
        • Unstructured Data Formats
        • Implementation with PHP
      • General Data Processing
        • Introduction
        • Storage and Management
          • Data Security and Privacy
          • Data Serialization and Deserialization in PHP
          • Data Versioning and Management
          • Database Systems for AI
          • Efficient Data Storage Techniques
          • Optimizing Data Retrieval for AI Algorithms
          • Big Data Considerations
            • Introduction
            • Big Data Techniques in PHP
      • ML Data Processing
        • Introduction
        • Types of Data in ML
        • Stages of Data Processing
          • Data Acquisition
            • Data Collection
            • Ethical Considerations in Data Preparation
          • Data Cleaning
            • Data Cleaning Examples
            • Data Cleaning Types
            • Implementation with PHP
          • Data Transformation
            • Data Transformation Examples
            • Data Transformation Types
            • Implementation with PHP ?..
          • Data Integration
          • Data Reduction
          • Data Validation and Testing
            • Data Splitting and Sampling
          • Data Representation
            • Data Structures in PHP
            • Data Visualization Techniques
          • Typical Problems with Data
    • ML Algorithms
      • Classification of ML Algorithms
        • By Methods Used
        • By Learning Types
        • By Tasks Resolved
        • By Feature Types
        • By Model Depth
      • Supervised Learning
        • Regression
          • Linear Regression
            • Types of Linear Regression
            • Finding Best Fit Line
            • Gradient Descent
            • Assumptions of Linear Regression
            • Evaluation Metrics for Linear Regression
            • How It Works by Math
            • Implementation in PHP
              • Multiple Linear Regression
              • Simple Linear Regression
          • Polynomial Regression
            • Introduction
            • Implementation in PHP
          • Support Vector Regression
        • Classification
        • Recommendation Systems
          • Matrix Factorization
          • User-Based Collaborative Filtering
      • Unsupervised Learning
        • Clustering
        • Dimension Reduction
        • Search and Optimization
        • Recommendation Systems
          • Item-Based Collaborative Filtering
          • Popularity-Based Recommendations
      • Semi-Supervised Learning
        • Regression
        • Classification
        • Clustering
      • Reinforcement Learning
      • Distributed Learning
    • Integrating ML into Web
      • Open-Source Projects
      • Introduction to EasyAI-PHP
    • Key Applications of ML
    • Practice
  • Neural Networks
    • Introduction
    • Overview of NN
      • History of NN
      • Basic Components of NN
        • Activation Functions
        • Connections and Weights
        • Inputs
        • Layers
        • Neurons
      • Problems and Challenges
      • How NN Works
    • NN Capabilities in PHP
    • Mathematics for NN
    • Types of NN
      • Classification of NN Types
      • Linear vs Non-Linear Problems in NN
      • Basic NN
        • Simple Perceptron
        • Implementation in PHP
          • Simple Perceptron with Libraries
          • Simple Perceptron with Pure PHP
      • NN with Hidden Layers
      • Deep Learning
      • Bayesian Neural Networks
      • Convolutional Neural Networks (CNN)
      • Recurrent Neural Networks (RNN)
    • Integrating NN into Web
    • Key Applications of NN
    • Practice
  • Natural Language Processing
    • Introduction
    • Overview of NLP
      • History of NLP
        • Ancient Times
        • Medieval Period
        • 15th-16th Century
        • 17th-18th Century
        • 19th Century
        • 20th Century
        • 21st Century
        • Coming Years
      • Key Concepts in NLP
      • Common Challenges in NLP
      • Machine Learning Role in NLP
    • NLP Capabilities in PHP
      • Overview of NLP Libraries in PHP
      • Challenges in NLP with PHP
    • Mathematics for NLP
    • NLP Techniques
      • Basic Text Processing with PHP
      • NLP Workflow
      • Popular Tools and Frameworks for NLP
      • Techniques and Algorithms in NLP
        • Basic NLP Techniques
        • Advanced NLP Techniques
      • Advanced NLP Topics
    • Integrating NLP into Web
    • Key Applications of NLP
    • Practice
  • Computer Vision
    • Introduction
  • Overview of CV
    • History of CV
    • Common Use Cases
  • CV Capabilities in PHP
  • Mathematics for CV
  • CV Techniques
  • Integrating CV into Web
  • Key Applications of CV
  • Practice
  • Robotics
    • Introduction
  • Overview of Robotics
    • History and Evolution of Robotics
    • Core Components
      • Sensors (Perception)
      • Actuators (Action)
      • Controllers (Processing and Logic)
    • The Role of AI in Robotics
      • Object Detection and Recognition
      • Path Planning and Navigation
      • Decision Making and Learning
  • Robotics Capabilities in PHP
  • Mathematics for Robotics
  • Building Robotics
  • Integration Robotics into Web
  • Key Applications of Robotics
  • Practice
  • Expert Systems
    • Introduction
    • Overview of ES
      • History of ES
        • Origins and Early ES
        • Milestones in the Evolution of ES
        • Expert Systems in Modern AI
      • Core Components and Architecture
      • Challenges and Limitations
      • Future Trends
    • ES Capabilities in PHP
    • Mathematics for ES
    • Building ES
      • Knowledge Representation Approaches
      • Inference Mechanisms
      • Best Practices for Knowledge Base Design and Inference
    • Integration ES into Web
    • Key Applications of ES
    • Practice
  • Cognitive Computing
    • Introduction
    • Overview of CC
      • History of CC
      • Differences Between CC and AI
    • CC Compatibilities in PHP
    • Mathematics for CC
    • Building CC
      • Practical Implementation
    • Integration CC into Web
    • Key Applications of CC
    • Practice
  • AI Ethics and Safety
    • Introduction
    • Overview of AI Ethics
      • Core Principles of AI Ethics
      • Responsible AI Development
      • Looking Ahead: Ethical AI Governance
    • Building Ethics & Safety AI
      • Fairness, Bias, and Transparency
        • Bias in AI Models
        • Model Transparency and Explainability
        • Auditing, Testing, and Continuous Monitoring
      • Privacy and Security in AI
        • Data Privacy and Consent
        • Safety Mechanisms in AI Integration
        • Preventing and Handling AI Misuse
      • Ensuring AI Accountability
        • Ethical AI in Decision Making
        • Regulations & Compliance
        • AI Risk Assessment
    • Key Applications of AI Ethics
    • Practice
  • Epilog
    • Summing-up
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On this page
  • The Future of Intelligent Machines
  • Autonomous Industrial Robots
  • Collaborative Robots (Cobots)
  • AI in Healthcare Robotics
  • AI in Agricultural Robotics
  • AI in Service Robots
  • AI in Autonomous Drones
  • AI in Space Exploration
  • The Future of Robotics with AI
  1. Artificial Intelligence
  2. Key Application of AI

AI in Robotics

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Last updated 1 month ago

The Future of Intelligent Machines

Artificial Intelligence and robotics are converging to create machines capable of performing complex tasks with a level of intelligence that mimics human reasoning. By integrating AI with physical robots, we are witnessing remarkable advancements in industries ranging from manufacturing to healthcare, agriculture, and even space exploration. These machines are evolving from simple automated systems to sophisticated entities capable of learning, adapting, and making decisions. Here’s a look at how AI is transforming robotics and making machines smarter, more efficient, and capable of extraordinary tasks.

Autonomous Industrial Robots

AI-driven robots are revolutionizing manufacturing by taking automation to the next level. These robots can perform tasks like assembling, packaging, and quality control autonomously, learning from their environment to optimize performance over time.

In the automotive industry, FANUC robots, equipped with AI, are widely used for tasks such as welding and assembly. These robots use machine learning to improve their precision over time, adapting to new production methods without needing manual reprogramming. AI also allows these robots to detect defects during production, improving product quality while reducing waste.

Collaborative Robots (Cobots)

Collaborative robots, or cobots, are designed to work alongside humans, assisting in tasks that require both human dexterity and robotic precision. AI plays a critical role in enabling cobots to understand their environment, work safely with humans, and even learn from human actions.

Universal Robots, a leader in cobot technology, uses AI to enhance the functionality of their machines. These cobots are capable of working in close proximity to human workers, handling tasks such as picking, placing, and packaging, while AI algorithms ensure that the robots adjust their actions based on the movements and needs of their human counterparts. This allows for safer and more efficient workflows in industries like electronics and pharmaceuticals, where precision and flexibility are essential.

AI in Healthcare Robotics

In healthcare, AI-powered robots are providing critical assistance in surgeries, rehabilitation, and even patient care. These robots can perform tasks with a level of precision and consistency that surpasses human capabilities, while AI enables them to adapt to specific patient needs.

Intuitive Surgical’s da Vinci System is one of the most well-known AI-powered surgical robots. It assists surgeons in performing minimally invasive surgeries with enhanced precision and control. AI helps the system predict movements, stabilize instruments, and provide real-time feedback, leading to faster recovery times and better outcomes for patients. Additionally, robots like TUG by Aethon use AI to navigate hospitals autonomously, delivering medication and supplies while avoiding obstacles and people, freeing up healthcare workers for more critical tasks.

AI in Agricultural Robotics

Agricultural robotics is benefiting greatly from AI, as these machines take over labor-intensive tasks like planting, harvesting, and monitoring crops. AI helps robots analyze soil quality, detect crop diseases, and manage resources more efficiently, leading to higher yields and more sustainable farming practices.

For instance, Blue River Technology’s See & Spray robot uses AI to detect and spray herbicides only where weeds are present, reducing the use of chemicals and minimizing waste. AI algorithms allow the robot to identify crops versus weeds in real-time, making farming more efficient and environmentally friendly. Similarly, AI-driven robots like Agrobot are used in harvesting crops such as strawberries, where they use vision systems to pick ripe fruit without damaging the plants, significantly speeding up the process compared to manual harvesting.

AI in Service Robots

Service robots, powered by AI, are becoming a common sight in areas like retail, hospitality, and customer service. These robots are capable of interacting with humans, answering questions, and performing tasks such as cleaning, delivering, and providing directions.

In hotels, for example, Pepper, an AI-powered humanoid robot created by SoftBank Robotics, is used to greet guests, assist with check-ins, and provide information. Its AI system allows it to understand speech, recognize emotions, and respond appropriately, creating a more engaging and personalized customer experience. Another example is Relay by Savioke, a service robot used in hotels to autonomously deliver items like towels or room service orders to guests, navigating through hallways and elevators with the help of AI algorithms.

AI in Autonomous Drones

AI is also transforming robotics in the form of autonomous drones, which are used for a wide variety of applications, from delivery services to surveillance, agriculture, and disaster response. These drones rely on AI to navigate complex environments, avoid obstacles, and make real-time decisions based on sensor data.

Zipline, for instance, uses AI-driven drones to deliver medical supplies to remote areas in countries like Rwanda and Ghana. These drones autonomously navigate long distances, even in challenging weather conditions, ensuring that essential medical supplies reach their destinations quickly. In agriculture, DJI’s Agras drones use AI to optimize spraying patterns for pesticides and fertilizers, maximizing efficiency and reducing chemical waste.

AI in Space Exploration

AI and robotics are also critical in space exploration, where robots are tasked with performing complex missions in environments that are inhospitable for humans. AI helps these robots navigate, make decisions, and conduct experiments without direct human intervention.

NASA’s Mars rovers, such as Perseverance, are equipped with AI systems that allow them to autonomously navigate the Martian surface, avoiding obstacles and selecting interesting geological sites for study. AI also enables these robots to perform tasks like drilling for soil samples, analyzing the Martian atmosphere, and sending valuable data back to Earth. The use of AI in space robotics allows missions to continue even when communication delays make real-time human control impossible.

The Future of Robotics with AI

The integration of AI into robotics is pushing the limits of what machines can do, allowing them to perform tasks that require intelligence, adaptability, and decision-making. From industrial automation and healthcare to agriculture and space exploration, AI-driven robots are shaping the future, making processes faster, safer, and more efficient. As AI continues to evolve, we can expect robots to play an even greater role in industries across the globe, transforming how we live and work.

AI in Robotics