AI in Robotics

## **1. Robot Motion Planning** - **Pathfinding**: - **Description**: Pathfinding algorithms enable robots to find the optimal path from a starting point to a destination while avoiding obstacles. - **Key Algorithms**: - **A* Algorithm**: Uses heuristics to find the shortest path efficiently. - **Dijkstra's Algorithm**: Guarantees the shortest path but can be slower for larger graphs. - **RRT (Rapidly-exploring Random Trees)**: Useful for robots navigating complex environments. - **Use Cases**: Autonomous navigation, warehouse robots, and search-and-rescue operations. - **SLAM (Simultaneous Localization and Mapping)**: - **Description**: SLAM enables robots to build a map of an unknown environment while simultaneously determining their location within that environment. - **Techniques**: - **EKF (Extended Kalman Filter)**: Combines sensor data to estimate the position of the robot and update the map. - **Particle Filter**: Approximates the probability distribution of the robot’s location. - **Graph-Based SLAM**: Uses graph theory to solve the localization problem efficiently. - **Use Cases**: Indoor robot navigation, autonomous vehicles, and drones. - **PID Control (Proportional-Integral-Derivative)**: - **Description**: A control system mechanism that continuously calculates an error value and applies corrections to minimize that error. - **Components**: - **Proportional**: Reacts to current error. - **Integral**: Accounts for past errors. - **Derivative**: Predicts future errors to apply corrective actions. - **Use Cases**: Robotic arms, motor control, and balancing robots like self-balancing scooters. #### **2. Perception and Sensing** - **LIDAR (Light Detection and Ranging)**: - **Description**: LIDAR sensors use laser pulses to measure distances and create detailed 3D maps of the surrounding environment. - **How It Works**: A laser emits pulses, and a sensor measures the time it takes for the pulse to return after reflecting off objects. - **Use Cases**: Self-driving cars, drones, and industrial robots. - **Computer Vision in Robotics**: - **Description**: Computer vision enables robots to interpret and understand visual information from the world, allowing them to interact with objects. - **Techniques**: - **Object Recognition**: Identifying and classifying objects in the environment. - **Depth Estimation**: Calculating the distance to objects using stereo cameras or depth sensors. - **Visual SLAM**: Combining SLAM techniques with camera input for more accurate localization. - **Use Cases**: Object manipulation, autonomous navigation, and facial recognition in service robots. - **Sensor Fusion**: - **Description**: Sensor fusion combines data from multiple sensors (e.g., cameras, LIDAR, GPS, IMU) to create a more accurate representation of the environment. - **Techniques**: - **Kalman Filters**: Combines noisy sensor data for more accurate estimates. - **Bayesian Networks**: Probabilistically fuses data from different sensors. - **Use Cases**: Self-driving cars, drones, and autonomous robotic systems that require precise navigation. #### **3. Autonomous Systems** - **Drones**: - **Description**: Drones, or unmanned aerial vehicles (UAVs), use AI to fly autonomously, avoiding obstacles and navigating to specified locations. - **AI Applications**: - **Path Planning**: Calculating optimal flight paths while avoiding obstacles. - **Object Detection**: Using computer vision to identify objects on the ground. - **Autonomous Landing**: Detecting safe landing zones in unknown environments. - **Use Cases**: Aerial surveillance, delivery systems, and agricultural monitoring. - **Self-Driving Cars**: - **Description**: Autonomous vehicles use AI for perception, decision-making, and control to navigate roads without human input. - **AI Components**: - **Perception**: LIDAR, radar, and cameras detect the environment. - **Decision-Making**: Algorithms decide the car’s next actions (e.g., lane changes, braking). - **Control**: PID control and other mechanisms govern the car’s acceleration, braking, and steering. - **Use Cases**: Urban transportation, ride-sharing services, and long-haul trucking. - **Robotic Arms**: - **Description**: Robotic arms are used in industrial automation, using AI to perform tasks such as welding, assembling, and painting. - **AI Capabilities**: - **Motion Planning**: Calculating precise movements for tasks. - **Object Manipulation**: Using vision and sensors to grasp and manipulate objects. - **Collaborative Robots (Cobots)**: Robots that work alongside humans, using AI to ensure safety and collaboration. - **Use Cases**: Manufacturing, medical surgery, and hazardous environment operations.