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.