Section outline

  • Obstacle Avoidance and Path Planning

    🚧 Obstacle avoidance and path planning are essential for building truly autonomous robots that can navigate complex environments without collisions. In this section, we explore how robots sense their surroundings and choose the most efficient route.

    • 👀 Sensing Obstacles

      To detect and respond to obstacles, robots typically use a combination of distance and proximity sensors. Common options include:

      • Ultrasonic Sensors: Emit sound waves and calculate distance based on echo timing
      • Infrared Sensors: Detect obstacles through reflection of IR light; effective in short ranges
      • LIDAR: (in advanced setups) Provides high-resolution distance maps using laser scanning

      🧠 Decision-Making Strategies

      Once obstacles are detected, the robot must decide how to navigate. Several algorithms help make this process intelligent and efficient:

      • Reactive Navigation: Simple approach where the robot reacts immediately to sensor input (e.g., turn when obstacle is near)
      • Bug Algorithms: Move towards goal until obstacle is found, then follow the obstacle boundary
      • A* Algorithm: Grid-based algorithm that finds the shortest path from start to goal using heuristics
      • Dijkstra’s Algorithm: Like A* but explores all paths with equal priority, often slower

    • 🛠️ Implementing a Basic Avoidance System

      Here is a step-by-step breakdown of a simple ultrasonic-based obstacle avoidance logic:

      1. Robot moves forward until an object is detected within 15 cm
      2. Stop the robot immediately
      3. Scan left and right using a rotating sensor or by turning the wheels
      4. Compare distances on both sides
      5. Choose the direction with more free space
      6. Resume movement in that direction

      🧪 Activity: Simulated Path Planning

      Using a simulation platform like Tinkercad Circuits, or in Python with Pygame, create a virtual robot navigating through a maze. Implement simple grid-based A* pathfinding where the robot must avoid randomly placed obstacles to reach a defined target location.

      📊 Visualization Techniques

      Visualizing the path can greatly help in debugging and understanding the robot’s behavior. Some techniques include:

      • Plotting distance readings in real-time
      • Displaying planned vs. actual paths
      • Color-coding obstacles and waypoints

      🎯 Proper obstacle avoidance and intelligent path planning are what transform a robot from reactive to proactive. This forms the foundation for higher-level autonomy such as room mapping, object tracking, or even robot swarming behavior.