Path planning is the process of determining a safe and efficient route for a robot to move from a starting point to a goal while avoiding obstacles. It involves algorithms that calculate feasible paths based on the robot’s environment, physical constraints (like size or turning radius), and objectives (such as minimizing time or energy). In robotics, this is critical for tasks like navigation, manipulation, or autonomous driving, where the robot must adapt to static or dynamic obstacles. Path planning is typically split into global planning (predefined routes using known maps) and local planning (real-time adjustments for unexpected obstacles).
Implementation begins with modeling the environment, often using grid maps, occupancy grids, or graph-based representations. Algorithms like A* or Dijkstra’s are common for global planning. For example, A* uses a heuristic to efficiently search for the shortest path on a grid, prioritizing nodes closer to the goal. For dynamic environments, local planners like the Dynamic Window Approach (DWA) evaluate possible velocities and trajectories within a short time horizon, selecting paths that avoid immediate collisions. Sampling-based methods like Rapidly-exploring Random Trees (RRT) are useful in high-dimensional spaces (e.g., robotic arms), generating random waypoints and connecting them to form a path. These algorithms often incorporate motion constraints, such as a car’s inability to turn sharply, ensuring the path is executable.
Practical implementation also relies on sensors (LiDAR, cameras) and real-time data processing. For instance, an autonomous warehouse robot might use a preloaded map for global planning but adjust its route locally using LiDAR to detect moving workers. ROS (Robot Operating System) provides tools like the Navigation Stack, which integrates global planners (e.g., A*) with local planners (e.g., DWA) and sensor data. Challenges include balancing computational speed with path optimality and handling uncertainty in sensor data. Developers often tune parameters like obstacle inflation (safety margins) or update frequencies to match the robot’s hardware limits. Effective path planning combines robust algorithms, accurate environment modeling, and iterative testing to ensure reliability in real-world scenarios.