Path Planning

Path planning algorithms search for routes through a representation of the environment. Grid-based methods divide space into cells, marking obstacles as blocked and free space as available. A* algorithm evaluates cells based on distance traveled plus estimated remaining distance, expanding the most promising paths first. RRT (Rapidly-exploring Random Tree) grows a tree of random configurations, connecting them when collision-free. The planner uses the robot's shape and dimensions to check if paths are truly collision-free. Cost functions can optimize for shortest distance, smoothest path, or safest route away from obstacles. Dynamic path planning updates routes when obstacles move or new information arrives. The final path is often smoothed and converted to motion commands.

Path planning enables humanoid robots to navigate buildings, finding routes from current location to destination rooms or workstations. In warehouses, robots plan efficient paths between shelving and packing areas while avoiding other robots and workers. Outdoor environments require planning over uneven terrain and around dynamic obstacles. Multi-floor navigation plans paths including stairs or elevators. Emergency response scenarios need quick replanning when original paths become blocked. Energy-efficient planning selects routes minimizing battery consumption.