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Chapter 527 sections
Deterministic algorithms like A* and D* form the foundation of path planning.
Sampling-based methods like RRT and PRM are scalable to high-dimensional spaces.
Trajectory optimization enhances path smoothness and dynamic feasibility.
Dynamic obstacle avoidance integrates perception with reactive control strategies.
Real-time planning in unknown terrain demands adaptability and robustness.
A best-first search algorithm that uses the cost-to-come and cost-to-go to find optimal paths in graph-based environments.
An algorithm designed for pathfinding in high-dimensional spaces by incrementally building a tree rooted at the start configuration.
A method that samples velocities to choose paths that avoid obstacles and progress toward the goal.
The process of generating trajectories that minimize costs related to smoothness and collisions, respecting physical constraints.
A technique that directs robots towards the boundaries between known and unknown areas to enhance mapping and exploration.