Title: Robotics. Lesson 6; Robotic Path Planning and Navigation

A* algorithm finds optimal paths by minimizing distance and cost.
Dijkstra’s algorithm calculates shortest paths using graph-based search techniques.
RRT (Rapidly-exploring Random Trees) generates paths in complex environments.
Probabilistic roadmaps map feasible routes in high-dimensional spaces.
Path smoothing removes sharp turns, optimizing robot movement efficiency.
Dynamic path planning adjusts routes based on environmental changes.
Waypoint navigation guides robots by setting sequential destination points.
Occupancy grids represent environments, marking free and occupied spaces.
Grid-based path planning divides environments into cells for easier navigation.
Graph search algorithms explore nodes to identify optimal robot paths.
Hybrid A* combines heuristic search with continuous motion planning.
Visibility graphs connect visible points, avoiding obstacles in pathfinding.
Voronoi diagrams create paths by maximizing distance from obstacles.
Trajectory optimization improves path smoothness, conserving robot energy.
Dubins paths create curvature-constrained routes for wheeled robots.
SLAM integrates mapping with localization for autonomous navigation.
Potential field methods repel robots from obstacles, guiding safe movement.
Landmark-based navigation uses recognized points for positional accuracy.
Cost maps assign weights to paths, optimizing navigation choices.
Kinematic constraints ensure paths are feasible within robot movement limits.

A Path Planning Example*: Finding optimal route in a grid-based map for a mobile robot.
Dynamic Path Planning Example: Recalculating routes to avoid sudden obstacles during navigation.
Occupancy Grid Example: Mapping an environment with free and blocked cells for safe movement.

Title: Robotics. Lesson 6; Robotic Path Planning and Navigation