Autonomous Robots: Path Planning

Compare breadth first search and depth first search in autonomous robot path planning, illustrating how each algorithm explores a search tree from the start to uncover doors and goals.

Learn to implement breadth-first and depth-first search on a grid world, including formal steps, depth tracking, and handling walls for autonomous path planning.

Explore assignment 1 in autonomous robots path planning on a 10 by 10 grid, start 0,0 and goal 5,9, navigate walls with breadth-first search to generate moves and visualize path.

Explore path planning with breadth-first search visualizations on grid maps, comparing start and goal positions, walls, and performance metrics, and introduce depth-first and star search foundations for autonomous robots.

This lecture introduces A* search in grid world path planning, explaining how it selects moves using the lowest heuristic value, combining distance traveled with the estimated distance to the goal.

Implement A* path planning with an enhanced H grid that visualizes heuristic values for every grid cell, including explored and unexplored nodes, and compare path costs.

Explore a star search solution for assignment two, using depth, heuristic values, the h grid and Harris tick grid, and compare it to breadth-first search on path planning tasks.

Compare breadth-first search and star search on a grid illustrating how diagonal moves, obstacles, and heuristic values affect exploration counts and the optimal path.

Scale up the A* path-planning implementation from a 10x10 grid to a larger NYC map, test with start and goal points, and distinguish black impassible regions from color passable areas.

Load a grid from a file for A* path planning, visualize the exploration depth, and encourage students to write the algorithm from scratch rather than copying solutions.

Shows solving assignment three with star search, comparing it to breadth-first search to reveal explored area, path choices, and a zigzag route near Central Park.

In this intro to assignment 4, learn how admissible and consistent heuristics guide A* path planning, using a grid of intersections with turn costs to find optimal routes.

Explore assignment 4 for autonomous robots path planning, starting with a non-optimal solution and moving toward the optimal, using an admissible non-consistent heuristic and explored and not explored lists.

Recap the path planning journey from breadth-first and depth-first searches on grid worlds to a star search with a heuristic function and weighted costs.