How Breadth-First Search can help in Robot Pathfinding
The Breadth-first-search is a method of uninformed search, so our example’s robot does not use any information about how far our robot has traveled from its initial state nor how far the robot is from the goal. Using BFS we begin by positioning the robot at the root node, now it starts looking for the goal by expanding all of neighbor nodes (successors) of the root node.. Here, successors of a node are said to hold all the allowable directions that the robot could travel next. Nodes that are already visited by the robots are not considered as successors. Take a look at the below picture, the robot can travel 8 possible directions.
The initial step in the Breath-first-search algorithm is to begin with the starting node. In this case, the ‘R’ serves as a starting node, so we visit node R first. Additionally, we add the visited node to the FIFO queue. By adding node ‘R’ to the queue, we ensure it is explored in the subsequent steps of the algorithm.
Initial node is visited first
We dequeue node R from the FIFO queue to expand it. Upon expansion, we discover a successor node labeled S, representing the direction south. Since node S has been visited, it is now enqueued in the FIFO queue for further exploration.
Node R is expanded
Similarly, we dequeue the S from the FIFO queue to expand it. We gain two more successor nodes that are labeled as S and E representing the direction of South and South east. These directions are the paths that our robot tends to traverse next. The visited nodes such as S and E are enqueued in the FIFO queue to avoid revisiting them.
Again S is dequeued from the queue and expanded by gaining the other two successors such as S and E. It is also enqueued to the FIFO queue.
Here, we dequeue the node E which leads to the expansion of two more nodes called S and N representing East and North East, respectively. As in the previous steps, we enqueue the newly visited nodes for further exploration.
The FIFO queue continues until the goal is found. Once the goal is discovered, the path that leads to a goal node is traced back up the tree so that it maps out the directions for the robot to reach the goal. In our example, let’s say that the goal node E was found when node SE was expanded. By traversing back up to the tree we can easily trace the path that the robot needs to follow. In this case, the robot needs to begin from the south then move to the southeast, and then finally head to the east to reach the goal.
Breadth First Search (BFS) for Artificial Intelligence
In artificial intelligence, the Breadth-First Search (BFS) algorithm is an essential tool for exploring and navigating various problem spaces. By systematically traversing graph or tree structures, BFS solves tasks such as pathfinding, network routing, and puzzle solving. This article probes into the core concepts of BFS, its algorithms, and practical applications in AI.
Table of Content
- What is Breadth-First Search?
- Key characteristics of BFS
- Breadth First Search (BFS) Algorithms
- How Breadth-First Search can help in Robot Pathfinding
- Practical Implementations of BFS in Pathfinding of Robots
- Conclusion
- FAQs on Breadth First Search (BFS) for Artificial Intelligence
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