Digital Communication


Digital Communication

Pathfinding: path finding in computing


Pathfinding algorithms are among the best known and most used algorithms. We show how pathfinding works and what it is used for.

What is pathfinding?

Pathfinding, also known as pathfinding, is a fundamental problem in computer science. Pathfinding consists of finding the shortest or most efficient path between two points. There are a multitude of pathfinding algorithms used in different application scenarios.

How does pathfinding work and what is it for?

A pathfinding algorithm generally begins by representing the problem in graph or grid form. A graph is a collection of nodes connected by edges; for example, imagine a flowchart. A grid is a two-dimensional field of cells, like a chessboard. Nodes or cells represent locations in the problem space, while neighboring edges or cells represent possible paths between them.

Once the problem is represented in the form of a graph or grid, pathfinding algorithms use different techniques to find the path between two points. In general, algorithms aim to find the shortest or cheapest path while being as efficient as possible.

Find shortest path in grid and graph
Pathfinding to find the shortest path in the grid and graph – increasing distances are shaded in color.

Pathfinding algorithms have many computer applicationsparticularly in:

  • The robotics : Pathfinding algorithms are used to help autonomous robots navigate complex environments. We think, for example, of cars that drive themselves or intelligent vacuum cleaners that move around the house on their own.
  • Video games : In video games, pathfinding algorithms are used to control the movements of non-player characters (NPCs). In a real-time strategy game, if you send units to the enemy base by clicking on them, you also use pathfinding algorithms.
  • Logistic : pathfinding algorithms are used in logistics to find the most efficient path for the transport of goods or people.
  • Traffic planning : Pathfinding algorithms are used to plan the best routes for a city’s traffic, while avoiding traffic jams.
  • Network Routing : In computer networks, pathfinding algorithms are used to find the fastest path for transmitting data between different nodes in the network.

Let’s see in detail some possible applications of pathfinding.

Pathfinding in logistics

Pathfinding in logistics consists of finding the best route for transportation of goods. An optimal route minimizes costs and travel time while ensuring the safety of the products transported. Pathfinding is therefore a decisive tool in logistics to optimize the movement of goods and reduce costs.

Let’s take a few examples to illustrate how pathfinding is used in logistics:

  • Vehicle routing : in freight transport, pathfinding algorithms optimize the route of delivery vehicles. The algorithm takes into account factors such as distance, traffic conditions and delivery time constraints to create the most efficient route.
  • Inventory management : pathfinding is used in inventory management to optimize the placement of goods. This helps ensure that goods are stored in optimal positions. This helps reduce the time and effort spent on collecting and delivering goods.
  • Supply Chain Management : pathfinding algorithms are used to optimize the entire supply chain, from its origin to its delivery. This ensures that products are transported as efficiently and economically as possible.

Pathfinding in video games

Pathfinding in video games is an important tool for creating impressive and realistic game worlds. The technology allows units and non-player characters (NPCs for Non-player characters) to move realistically and effectively in the game world. Pathfinding algorithms are used to determine the optimal path for NPC movement, avoiding obstacles and other dangers.

In video games, pathfinding is used, among other things, for the following tasks:

  • Enemy NPCs : Pathfinding is used to control the behavior of enemy NPCs. NPCs can thus follow the player while avoiding obstacles and other dangers.
  • Unit control : pathfinding allows you to control the movement of friendly units in the game world. This can involve guiding NPCs to their destination or following the player character.
  • Obstacle avoidance : Pathfinding algorithms ensure that units avoid obstacles such as walls, cliffs or other hazards.
  • Map/level generation : Pathfinding algorithms are also used for the procedural generation of maps or levels. This makes it possible to create realistic and varied game worlds.

Pathfinding for network routing

Pathfinding is used in network routing to find optimal paths for data packets across a network. Pathfinding algorithms provide network administrators with the ability to improve network performance based on circumstances. Common applications of pathfinding in network routing include:

  • Traffic engineering : pathfinding algorithms optimize network traffic and minimize congestion. By analyzing network topology and traffic patterns, pathfinding algorithms help identify the most efficient paths for data packets through the network.
  • Quality of Service (QoS) : Path finding algorithms allow network traffic to be prioritized based on quality of service requirements. For example, data with time requirements such as VoIP or video streaming are routed with priority through the network. Prioritization is used as part of the cost function.
  • Load Balancing : Specially adapted pathfinding algorithms are used to distribute network traffic across multiple paths. Through load balancing, pathfinding algorithms help improve network performance and reduce the risk of congestion.
  • Failure resistance : Pathfinding algorithms are used to find alternative paths for data flow in the event of a network outage. This helps ensure reliable delivery of data packets in the event of a network component failure.

Pathfinding in transport planning

Pathfinding is used in the transport sector to optimize traffic flow and reduce traffic jams. Pathfinding algorithms help transportation engineers design efficient transportation networks and develop strategies to improve traffic flow. Here are some of the main applications of pathfinding in the transport sector.

  • Route planning : Pathfinding algorithms are used to plan optimal routes for vehicles while avoiding congested areas. This helps improve traffic flow and reduce delays.
  • Traffic light optimization : Pathfinding algorithms can be used to optimize traffic light switching based on traffic patterns and demand. Synchronizing traffic lights and adjusting schedules can improve traffic flow.
  • Event management : Pathfinding algorithms are used to identify alternative routes for vehicles in the event of an accident or road closure. Pathfinding thus makes it possible to reduce traffic jams and improve traffic flow in the areas concerned.
  • Public transport : pathfinding algorithms make it possible to optimize public transport routes and timetables. This helps to improve the efficiency of public transport systems and reduce traffic congestion.

What are the existing pathfinding algorithms?

Pathfinding challenges arise from the constraints of the specific problem space. It is therefore necessary to take into account the obstacles that block the direct path, as well as possible costs of traveling in space. Costs can be multidimensional, for example when a path is cheaper from an energy point of view, but requires a longer travel time.

Where applicable, defined points must be included in the path; a pathfinding algorithm then guarantees that movement in space does not take place in circles. In general, an optimal path must be found as efficiently as possibleespecially if pathfinding needs to take place in real time.

A few common pathfinding algorithms are :

  • Breadth-First Search (BFS): This algorithm explores all neighboring nodes from the starting point before moving to the next level of nodes until the target is reached.
  • Dijkstra’s algorithm : the algorithm explores the graph by first visiting an unexplored node closest to the starting point, then iteratively updating the distance of all nodes from the starting point until the goal is reached.
  • Research A* : This algorithm combines ideas from BFS and Dijkstra’s algorithm using a heuristic function to guide the search to the target node.
  • Greedy Best-First Search Algorithm : with this algorithm, the next node to explore is selected based on a heuristic estimate of the distance to the target node.
  • Two-way search : from the start node and the end node, this algorithm simultaneously searches towards the center of the graph to find the shortest path.

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