Roblox provides a robust PathfindingService, which is designed to simplify the process of navigating complex environments by automatically calculating the optimal path between two points, considering obstacles and dynamic changes in the environment. This service is highly efficient and leverages a system similar to the A* algorithm to determine paths. By using PathfindingService, developers can quickly implement pathfinding capabilities without needing to manually code the underlying logic, making it an excellent choice for straightforward navigation tasks in games and simulations.
In contrast, developing a custom pathfinding system, as demonstrated in our script, offers deeper insights and flexibility that can be particularly valuable for advanced users or those with specific needs. By implementing the A* algorithm manually, developers gain a thorough understanding of heuristic-based search methods and how to optimize pathfinding for their unique requirements. Our custom system, which dynamically updates the path every second, not only showcases the mechanics of A* but also demonstrates how to create a more interactive and engaging experience. This approach allows for fine-tuning and customization that might not be as easily achievable with the built-in service.
While Roblox’s PathfindingService is perfect for quickly setting up navigation with minimal effort, our custom A* algorithm provides a foundation for deeper learning and greater customization. This custom approach can be adapted to various applications beyond gaming, such as robotics and logistics, where precise control over the pathfinding logic is necessary. By comparing both methods, developers can choose the appropriate tool for their specific project needs, whether they prioritize ease of implementation or the flexibility and control offered by a custom solution.
-- Define the positions of nodes
local nodes = {
A = Vector3.new(0, 0, 0),
B = Vector3.new(-5, 0, -5),
C = Vector3.new(5, 0, 0),
D = Vector3.new(-5, 0, -10),
E = Vector3.new(10, 0, -10),
}
-- Define the edges between nodes with weights
local edges = {
{ from = "A", to = "B", weight = 1 },
{ from = "A", to = "C", weight = 5 },
{ from = "B", to = "D", weight = 7 },
{ from = "C", to = "E", weight = 8 },
{ from = "D", to = "E", weight = 10 },
}
-- Convert the edge list into an adjacency list for easier neighbor lookup
local graph = {}
for _, edge in ipairs(edges) do
graph[edge.from] = graph[edge.from] or {}
graph[edge.from][edge.to] = edge.weight
graph[edge.to] = graph[edge.to] or {}
graph[edge.to][edge.from] = edge.weight
end
-- Heuristic function for A* (using Euclidean distance)
local function heuristic(a, b)
return (nodes[a] - nodes[b]).Magnitude
end
-- Implementation of the A* algorithm
local function aStar(startNode, goalNode)
local openSet = { startNode }
local cameFrom = {}
local gScore = {}
local fScore = {}
-- Initialize scores for all nodes
for node, _ in pairs(nodes) do
gScore[node] = math.huge
fScore[node] = math.huge
end
gScore[startNode] = 0
fScore[startNode] = heuristic(startNode, goalNode)
-- Main A* algorithm loop
while #openSet > 0 do
table.sort(openSet, function(a, b) return fScore[a] < fScore[b] end)
local current = table.remove(openSet, 1)
-- If the goal is reached, reconstruct the path
if current == goalNode then
local path = {}
while cameFrom[current] do
table.insert(path, 1, current)
current = cameFrom[current]
end
table.insert(path, 1, startNode)
return path
end
-- Evaluate neighbors of the current node
for neighbor, weight in pairs(graph[current]) do
local tentative_gScore = gScore[current] + weight
if tentative_gScore < gScore[neighbor] then
cameFrom[neighbor] = current
gScore[neighbor] = tentative_gScore
fScore[neighbor] = gScore[neighbor] + heuristic(neighbor, goalNode)
if not table.find(openSet, neighbor) then
table.insert(openSet, neighbor)
end
end
end
end
return nil
end
-- Function to create a node in the workspace
local function createNode(name, position)
local part = Instance.new("Part")
part.Name = name
part.Size = Vector3.new(1, 1, 1)
part.Position = position
part.Anchored = true
part.Shape = Enum.PartType.Ball
part.BrickColor = BrickColor.new("Bright yellow")
part.CanCollide = false
part.Parent = workspace
local billboard = Instance.new("BillboardGui", part)
billboard.Size = UDim2.new(0, 100, 0, 50)
billboard.StudsOffset = Vector3.new(0, 2, 0)
billboard.AlwaysOnTop = true
local textLabel = Instance.new("TextLabel", billboard)
textLabel.Text = name
textLabel.Size = UDim2.new(1, 0, 1, 0)
textLabel.TextScaled = true
textLabel.BackgroundTransparency = 1
textLabel.TextColor3 = Color3.new(0, 0, 0)
end
-- Function to create an edge between two nodes in the workspace
local function createEdge(fromPos, toPos, weight)
local part = Instance.new("Part")
part.Size = Vector3.new(0.2, 0.2, (fromPos - toPos).Magnitude)
part.CFrame = CFrame.lookAt((fromPos + toPos) / 2, toPos)
part.Anchored = true
part.BrickColor = BrickColor.new("Really black")
part.CanCollide = false
part.Parent = workspace
local billboard = Instance.new("BillboardGui", part)
billboard.Size = UDim2.new(0, 50, 0, 25)
billboard.StudsOffset = Vector3.new(0, 2, 0)
billboard.AlwaysOnTop = true
local textLabel = Instance.new("TextLabel", billboard)
textLabel.Text = tostring(weight)
textLabel.Size = UDim2.new(1, 0, 1, 0)
textLabel.TextScaled = true
textLabel.BackgroundTransparency = 1
textLabel.TextColor3 = Color3.new(1, 1, 1)
end
-- Function to clear previous path visualization
local function clearPreviousPath()
for _, part in ipairs(workspace:GetChildren()) do
if part.Name == "PathPart" then
part:Destroy()
end
end
end
-- Function to visualize the path found by the A* algorithm
local function visualizePath(path)
clearPreviousPath()
local totalWeight = 0
for i = 1, #path - 1 do
local fromPos = nodes[path[i]]
local toPos = nodes[path[i + 1]]
local part = Instance.new("Part")
part.Name = "PathPart"
part.Size = Vector3.new(0.2, 0.2, (fromPos - toPos).Magnitude)
part.CFrame = CFrame.lookAt((fromPos + toPos) / 2, toPos)
part.Anchored = true
part.BrickColor = BrickColor.new("Bright green")
part.CanCollide = false
part.Parent = workspace
totalWeight = totalWeight + graph[path[i]][path[i + 1]]
end
print("Path from " .. path[1] .. " to " .. path[#path] .. " with total weight: " .. totalWeight)
end
-- Create nodes and edges in the workspace
for name, position in pairs(nodes) do
createNode(name, position)
end
for _, edge in pairs(edges) do
createEdge(nodes[edge.from], nodes[edge.to], edge.weight)
end
-- Function to choose a random node from the nodes list
local function chooseRandomNode()
local keys = {}
for key in pairs(nodes) do
table.insert(keys, key)
end
return keys[math.random(1, #keys)]
end
-- Function to update the path every second
local function updatePath()
while true do
local startNode = chooseRandomNode()
local goalNode = chooseRandomNode()
while startNode == goalNode do
goalNode = chooseRandomNode()
end
local path = aStar(startNode, goalNode)
if path then
visualizePath(path)
else
print("No path found")
end
wait(1) -- Update path every second
end
end
-- Start updating paths
spawn(updatePath)
You may notice multiple parts being selected at once in the included image. This is due to the path chosen extending over multiple pieces. If you watch the output and compare it to the highlighted part, you will notice the chosen path with weight.