How to Design Path Planning in Simulink: Step-by-Step Guide

To design path planning in Simulink, use blocks like Stateflow for decision logic and MATLAB Function blocks to implement algorithms such as A* or RRT. Connect these with sensor inputs and actuator outputs to simulate robot navigation.

Syntax

Path planning in Simulink involves combining blocks to create a model that calculates a route from start to goal. Key components include:

• Stateflow: For managing states and decision logic.
• MATLAB Function block: To write custom path planning algorithms.
• Sensor blocks: To input environment data.
• Actuator blocks: To output control commands.

The general syntax is to connect these blocks in a Simulink model to simulate the path planning process.

text

Stateflow + MATLAB Function + Sensor Input -> Path Planning Algorithm -> Actuator Output

Example

This example shows a simple path planning using a MATLAB Function block implementing a grid-based A* algorithm. The model inputs a grid map and start/goal points, then outputs the planned path coordinates.

matlab

function path = pathPlanning(gridMap, startPoint, goalPoint)
    % Simple A* path planning on a grid
    % gridMap: 2D matrix (0=free, 1=obstacle)
    % startPoint, goalPoint: [row, col]

    % Initialize open and closed sets
    openSet = [startPoint, 0, 0, heuristic(startPoint, goalPoint), 0]; % [row, col, g, h, f]
    closedSet = [];
    cameFrom = containers.Map();

    function h = heuristic(p1, p2)
        h = abs(p1(1)-p2(1)) + abs(p1(2)-p2(2));
    end

    while ~isempty(openSet)
        % Sort openSet by f = g + h
        [~, idx] = min(openSet(:,4) + openSet(:,5));
        current = openSet(idx, 1:2);
        if isequal(current, goalPoint)
            % Reconstruct path
            path = current;
            while isKey(cameFrom, mat2str(current))
                current = cameFrom(mat2str(current));
                path = [current; path];
            end
            return
        end
        openSet(idx,:) = [];
        closedSet = [closedSet; current];

        % Check neighbors
        neighbors = [current(1)+1, current(2); current(1)-1, current(2); current(1), current(2)+1; current(1), current(2)-1];
        for i=1:size(neighbors,1)
            n = neighbors(i,:);
            if n(1)<1 || n(2)<1 || n(1)>size(gridMap,1) || n(2)>size(gridMap,2)
                continue
            end
            if gridMap(n(1), n(2)) == 1 || any(ismember(closedSet, n, 'rows'))
                continue
            end
            tentative_g = openSet(idx,3) + 1;
            inOpen = ismember(openSet(:,1:2), n, 'rows');
            if ~any(inOpen) || tentative_g < openSet(inOpen,3)
                cameFrom(mat2str(n)) = current;
                g = tentative_g;
                h = heuristic(n, goalPoint);
                f = g + h;
                if ~any(inOpen)
                    openSet = [openSet; n, g, h, f];
                else
                    openSet(inOpen,3:5) = [g, h, f];
                end
            end
        end
    end
    path = [];
end

Output

When run in Simulink with proper inputs, outputs a matrix of path coordinates from start to goal avoiding obstacles.

Common Pitfalls

• Not properly initializing the grid or map data, causing the algorithm to fail.
• Ignoring obstacles or invalid start/goal points leading to no valid path.
• Incorrectly connecting blocks in Simulink, resulting in simulation errors.
• Using blocking code inside MATLAB Function blocks that slows simulation.

Always validate inputs and test the algorithm standalone before integrating into Simulink.

matlab

%% Wrong: No obstacle check
function path = badPathPlanning(gridMap, startPoint, goalPoint)
    % Ignores obstacles
    path = [startPoint; goalPoint];
end

%% Right: Check obstacles and plan path
function path = goodPathPlanning(gridMap, startPoint, goalPoint)
    % Use A* or other algorithm with obstacle checks
    path = pathPlanning(gridMap, startPoint, goalPoint);
end

Quick Reference

• Use Stateflow for managing path planning states.
• Implement algorithms in MATLAB Function blocks.
• Feed environment data via sensor blocks.
• Output control commands to actuators.
• Test algorithms standalone before Simulink integration.

Key Takeaways

Use MATLAB Function blocks to implement custom path planning algorithms like A* in Simulink.

Connect sensor inputs and actuator outputs properly to simulate real robot navigation.

Validate your map and start/goal points to avoid simulation errors.

Use Stateflow for managing decision logic and path planning states.

Test your algorithm standalone before integrating into the Simulink model.