C++ Robot Programming: Motion Control and Path Planning

In modern robotics technology, motion control and path planning are two crucial areas. This article will introduce basic users to how to perform simple motion control and path planning using C++, along with corresponding code examples.

1. Motion Control

1.1 What is Motion Control?

Motion control refers to managing the movement of a robot through algorithms, including speed, acceleration, direction, etc. We can achieve the robot’s movement by setting target positions and current position information.

1.2 Basic Concepts

Position: The current position of the robot.
Target Position: The position the robot needs to reach.
Speed: The rate at which the robot moves.
Acceleration: The rate at which the robot changes speed.

1.3 Example Code

The following is a simple C++ program that simulates the linear movement of a robot:

#include <iostream>#include <cmath>
class Robot {
public:
    Robot(double x, double y) : posX(x), posY(y) {}
    void moveTo(double targetX, double targetY, double speed) {
        double distance = calculateDistance(targetX, targetY);
        if (distance == 0) {
            std::cout << "Already at the target position." << std::endl;
            return;
        }
        // Move to target position
        while (calculateDistance(posX, posY) > speed) {
            posX += (targetX - posX) / distance * speed;
            posY += (targetY - posY) / distance * speed;
            std::cout << "Current Position: (" << posX << ", " << posY << ")" << std::endl;
        }
        // Reached target position
        posX = targetX;
        posY = targetY;
        std::cout << "Reached Target Position: (" << posX << ", " << posY << ")" << std::endl;
    }
private:
    double calculateDistance(double x, double y) {
        return sqrt(pow(x - posX, 2) + pow(y - posY, 2));
    }
    double posX; // Current x coordinate
    double posY; // Current y coordinate
};
int main() {
    Robot myRobot(0.0, 0.0); // Initialize robot at origin (0,0)
    myRobot.moveTo(10.0, 10.0, 1.0); // Move to (10,10) at unit distance of 1
}

1.4 Program Explanation

In the above code, we define a Robot class that includes the current position (posX and posY) and a moveTo method that moves the robot to a specified position. We use the Euclidean distance formula to calculate the distance between the current position and the target position, and update the robot’s coordinates step by step based on the set speed.

2. Path Planning

2.1 What is Path Planning?

Path planning is the method of finding a valid route between a given starting point and an endpoint. This often involves avoiding obstacles and selecting the shortest or optimal route.

2.2 Basic Concepts

Starting Point: The departure location of the robot.
Ending Point: The destination of the robot.
Obstacles: Irregular areas that affect path selection.

2.3 Example Code (A* Algorithm)

Below is a simplified implementation of the A* algorithm to find a feasible path from the starting point to the endpoint:

#include <iostream>#include <vector>#include <queue>#include <unordered_map>
struct Node {
    int x,y,f,g,h;
};
struct CompareNode {
    bool operator()(Node const& n1, Node const& n2){
        return n1.f > n2.f;
    }
};
class PathPlanner {
public:
    PathPlanner(int width,int height): gridWidth(width), gridHeight(height){}
    void addObstacle(int x,int y){
        obstacles.insert(std::make_pair(x,y));
    }
    void findPath(Node startNode , Node endNode){
        std::priority_queue<Node,std::vector<Node>,CompareNode> openSet;
        openSet.push(startNode);
        while(!openSet.empty()){
            Node current = openSet.top();
            openSet.pop();
            if(current.x == endNode.x && current.y == endNode.y){
                std::cout<<"Path found to ("<<current.x<<","<<current.y<<")"<<std::endl;
                return ;
            }
            for(auto &direction : directions){
                int new_x = current.x + direction[0];
                int new_y = current.y + direction[1];
                if(isValid(new_x,new_y)){
                    Node neighbor{new_x,new_y};
                    neighbor.g = current.g + cost(current,new_x,new_y);
                    neighbor.h = heuristic(neighbor,endNode);
                    neighbor.f = neighbor.g + neighbor.h;
                    openSet.push(neighbor);
                }
            }
        }
        std::cout<<"No path found!"<<std::endl;
    }
private:
    int gridWidth ,gridHeight ;
    const int directions[4][2]{{-1 ,0},{+1 ,0},{0 ,-1},{+1 ,0}};
    bool isValid(int x,int y){
        return !(x<0 || y<0 || x>=gridWidth || y>=gridHeight || obstacles.count({x,y}));
    }
    int cost(Node &node,int new_x,int new_y){return node.g+((node.x==new_x && node.y==new_y)?14:10);}
    int heuristic(Node &a , Node &b){return abs(a.x-b.x)+abs(a.y-b.y);}
private:
    std::unordered_set<std::pair<int,int>> obstacles ;
};
int main(){
    PathPlanner planner(20 ,20 );
    planner.addObstacle(5 ,5 );
    planner.addObstacle(6 ,5 );
    Node start{start_node_position};
    Node goal{goal_node_position};
    planner.findPath(start ,goal );
}

A* Algorithm Explanation

In this example, we create a PathPlanner class that can handle obstacles in a grid and find a valid path from the starting node to the ending node. We use a priority queue to store nodes to be explored and evaluate each node based on a heuristic function (such as Manhattan distance) to select the best next action direction.

Conclusion

This article introduced basic motion control and simple path planning methods in C++, helping basic users understand these concepts through examples. In practical applications, you can extend these basic functionalities to accommodate more complex scenarios, such as obstacle avoidance in dynamic environments and fusion of various types of sensor data. We hope this article inspires you to delve deeper into learning more about robot programming!