Table of Contents
BFS & DFS Implementation (2023) (IN-PROGRSS)
Author: Francis Palustre Email: palusf1@unlv.nevada.edu
Date: Last modified on <11/13/23>
Keywords: Navigation, BFS, DFS, Robotino, cpp,
The photo above depicts the environment used for travel which allows you to visually see the intended path that should be taken for Robotino . The big picture problem is understanding the traversal process and how it came to be. Solving this partially or completely is important because this gives robots an autonomous navigation process, limiting human interference. This tutorial shows you how to apply BFS and DFS to Robotino and should take approximately 1-2 hour to complete.
Motivation and Audience
This tutorial's motivation is to find ways for robots to autonomously navigate within their environments. Readers of this tutorial assumes the reader has the following background and interests:
* Know how to code in CPlusPlus and are familiar with function handling
* Know how Breadth-First and Depth-First Search algorithms work
* Know how to navigate within the terminal in Linux
* This tutorial may also attract readers who are interested in implementing algorithms in their own projects
The rest of this tutorial is presented as follows:
Setup and Understanding
To setup Robotino, please take a look at the following wikis:
How to Setup Robotino for Programming and Navigation
Robotino API2 Setup
Hello World on the Robotino
To understand how Breadth-First Search and Depth-First Search work, please take a look at the following wikis and links:
Breadth-First Search and Depth-First Search
Breadth-First Search (GeeksForGeeks)
Breadth-First Search (Programiz)
Depth-First Search (GeeksForGeeks)
Depth-First Search (Programiz)
General Functions
General functions are functions that are found in both files of BFS and DFS
- getCurrentTime() gets the current time in UTC in seconds. It is used to provide the time needed for a movement for Robotino.
double getCurrentTime() {
struct timeval tv; //timeval struct to store time values
gettimeofday(&tv, NULL); //get current time and store in timeval
//convert time to seconds and add microseconds for precision
return (tv.tv_sec) + tv.tv_usec / 1000000.0;
}
- roboMove() prints the final path from the algorithm and will result into the movement of Robotino
void roboMove(map<string, vector<string> > newGraph, string startNode, string desiredNode) {
vector<string> roboPath = BFS(newGraph, startNode, desiredNode); //or DFS
//couts the final path
for (vector<string>::iterator it = roboPath.begin(); it != roboPath.end(); it++) {
cout << *it << " ";
} cout << endl;
//movement of Robotino
for (int i = 0; i < int(roboPath.size()) - 1; i++) {
//movement is based on current and next value in path
int currPos = atoi(roboPath.at(i).c_str());
int nextPos = atoi(roboPath.at(i + 1).c_str());
if ((currPos + 6) == nextPos) {
moveY(0,1);
}
if ((currPos - 6) == nextPos) {
moveY(1,0);
}
if ((currPos - 1) == nextPos) {
moveX(1,0);
}
if ((currPos + 1) == nextPos) {
moveX(0,1);
}
}
omni.setVelocity(0,0,0);
}
- removeEdges() removes the edges of selected nodes. Can be used to represent obstacles/walls. Works because you can have a disconnected graph (as in, floating nodes), but you cannot have unconnected edges
map<string, vector<string> > removeEdges(map<string, vector<string> > graph, vector<string> removeVec) {
for (vector<string>::iterator it = removeVec.begin(); it != removeVec.end(); it++) { //for every edges of the current node...
vector<string> adjNodes = graph[*it];
for (vector<string>::iterator edges = adjNodes.begin(); edges != adjNodes.end(); edges++) { //for every node of those edges...
for (vector<string>::iterator it = roboPath.begin(); it != roboPath.end(); it++) {
//disconnects the connected nodes from current edge
vector<string>::iterator removeNode = remove(graph[*edges].begin(), graph[*edges].end(), *it);
graph[*edges].erase(removeNode, graph[*edges].end()); //getting rid of edges
}
graph[*it].clear(); //removing current node
}
}
Robotino Functions
Robotino functions are functions that used for Robotino's movement
- moveX() is the movement in the horizontal direction
void moveX(int first, int second) {
int checker = first - second; //determines if movement is positive or negative
int distance = abs(checker); //how much travel is needed
double start_time = getCurrentTime();
double end_time = (distance * 1.772)+ start_time; //offset of 1.772 is used (can change to whatever is yours)
double elapsed_time = 0;
for (int i =0; i < distance;i++) {
if (checker > 0) { //if positive...
elapsed_time = 0; //resetting time to avoid continous movement
do {
elapsed_time = getCurrentTime(); //updating elapsed time
omni.setVelocity(0.0015,-0.2,0); //velocity of Robotino (adjust accordingly)
} while (elapsed_time < end_time);
} else { //if negative...
elapsed_time = 0;
do {
elapsed_time = getCurrentTime();
omni.setVelocity(-0.00151,0.2,0);
} while (elapsed_time < end_time);
}
}
omni.setVelocity(0,0,0); //setting velocity to 0 for precautionary sake
}
- moveY() is the movement in the vertical direction
void moveY(int first, int second) { //same comments applied to this function
int checker = first - second;
int distance = abs(checker);
double start_time = getCurrentTime();
double end_time = (distance * 1.772)+ start_time;
double elapsed_time = 0;
for (int i =0; i < distance;i++) {
if (checker > 0) { //if positive...
elapsed_time = 0;
do {
elapsed_time = getCurrentTime();
omni.setVelocity(0.2,-0.00131,0);
} while (elapsed_time < end_time);
} else {
elapsed_time = 0;
do {
elapsed_time = getCurrentTime();
omni.setVelocity(0.2,-0.00131,0);
} while (elapsed_time < end_time);
}
}
omni.setVelocity(0,0,0);
}
Breadth First Search
Algorithm for BFS
vector<string> BFS(map<string, vector<string> > graph, string startingNode, string desiredNode) {
//Creating containers of visited and queued nodes
vector<string> visited;
queue<vector<string> > queue;
queue.push(vector<string>(1, startingNode)); //starting the queue with our starting node
while (!queue.empty()) {
vector<string> path = queue.front(); //store node item in path
queue.pop(); //removes node from queue
string currNode = path.back(); //tailed node becomes current node
//iterates through a set to find the current value in the path
vector<string>::iterator findCurr = find(visited.begin(), visited.end(), currNode);
if (findCurr == visited.end()) {
visited.push_back(currNode); //marks as visited
vector<string> adjNodes = graph[currNode]; //neighbor nodes of current
for (vector<string>::iterator it = adjNodes.begin(); it != adjNodes.end(); it++) { //iterates through the adjNodes
vector<string> newPath = path;
newPath.push_back(*it); //add neighbor to newPath vector
queue.push(newPath); //adds resultant path to queue
if (*it == desiredNode) { //if desired found, return current path
return newPath;
}
}
}
}
//if no path, return empty vector
return vector<string>();
}
Depth First Search
Algorithm for DFS.
There are two ways to traverse in DFS, an iterative approach and recursive approach. The following is a recursive approach because it is the most common. In the source code, there will be iterative as well.
vector<string> DFS(map<string, vector<string> > &graph, const string &startingNode, const string &desiredNode, map<string, bool> &visited) {
//mark the current node as visited
visited[startingNode] = true;
//containers of current path
vector<string> path;
//get adjacent nodes of starting node
vector<string> adjNodes = graph[startingNode];
//iterate through adjacent nodes
for ( vector<string>::iterator it = adjNodes.begin(); it != adjNodes.end(); it++) {
const string& nextNode = *adjNodes;
//check if next node has been visited
if (!visited[nextNode]) {
//recursively call DFS on the next node
vector<string> subPath = DFS(graph, nextNode, desiredNode, visited);
//if subPath not empty, path to desired node is found
if (!subPath.empty()) {
//insert current node at beginning of subPath and return final path
subPath.insert(subPath.begin(), startingNode);
return subPath;
}
}
}
//if no path, return empty vector
return vector<string>();
}
Final Words
This tutorial's objective was to implement the BFS and DFS algorithm into Robotino using an alternative method when compared to the first version. Complete source code for the algorithms can be found at the beginning of the tutorial. Once the concepts were conveyed the reader could try to implement BFS and DFS and other path planning algorithms into their own projects.
Speculating future work derived from this tutorial, includes applying these and other algorithms such as Dijkstra and A* to Robotino and other robots. In the big picture, the problem of autonomous navigation can be solved with this tutorial.
For questions, clarifications, etc, Email: palusf1@unlv.nevada.edu