Graph-Based Path Planning: A*

A while back I wrote a post about one of the most popular graph based planning algorithms, Dijkstra’s Algorithm, which would explore a graph and find the shortest path from a starting node to an ending node. I also explored the very fundamentals of graph theory, graph representations as data structures (see octrees), and finally, a python implementation that animates the search process for several example graphs. If you haven’t read that yet, I urge you to please check that out, because I will be continuing the discussion assuming some prior knowledge with the subject.

An example octomap of an outdoor environment. A map this large can be visualized efficiently thanks to the tree-based structure of an Octomap. Source:

Dijkstra vs A*

Dijkstra is novel because it begins exploring from a starting node and continues until it reaches the ending node. It then identifies the list of nodes that connect the end node to the start as one single path. This means that Dijkstra works by exploring nodes in order based by their distance from the starting node. To put it another way, this means that it will equally explore nodes that are farther from the goal as well as nodes closest to the goal as it expands outwards from the starting node. As one can imagine, that can become computationally expensive as our graph representation of our world grows.


Example of Dijkstra’s Algorithm attempting to go from a start node to a goal node. See how it has to expand out almost the entire distance of the path in every direction before it finds the goal? Source:

So, in 1968, an AI researcher by the name of Nils Nilsson tried to improve on this planning approach for Shakey the Robot, a prototype mobile robot from SRI. The algorithm that was developed for Shakey is what is now known as A* (pronounced ‘A Star’). A* tries to improve on Dijkstra’s Algorithm by focusing only on exploring nodes that bring us closer to our goal. This greatly improves our speed of finding the shortest and most direct path.


The same path planning problem as shown above, but look at how quickly the goal is found? With the use of some forcing heuristic, we ensure that we only expand nodes that drive us towards the goal node. Source:

The way A* does this is via the introduction of a heuristic function that guides the node expansion towards the goal. Our heuristic functions will be used to create a correlation from every node in the graph to a non-negative cost value. Heuristic functions (represented as the function H below) must satisfy two basic criteria:

  1. H(goal) = 0
  2. For any two adjacent nodes x and y:
    1. H(x) <= H(y) +d(x, y)
    2. d(x, y) = weight/length of edge from x to y

The notion that our cost is zero at the goal node helps address the issue described above that when we explore from the starting node, we have no real direction. So if we have some extra value that can assess if an explored node will bring us closer to the goal, we improve our search time. Following these two criteria, we can ensure that every node will be less than or equal to the length of the shortest path from node n to the goal. Two of the most popular examples of heuristics are the Euclidean Distance, and Manhattan Distance from any node, n, in the graph to the goal, g, in the graph. We can, therefore, say, given any node, n, at (xₙ, yₙ) and a goal, g, at (xg, yg):

  1. Euclidian Distance
  2. Manhattan Distance

With this heuristic now in place, we can update our previous Dijkstra algorithm as follows:

  • For each node, n, in the graph:
    • n.distance = Infinity, n.g = Infinity
  • Create an empty list
    • start.distance = 0
    • start.f=H(start)
    • add start to list
  • While list not empty:
    • Let current = node in the list with the smallest f value, remove current from list
    • If (current == goal) return Success
    • For each node, n that is adjacent to current:
      • if n.g > (current.g + cost of edge from n to current
        • n.g= current.g+ cost of edge from n to current
        • n.f = n.g + H(n)
        • n.parent = current
        • Add n to list if it is not there already

A* Python Example

In keeping with my previous post on Dijkstra’s Algorithm, I have written a quick Python example to illustrate the above algorithm.

To get started, let’s clone the repo locally on your machine:

$ git clone

With that cloned locally, you should be able to run it by navigating to:


and running:

$ python

You should see a window open up similar to the one below showing an occupancy map. The darker blue color is free space, the lighter blues are walls/obstacles. The yellow square is the starting node, and the orange square is the goal node. You will see that we will begin searching around the starting node. Once a node is searched, it will become aqua blue, and the search will keep radiating out until the goal node is found. Once that is found, the shortest path is calculated and visualized in red.


A* Algorithm in action for an Occupancy Map. It will search until the goal is found, then visualize the path in red.

In the above example, there is a large L shaped wall that would prevent the quick expansion of a Dijkstra approach. That method would have to wrap around the wall and search nearly the entire map before it found the goal. Alternatively, our A* searches grow towards the goal until it is reached, with very little concern for nodes that take us away from the ending node.

In the example map code, you can see that we define a test_grid variable as a list of lists where ‘0’ indicates an open node and ‘1’ indicates an obstacle:

    test_grid = [[0, 0, 0, 0, 0, 0],
                 [0, 1, 1, 1, 1, 0],
                 [0, 1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 0, 0],
                 [0, 1, 0, 0, 1, 0]]

We then define the start and end nodes as a list of [x, y] node positions (starting from 0). Using this basic format, you can challenge yourself to create different maps to test your A* Algorithms.

Another interesting thing is our heuristic function, which will calculate how far each node is on the graph from our goal node. For a map this size, calculating the cost from the goal to each surrounding node costs very little, but for larger robotic applications, it makes more sense to calculate the heuristic at every iteration of the loop for computational efficiency.

What makes this A* implementation especially intriguing is the way we handle the heuristic value in our loop. As shown below:

for i in range(len(delta)):
    x2 = x + delta[i][0]
    y2 = y + delta[i][1]
      if len(self.grid) > x2 >= 0 <= y2 < len(self.grid[0]):
          if closed[x2][y2] == 0 and self.grid[x2][y2] == 0:
              g2 = g + cost
              f = g2 + self.heuristic[x2][y2]
              open.append([f, g2, x2, y2])
              closed[x2][y2] = 1
              delta_tracker[x2][y2] = i

In this loop, we will search through every possible orthogonal direction, and then calculate the f value as described in the pseudo-code above, which is just the running cost + the heuristic cost. Once our cost is found for the explored node, we add it to a running list of possible “open” nodes from which we pick the lowest f value on the next iteration, thus bringing us closer to the goal.

That’s it. That’s what makes A* such an amazing yet powerful algorithm!

Where to go from here:

If you are still curious about playing with some code, I challenge you to upgrade my Dijkstra’s code to A*, since I decided to go a different route with my example.

If you want to look at other examples of A* in Python, check out Python Robotics’ A* Example.

A* ROS Example

The ROS Navigation Stack is a set of software packages that make path planning and motion control (more on this in a coming post) as simple as possible for differential drive and holonomic wheeled mobile robots. There are multiple packages in this stack to help with everything from building up Cost Maps with costmap_2d to localization with packages such as amcl, which is an adaptive Monte Carlo Localization approach. Monte Carlo Localization is the process of using a known map and sensor measurements to localize where a robot is with a high degree of confidence using something called particle filters (see my other post about Kalman Filters for some motivation on the state estimation problem, PFs are just another type of filter). The most pertinent portion of the stack for Grid-Based path planning will be the global_planner package.




This package uses an implementation of A* for a fast, interpolated global planner for navigation via a discretized map. There are other implementations like a traditional Dijkstra planner that are available for configuration during launch, but the A* is undoubtedly faster.

While this provides a high-level plan through a map, the question remains of how the robot’s base frame must be controlled taking into account local obstacles such as corners of walls and stand-alone objects. The move_base package provides such a feature. Once the global planner searches and publishes a plan, this package will then take that plan and link the global/local planners with an actual controller. Below is a graphic showing how the move_base node subscribes and publishes to various topics for a differential wheel robot.


Enter a caption

Looking at the source code directly on the ROS Navigation Github, you can see the source code for the global planning package has multiple source files, one of which is devoted solely for A*. The source script shows the implementation of the AStarExpansion class. The real planner is in AStarExpansion::calculatePotentials(), which will take in a starting pose, an end pose, and then iterate through a potential field and add each node it expands to a queue until the expanded node reaches the goal. If it does not find a goal, it returns back a ‘false’.

bool AStarExpansion::calculatePotentials(unsigned char* costs, double start_x, double start_y, double end_x, double end_y,
                                        int cycles, float* potential) {
    int start_i = toIndex(start_x, start_y);
    queue_.push_back(Index(start_i, 0));

    std::fill(potential, potential + ns_, POT_HIGH);
    potential[start_i] = 0;

    int goal_i = toIndex(end_x, end_y);
    int cycle = 0;

    while (queue_.size() > 0 && cycle < cycles) {
        Index top = queue_[0];
        std::pop_heap(queue_.begin(), queue_.end(), greater1());

        int i = top.i;
        if (i == goal_i)
            return true;

        add(costs, potential, potential[i], i + 1, end_x, end_y);
        add(costs, potential, potential[i], i - 1, end_x, end_y);
        add(costs, potential, potential[i], i + nx_, end_x, end_y);
        add(costs, potential, potential[i], i - nx_, end_x, end_y);


    return false;

Example with ROS/Gazebo:

Using our trusty Turtlebot simulator and a previously explored map, we can have Turtlebot plan a global path, then navigate to that goal on the map with a Monte Carlo Localization node to provide a Particle Filter/Estimate of where the robot exists in the map and a controller to actually move the base frame of the robot.

To install the package, we will run:

$ sudo apt-get install ros-[YOURDISTRO]-turtlebot-stage

Then once that is installed, run:

$ rospack profile # Update our ROS package lists to find our new package.
$ roslaunch turtlebot_stage turtlebot_in_stage.launch 

If everything loads successfully, you should be prompted with an RViz window similar to this:


RViz window showing Turtlebot Navigating towards a goal.

In the gif above, you see the robot was given a pose, an (x,y) position in space and an orientation via the red arrow that the local planner and controller are now using. The faint red line is the output of the A* planner and the square grid around it is how the local planner is following the desired trajectory from the global planner.

To give our robot a goal, we will click the button labeled “2D Nav Goal”, and click anywhere on the map, and drag in the direction we want it to have its final orientation. From there, you should see the robot move towards the goal as shown below:

A* is an incredibly powerful algorithm that not only has applications in path planning for mobile robotics but also for general Artificial Intelligence, including the problem of semantic parsing using stochastic grammars in Natural Language Processing! It really makes you appreciate how simple ideas can find usage in world-changing applications.

Until next time, happy robotting!

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Have a comment, issue, or question? Leave a comment or message me on GitHub.


  1. Robotics: Motion Planning by CJ Taylor
  2. Artificial Intelligence for Robotics by Sebastian Thrun

State Estimation: Kalman Filters

I know it has been awhile since I last posted, so let’s hit the ground running!

As a quick recap, in my last post, I began to discuss mobile robots and how we can programmatically endow these systems with the ability to find a trajectory to get from a starting point to a goal point. While using Dijkstra’s Algorithm (or any other flavor of path planning algorithm) helps solve that problem in a neat little box, that is only a fraction of the problem. Another portion is actually using a controller to move along that trajectory, and then utilizing various sensors to estimate where we are along that planned path. State estimation is not only important for path planning and trajectory following, but also of utmost importance for intelligent mobile robots that explore new areas and build maps through SLAM (Simultaneous Localization and Mapping) for us to localize moving targets in the world. An example of this would be autonomous vehicles tracking other cars on the road.

Kalman Filters allow us to not only track our own position in space but keep track of other dynamic bodies in the environment, such as cars on the road. Source:

Tracking objects of interest for the robot to interact with (or avoid) as well as its position in space is a very difficult task because the robot must sample from a slew of sensors, such as IMUs, object tracking cameras, and wheel encoders many times a minute for real time operation. The catch 22 is that no sensor is perfect so with each sample we accumulate error, aka noise, thus resulting in a low confidence of the “true” state of our robot or its environment. This can lead to many challenging situations as most robots must navigate collision free while simultaneously interacting with an incredibly dynamic environment. In prior robotic applications, such as robot arms in factories, the robot’s environment must be strictly constrained to minimize error while the robot performs its task. Modern day roboticists aim to produce robots that do not rely on these environmental constraints in order to properly function but are capable of reacting intelligently when placed in more complex settings.

Saved by the Bell (Curve)

Gaussian functions (distributions) are one of the most common mathematical functions in statistics and have applications in other branches of math, science, business analysis, AI, and so on. Most people know them as Bell Curves, and they’re handy little creatures for a slew of reasons:

  1. They are comprised of two parameters, a mean and a variance, and therefore are easy to compute and interpret visually.
  2. They have extremely useful mathematical properties. Any complex Gaussian distribution can be broken down into simpler Gaussians that make them up.
  3. They are fine examples of the Central Limit Theorem, which states that given a large enough sample size with some finite threshold of variances, the mean of all the samples will be the mean of the entire population. Basically, if you take a bunch of samples, and it seems to suggest something, that means that it’s probably true for the total population.

As stated above, a 1 dimensional (1D) Gaussian really has two parameters, a mean and a variance, which we can express mathematically as such:


A Standard 1D Gaussian Distribution Function.

The statement above should be read as “the probability of x“, which will be the variable we are trying to model, with 𝜇 being the mean of the distribution, 𝜎² is the variance, and 𝜎 is the standard deviation. Below are a few examples of 1D Gaussians:

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Above are several examples of 1D Gaussian Distribution illustrating how the adjusting the mean and variance will shift and stretch/compress the distribution. Credit: Dan Lee

While 1D Gaussians have a variety of applications, we live in a multi-variable world, therefore we will need to explore multi-variable Gaussians. Multivariable Gaussians follow the same bell curve styled shape as a 1D Gaussian, but imagine that it’s similar now to a tall hat with a wide brim around it:

Example 2D Gaussian Distribution, note that if viewed from orthogonal views along the X, or Y axis, it would look like two separate 1D Gaussians.

We can describe a Multivariate Gaussian with the following equation:

or we can also model it as follows:

Where is the number of dimensions (in the image above, it is two), x is our variable of interest, μ is the Mean vector, and Σ is the Covariance matrix. Note how some of these values are bolded, indicating they are non-scalar variables. As a quick exercise, I suggest the interested reader use a D value of 1 in the above equation and simplify it. You will see that it will be exactly the same as the 1D equation shown above.

One of the newest terms above is Σ, the covariance matrix, which contains variance terms along the diagonal and correlation terms in the off-diagonal. The Covariance matrix has a few interesting properties. The Covariance matrix will always:

  1. Be Positive Definite and Symmetric.
  2. Have a property known as Diagonalization: Σ can be decomposed in the form of UDUᵀ. (D is a Diagonal matrix.)

An example of a Covariance Matrix is shown below:


Like the 1D examples above, Multivariable Gaussians are centered around the mean value of μ, which is the center and the highest peak of the Gaussian. For the sake of easy visualization, we will be looking at 2D Gaussians for the remainder of the section. Since we have a 2D example, the μ matrix will be of size 2×1, and our state will also be size 2×1. We can visualize it as follows:

Untitled drawing (2)

A simple example of a 2D Gaussian. Source: Dan Lee, University of Pennsylvania

Much like in the 1D example, any changes to the mean matrix will shift the Gaussian to be centered around that point in space as shown in the following image. This image also shows how the Covariance matrix will now affect the width of the Gaussian in the component directions.

A shift in the Gaussian’s mean moves the entire distribution.  Source: Dan Lee, University of Pennsylvania

Notice in the examples above, the Σ matrix, is Identity. That means it will have a width of one unit in both the x direction and the y direction. If we were to shift these sigma values to be larger than one, as expected, our distribution will get wider in their component directions. If they get smaller, the distribution will become more narrow. If you think about it, it makes sense. Since our Covariance matrix is really just a matrice of variances, if we have a lot of data that is spread out, our distribution is spread out. If we have little variation in a group of data, our variance, and in turn, our covariance is very minimalistic.

RealityBytes (1)

The Covariance matrix describes the width of our Gaussian. Source: Dan Lee, University of Pennsylvania

One thing to note from above is that the covariance matrices have no off-diagonal terms. When we have variable values in the off diagonal terms, the top down view of the Gaussian doesn’t stay circular, but will, in fact, turn ovular with a skew to one side or the other.


Two Dimensional General Case Covariance Matrix. Source: Dan Lee, University of Pennsylvania

I will close this section with a few properties of interest in regards to the Covariance Matrix:

  • The Covariance Matrix is ALWAYS Symmetric and Positive Definite.
  • The Covariance Matrix has a property known as Diagonalization. That means that the Covariance Matrix can be decomposed into the form of UDUᵀ where D is the diagonal matrix.

The Kalman Filter

Enough statistics, let’s get back to robotics and see how we can use this to our advantage. Robots are very complex dynamical systems that bring together a multitude of sensors, mechanisms, algorithms, and electronics. Fortunately, in order to make things easier for us to implement controllers and track their state, we can break robots down into abstractions while problem-solving. Taking a page out of control theory, we can utilize state-space representation to decompose our robot into a system state and observed measurements.

A robot’s state is any quantity of interest for that robot modeled as a dynamical system. These quantities are things like position, velocity, acceleration, and orientation in space – keep in mind, this is in terms of our robot’s base frame, or the object we are tracking. The measurement abstraction is what we observe. What do our sensors pick up, what do we measure? Examples of that are range sensors giving us distance from a wall, rotational sensors tracking our angle from an initial angle, IMUs giving us acceleration changes, and the colors of an object in our camera. We use the measurements from these sensors to update what state we are in. What Kalman Filters (KFs) really do are two very simple things: They update our current belief of our state based off of measurements taken at previous and current time-steps. KFs also use the data we have collected to predict what the next state will be for our system.

RealityBytes (2)

High-Level View of A Kalman Filter.

Note how I used the term belief, this is where all of the statistics introduced above will be brought back into play. We represent our beliefs at any discrete time-step, t, by use of the mean, μₜ and covariance, Σₜ, of our system in question.

A Kalman Filter can be applied to any system given the following three criteria:

  1. The state transition system must be linear. That is to say: ẋ=Ax + Bu + ε as described in the link above, where x is the state vector, u is the control vector and A and B  are matrices of size n x n  and  n x m respectively with  being the dimension of the state vector x, and m being the dimension of the control vector u. The last term, ε, is a Gaussian random vector that captures the uncertainty introduced by the state transition. It will be the same dimension as the state vector with a mean of zero and a covariance of R. Using the formula above for multivariable Gaussian, we can say the state transition probability is as follows:

  2. The measurement probability p(z|x) must also be linear, with added Gaussian noise: z = Cx + 𝛿, where C is a k x n matrix with k being the dimension of the measurement vector z. The added sensor noise vector 𝛿  is a multivariate Gaussian with a mean of zero and a covariance of Q. We can apply the same equation again and model the measurement probability as follows:
  3. Given the two criteria above, we can then assume that our belief will be a normal distribution as well. We can then state that our belief is as follows:
    If you wish for a full proof of the above three equations and the derivation of the Kalman Filter, you can find it in Probabilistic Robotics By Sebastian Thrun in Chapter 3.2.4.

Kalman Filters take these Gaussian models of our state and measurements and helps us represent our belief at some time-step, t, by the mean and covariance, μₜ and Σrespectively. In order to calculate what our current estimate is at the current time-step, we will use the previous time-step estimates as inputs: t, μₜ₋₁, and Σₜ₋₁. In order to update these parameters, the Kalman filter will require the control and measurement values uₜ and zₜ. Therefore, we can algorithmically write it out as such:  

As you can see, the function takes in our previous estimate of our mean and covariance as well as our new control and measurement vectors. This is then used to calculate our new estimate of where we are as well as predict where we’re going.

Going through it line by line, our Kalman gain specifies the degree to with which we allow our new measurement to be incorporated into the state estimator. This is helpful because it keeps the filter in check and doesn’t allow for any crazy jumps. It smoothes things out. After we calculate Kalman gain, we incorporate it into the state estimate as well as the deviation of the actual measurement (z) as well as our prediction of the previous state and our expected measurement (C). Once that’s done, we take all of these terms and update our covariance. In the prediction portion of the algorithm, we calculate what we expect the next time-step mean and covariance should be (hence the “bar notation”), this is where we fill in the A andmatrices to calculate it.

The Kalman filter is also an efficient algorithm from a computational point of view during the update steps. As you can see in the previous examples, it’s not very long, nor is it very complicated. It’s all relatively simple math. For most matrix libraries, the inversion of a d x d matrix is roughly O(d^2.4). For each loop of the filter, the lower bound will be similar, O(k^2.4) with being the dimension of our measurement vector z. As the text Probabilistic Robotics points out, in many robotic applications such as mapping, the performance of this filter will be dominated by the as O(n²) given the measurement space is much lower than the state spacePlease note that the predicting step can be very intensive, but thankfully there are some ways to help minimize that cost in situations such as SLAM where the covariance requires an AΣₜ₋₁Aᵀ  computation where A is near identity. Although this special case requires the AΣₜ₋₁Aᵀ  to be evaluated specifically using things such as sparse matrix multiplication.


Illustration of Kalman filters: (a) initial belief, (b) a measurement (in bold)
with the associated uncertainty, (c) belief after integrating the measurement into the
belief using the Kalman filter algorithm, (d) belief after a motion to the right (which
introduces uncertainty), (e) a new measurement with associated uncertainty, and (f)
the resulting belief. Source: Probabilistic Robotics, Thrun

Using a Kalman in Python

Now imagine a mobile robot moving in a straight line. As stated above, a perfect sensor does not exist, so we will always have some level of noise (modeled by Gaussians) that will lead to error in our state estimation. To counteract the noise from our sensor, we can call on our handy friend the Kalman filter to try and track what our actual trajectory is in an X-Y plane to ensure we are moving along the desired path. To do that, we will use a bit of Python.

As shown in the section above, the standard Kalman Filter is relatively easy to implement in software. If you followed my path planning example code last post, all you will have to do is use:

$ git fetch
$ git pull

If you did not, you can easily clone it into a local directory on your computer by running:

$ git clone

Then it should pull in all the new features. You will then want to navigate to:


You will then run:

$ python

Which should generate a window like the one shown below:



The output of the example code. You will see that for the example path a robot is taking, the Kalman Filter is providing a reasonable estimate of its trajectory given noisy data.

In this example, we have several noisy measurements taken as our robot moved along its path, as well as the actual trajectory as estimated by the Kalman Filter. The projected trajectory is relatively linear, which is exactly what we hope to see. The code itself is relatively simple. It uses the same linear equations shown above and a few other equations to ensure our data is in the correct format and consumed properly. Same as above, we return the same values as feedback into our estimate. I personally find the filtering process really cool because it enables us to have a higher degree of confidence that our robot is where we think it is.

Having accurate state information is crucial as we attempt to bring robots out of controlled environments into areas with dynamic surroundings that we can’t control nor model. In these new settings, robots will have to intelligently localize where they are in the world through exploration and map building as well as returning to known areas for various tasks.

I strongly suggest you play around with the data such as the starting state and the starting covariance matrix, as well as various trajectories and see how the filter reacts. Can you make some adjustments to make it more robust to patterns that zig-zag back and forth? If you end up doing something with it, I would love to see what you guys have!

Extended Kalman Filters and ROS

I previously stated that one of the criteria for the Kalman Filter algorithm was that the system we were going to track and estimate be linear; unfortunately, that’s not always an option as our systems become more and more complex. The constraint of a linear system lead to the development of two other variations of the Kalman Filter: the Extended Kalman Filter and the Unscented Kalman Filter.

The extended Kalman filter attempts to solve the problem by linearizing around an estimate of the mean and covariance at a current time-step. It’s very popular in navigation systems, so it’s a very matured algorithm. I urge you to read through the links above if you are interested in some of the underlying math. For the time being, I’m going to just show you how you can leverage its power with an already existing ROS package.

The package robot_pose_ekf is a catkin package that is part of the ROS Navigation Stack. The true power of this package is that it will estimate a robot’s position and orientation in 3D as well as fuse data from odometry, IMUs, and Visual Odometry out of the box. This is incredibly attractive for robotic applications like SLAM, where exploration and map building rely so heavily on our estimate of where we are in space!


Experimental Results from the robot_pose_ekf package. Source:

In this section, we will be using Gazebo Simulation and TurtleBot to map out the simulation world of Willow Garage (the people who developed the PR2 and kind of made ROS a thing).

If you followed the example above, you will have already cloned the Catkin Package that I created named turtlebot_ekf. I took several existing Turtlebot packages for ROS and Gazebo, and compiled them all into a few simple launch files for ease of usage.

I suggest cloning the above git link into a catkin workspace then building and sourcing the workspace. From there, open four terminals and launch in the first three:

$ roslaunch turtlebot_ekf spawn_turtlebot_willow_world.launch

$ roslaunch turtlebot_ekf mapping.launch

$ roslaunch turtlebot_teleop keyboard_teleop.launch

You should then be prompted with a Gazebo simulation window for the Willow Garage world with the Turtlebot robot in one of the rooms. You should also have an RViz Window open below that already loads the proper configuration to visualize the robot as well as the map that it is building.



Gazebo Simulation and RVIZ window showing the Turtlebot as it explores the environment and builds up a map. The bottom right-hand terminal shows the robot_pose_ekf package estimating its position in space relative to its starting point.

In the final terminal you will want to run:

$ rostopic echo /robot_pose_ekf/odom_combined

That will show a direct stream of our robot’s pose. It is important to note that this is relative to the starting position of the robot. If the robot were to return to a previously explored map of interest, it’s reference odometry frame will be different. The robot will have to use various keypoints from the environment to place itself in the world. While this is relevant, it is out of scope for this post.

I suggest you use the third window and drive around the robot. You will see not only how the map builds up, but as you pass over areas multiple times, it will build confidence in it’s belief that specific obstacles are there. It will also show how the robot’s pose is tracked with an associated covariance in the EKF window:


I encourage you to drive the vehicle around and play a bit more with some of the mapping functionality. The especially curious reader should look through some of the source code to see how the algorithm is implemented at this link.

Happy roboting and thank you for reading!!!


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Graph-Based Path Planning: Dijkstra’s Algorithm.

In my last post, we built on kinematics by introducing Jacobians for robotic manipulators. This week, I’m looking to shift a bit and start to explore mobile robots and path planning. Mobile robots are all around us now. From useful Roombas to the rise of autonomous cars, mobile robots are becoming more and more popular in our everyday lives. These robots, unsurprisingly, move around and must use an array of sensors to perceive their environments, even accounting for how these environments may change. Robots must be able to achieve specific goals, and that requires them to navigate to specific areas of interest. So, how do we go about giving robots this ability? Aside from more advanced problems like SLAM (Simultaneous Localization and Mapping), which  requires a robot to construct a map while trying to estimate (guesstimate) their position in space, there are a number of well-known algorithms to plan the shortest path from a start location to a goal location in an (assumed) static map. So let’s get into it!

Amazon’s (previously Kiva Systems’) mobile warehouse robots must be able to plan a path from their current location to a goal location in order to move goods efficiently from storage to shipping. Note that a more collaborative level is also at work to ensure robots aren’t colliding, but that is out of the scope of this post.

What’s a Graph?

To answer the question of what a graph is, we turn to a branch of mathematics called Graph Theory which states that a graph is a structure that relates a set of vertices (or nodes) through edges. We can, therefore, state that a graph is an ordered pair G = (V, E), with V being the set of vertices and E their associated edges.

This is a simple example of a graph and shows the relationship between vertices/nodes via edges. Source:

This definition is purposefully general as graphs can be used to model various data structures, for instance, an Octree. Octrees have applications in things such as 3D computer graphics, spatial indexing, nearest neighbor searches, finite element analysis, and state estimation.

An Octree is a tree-based data structure with a root node that branches out into 8 children nodes that can then branch out even further. Source:

In robotics especially, octrees have been leveraged via the creation of the OctoMap Library, which implements a 3D occupancy grid mapping approach. This provides data structures and mapping algorithms that not only assist in mobile robot navigation and mapping, but also helps in path planning for manipulators in cluttered environments.

An example octomap of an outdoor environment. A map this large can be visualized efficiently thanks to the tree based structure of an Octomap. Source:

Dijkstra’s Algorithm

Dijkstra’s Algorithm is a fairly generic way to find the shortest path between two vertices that are connected by edges. Let me present to you an interesting problem. Say that we are planning a trip with connecting flights, and we want to get from one city to another in the most efficient way, we can generate a graph like this:

An example graph relating cities and their distances from each other. Source:

Say that we wanted to plan a trip from Austin to Washington. There are several paths that we can take:

  1. Path A, 1560 miles: Austin -> Houston -> Atlanta -> Washington
  2. Path B, 2980 miles: Austin -> Dallas -> Denver -> Atlanta -> Washington

Notwithstanding the philosophical quandary regarding the merits of the journey versus the destination, if our final goal was Washington, Path A would definitely be the most desirable. The above example was solved with relative ease by looking through the graph and quickly summing up the distances between edges of our starting node in Austin and our destination node, Washington. One can also solve it by trying to count the number of nodes between the start point and end point.

Another example of a graph is a grid-based structure as shown below. This grid denotes a starting point and a goal point with several different paths that would solve the problem. In this type of graph, each square is a node (vertex) that is connected to its four surrounding neighbors via edges that have a value of one.


An example grid that shows multiple solutions to a path planning problem. Source: C.J. Taylor, University of Pennsylvania

Again, we can solve the above path planning problem by counting how many steps it would take to reach the start position from the goal, or vice versa. While this is a real planning solution (called the Grassfire Algorithm), it’s often tedious and very computationally intensive because each node must be visited to find the shortest path. This can pose a serious problem if our map is comprised of hundreds of nodes.


By counting the number of steps it would take to go from the goal to our starting point, we can find the shortest path. This is a very tedious process because each node must be visited.  Source: C.J. Taylor, University of Pennsylvania

While the above two examples are easy to solve as humans, the real difficulty is programmatically solving these problems in a robust and efficient manner. Enter Dijkstra’s Algorithm, which was developed by Edsger W. Dijkstra. Djikstra was incredibly influential in the rise of the field of computer science, both from theoretical and implementation/engineering perspectives. Dijkstra began to try and solve the path planning problem in 1956, and then published his solution three years later detailing out his algorithm. The above example started at the goal and radiated outwards until it visited every node, counting each step along the way. Djikstra proposed a similar solution, but one that starts at the first node and grows out towards the goal. Using “Big O” notation, we can state that the time it will take to run this algorithm increases linearly with the number of edges, meaning: O(|V|), where |V| is the number of nodes/vertices on the graph.

We can describe the algorithm in pseudo-code as follows:

  • For each node, n, in the graph:
    • n.distance = Infinity
  • Create an empty list
    • start.distance = 0, add start to list
  • While list not empty:
    • Let current = node in the list with the smallest distance, remove current from list
  • For each node, n that is adjacent to current:
    • if n.distance > current.distance + length of edge from n to current
    • n.distance = current.distance + length of edge from n to current
    • Add n to list if it is not there already

Using the above pseudocode, we can find the shortest path from Austin to Washington in a programmatic manner. Below is a great animation I found that really shows the algorithm in action:


Dijkstra’s Algorithm on a graph. Source:

Computational Complexity of Dijkstra’s Algorithm

Djikstra’s algorithm is an improvement to the Grassfire method because it often will reach the goal node before having to search the entire graph; however, it does come with some drawbacks.  On occasion, it may search nearly the entire map before determining the shortest path. That can be both tedious and time-consuming depending on the number of nodes to be searched.  We can estimate worst case performance by using “Big O” notation, O(|V|²).

One can improve the efficiency of Dijkstra’s Algorithm by using a clever data structure known as a priority queue. This particular data structure is different from a normal queue data structure because instead of the standard “first in, first out” implementation, it will pop the values based off of the associated priority.

Simple visualization of a priority queue. Source:

This simple data structure can help reduce how quickly the computational load increases. This rate can be described as O((|E|+|V|)log(|V|)). Where |E| and |V| are the number of edges and vertices (nodes) respectively.

Dijkstra’s Algorithm in Python

This week’s code snippet is purely Python and is a modified implementation of Dijkstra’s that I used for a mobile robot.  To get started, let’s clone the repo locally on your machine:

$ git clone

With that cloned locally, you should be able to run it by navigating to:


and running:

$ python

You should see a window open up similar to the one below showing an occupancy map. The darker blue color is free space, the lighter blues are walls/obstacles. The yellow square is the starting node, and the orange square is the goal node. You will see that we will begin searching around the starting node. Once a node is searched, it will become aqua blue, and the search will keep radiating out until the goal node is found. Once that is found, the shortest path is calculated and visualized in red!

There are three maps that will run in sequence if you just run the command above. The first two will iterate and find the goal, while the last one will fail because the goal node is blocked off from the start node via a wall. This was intentional to test my implementation and how it will handle such an event.


Dijkstra’s Algorithm in action for an Occupancy Map. It will search until the goal is found and then visualize the path in red.

Once you’ve run the out of the box code, let’s really look into it and see what’s going on. The function itself takes five arguments and returns a discrete path or a failure boolean:

def dijkstras(occupancy_map, x_spacing, y_spacing, start, goal)

The variable occupancy_map is a NxM numpy array of 0s and 1s that represents the locations of walls and obstacles as well as free space in the world. The variables x_spacing and y_spacing are parameters that represent the space between adjacent columns and rows respectively. I told you that this was implemented on a real robot, so when a map was explored and built up, we discretized the metric values into nodes on a graph using the following relationships:

x = (j + 0.5)*x_spacing
y = (i + 0.5)*y_spacing

The starting and goal positions are a 3×1 numpy array of (x, y, theta) with theta being the orientation of the robot in space. We don’t need to worry too much about any constraints on theta because we assume the robot is holonomic. Holonomic means that the robot is like a Roomba. It can rotate around its z-axis, without having to move in the x or y directions. This makes planning much easier for this type of robot vs. a nonholonomic robot, like a car.

Two very useful variables that I have defined in the Python file are DEBUG  and VISUAL.  If DEBUG is set equal to ‘True’, then the print statements within the code will be displayed on the terminal during every iteration. If VISUAL is set to ‘True’, then the animation windows will be created and displayed as the algorithm runs. Use them as you see fit to play around with maps, adjusting the start and goal positions. You can also change the dimensions and “landscape” of the occupancy maps that you pass to the algorithm. Again, I really urge that you take the time to read through the code, the comments, and play around with Dijkstra’s Algorithm to get a good feel for it!


Setting the DEBUG and VISUAL flags to True will show the animation as well as the data flow behind the scenes on your screen.

In future posts, we will look to an extension of Dijkstra called A* (pronounced “A Star”), and how it uses one relatively simple trick to make Dijkstra a little more efficient.

Until next time, happy robotting!

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