Embedded Motion Control 2015 Group 5

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This Wikipedia page is written as a log of the project of the course Embedded Motion Control. The aim of this course is to design and apply software in the context of an autonomous robot. Therefore two assessment moments are held. This Wikipedia page is structured according to these two assessment moments, namely the corridor challenge and the maze challenge.

Group Members

Name: Student id:
Bart van Willigen 0770142
Joost Peters 0747630
Robin Loose 0771575
Koen Bos 0763939
Joost Franssen 0824821
Lotte de Koning 0655209
Marjon van 't Klooster 0819200

Software Design

System interfaces.
Task-skill-motion context

The goal of this exercise is to solve a unknown maze with the PICO robot. The requirements and specification we made, can be found here. The software design for this challenge is represented in two ways: a task-skill-motion context and a composition pattern. The task-skill-motion context represents the model for the behavior of the software and the composition pattern is a model for the structure of the software and described the interaction of the behavior model. While developing the software it is important to decouple these “5C’s” of the composition pattern for the functionality. To develop a system, in this case a system for the PICO robot to solve the maze, it is important to couple all the functionalities.

Task-skill-motion context

The task-skill-motion context flowchart is shown in the figure to the right:

  • Challenge context: the goal is to solve the maze in an optimal way.
  • Robot context performs the communication between the software and the hardware. It sends out the LRF-sensor data and receives the velocity parameters from the software. This is in our cases provided, only one layer is added to make sure that the given LRF data is useful for the software.
  • Environment context uses the LRF-sensor data to analyze the environment. This information is passed to the task context and the skill context, where it is saved in the topological map.
  • Task context makes the decisions based on the maze-solving algorithm we use. This context feeds and monitors the skill context.
  • Skill context contains the navigation (motion skills) and the maze mapping. The motion skills sends instructions to the robot context. The maze mapping saves data in the topological map about the environment and the taken decisions.

Composition pattern

Each context of the task-skill-motion context has a composition pattern. For example the task context. The task context receives information from the environment context and the skill context. This information is monitored (configuration) to see if PICO is running into a collision or if PICO is still executing a skill. The coordinator can be found in the feedforward block, which makes decisions based on the input from the environment analysis and the feedback from the task monitor. This decision is translated to an output for the skill context (computation). The skill context can use this output to execute a skill (for example rotating) and to update the decision in the topological map. The scheduler will make sure the decisions are executed in the right order. For example, always first prevent a collision. The data is communicated to the skill context.

Corridor challenge

During an intermediate review on May 13th the group’s progression is assessed for the first time. The corridor challenge, or corridor competition, requires the design teams to let the robot drive through a corridor and then take the first exit. The precise location of this exit is unknown in advance. For more specifics on the corridor challenge see [1]. The Corridor challenge provides an indication of the group’s progress on taking the first steps towards an autonomous PICO.


For the corridor competition several considerations are made towards the first design steps of the code. This paragraph presents an overview of the most important decisions that were made during the meetings before the corridor challenge.

LRF data
To know and use the position and orientation of the robot and its surroundings, either the Odometry data or the Laser Range Finder is used for the main data acquisition. For the Corridor challenge, only the local positioning and the local orientation of PICO are important. Since the Odometry data is highly affected by disturbances (e.g. slip of the wheels) it is not very accurate for local positioning and orientation. Therefore the LRF is more suitable for this challenge than the Odometry data. The data can be divided in different bundles, all kinds of different tasks and skills can make use of the bundles which are most applicable. When global positioning and orientation become more important, the Odometry data can be used for rough estimations. For the Corridor challenge, the LRF data has not been altered, the raw laser data is used to make PICO navigate through the corridor.

Manual setpoints
Instead of implementing a potential field, the group decided to manually place setpoints and use these as reference for e.g. cornering. An example of such a manual setpoint is elaborated on in the code overview in this chapter: Center at junction.

During the challenge, it was clearly visible which groups used a potential field: PICO drove through the corridor very fluently. However, the main disadvantage of using such a potential field is not being able to manually place and control the setpoints.

Robustness over speed
Although the goal is to complete the corridor challenge as fast as possible, the group decided that robustness of the code had priority over completing the competition the fastest way.

Simple cornering strategy
For the corridor challenge a simple corning strategy is used based on driving to the center of the junction and rotating accordingly. More optimal trajectory planning will be developed for the Maze challenge.

Code overview

The group decided that the corridor challenge provided a good opportunity to attempt to implement the newly learned code design and composition patterns. The corridor challenge could improve our understanding of the link between schematics on paper, and C++ code on screen. Therefore the structure of the code should be considered during the first design steps of the code. The structured code also helps to ease the multi-team development of the C++ code.

Aligning and centering the robot
In order to drive straight and centered in a corridor, a function is created which aligns and centers the robot w.r.t. the corridor walls. The function consists of two parts; a part that uses two beam bundles on each side of the robot and compares their length in order to center the robot (Figure left), and a part that uses three small beam bundles on one side of the robot to align it with the walls. A safety check is implemented to make sure the robot ignores cracks in the corridor walls. A proportional controller is implemented for correcting the robot’s misalignment, this means that the more the robot is off-center or misaligned, the larger the control action will be to correct this.

Implementation in corridor challenge:
When implementing the function for the corridor challenge, two issues arose: the proportional control could lead to problems when the system stability was compromised. Extreme high gain would result easily into a crash. A second issue was compatibility with the rotate function; during rotation at the corner, the alignment function interfered and this resulted into unwanted effects. A reason for this could be our main algorithm that consisted of a switch-case construction that was far from optimal. During the corridor challenge the align-part was disabled in order to complete the challenge.

Recognize junction
The robot is able to robustly distinguish cracks from junction. If the laser on the left or right of the robot detects a large distance, a bundle of lasers around the perpendicular laserbeam is analyzed. The angle between the first and last bundle with a large distance is calculated and this angle, combined with the corresponding distances at the outer beams of the bundle, is used to calculate the width of the crack (or junction).

width_crack = sin(angle_ crack2)*distance[outer_ bundle];

This width is now used to distinguish small cracks from actual junctions in the corridor.

Distinguish cracks from junctions

Take a turn
When a decision is made to turn left or right some skills will be performed. First PICO is positioned in the middle of the junction, with the skill: junction_mid. Then PICO will rotate on his position around his axis with the function rotate and after this PICO will drive forward until it is in the corridor. When two walls are detected next to him the center function will take over and will drive further until the exit is detected. These different skills are described below.

Center at junction
When a junction is detected and the decision has been made to enter a specific corridor, the function ‘junction_mid’ is started. The goal of this function is to stand still at the exact middle (target) of the corridor which is about to be entered. To find this exact middle, the beam to the opposing corner of the entrance is measured (1) as soon as beam (2) passes the corner. Beam (1) can then be used to compute the width of the corridor (corr_width) and therefore, the middle of the corridor is also known. Junction_mid ends when it has reached the half width of the corridor (target).

The rotation for the corridor challenge is based on the form of the corner of the wall. As example the right rotation will be used as explanation. First the corner is detected through analyzing the laser bundle at the upper right quarter of PICO, the shortest vector bundle is used as reference for the corner. For a 90 degrees rotation it can be calculated which vector bundle is needed at the left side of PICO. PICO will rotate until the vector bundle at his left side is equal to the reference vector bundle. See the rotation skill figure below.

Center skill for on a junction
Rotation skill

Detect exit
This function is constantly checking if the exit of the maze is reached. The LRF data is divided into ten bundles and if all of these bundles each show an average distance larger than 1.5 m, the exit of the maze is reached. In this case, the robot stops and the program is ended.


PICO executed the Corridor Challenge as expected, however this was most certainly not the most efficient way:

  • Centering PICO between two walls was not robust with gaps in the walls;
  • Cornering was not efficient: PICO did not follow the apex of the corner;
  • PICO was not programmed to drive at maximum (driving/angular) velocity;
  • about 90% of the data was thrown away;
  • The code's structure was insufficient.

If PICO had to do the Corridor Challenge again, certain changes would be made to the code:

  • The entire code structure has to be revised;
  • To make navigating the maze more robust, all data has to be used in stead of 10%;
  • However this raw data has to be modified in order to make it applicable;
  • The output of the modified raw data is very different from the LRF output, therefore most of the skills/tasks have to be rewritten;
  • To be able to compete with other groups, PICO needs to follow the apex.

Corridor Challenge

Maze challenge

The final competition on June 17th was the A-maze-ing challenge, or maze competition. The goal of this competition is to let PICO autonomously drive through a maze and find the exit. The maze contains doors and the exact exit location is unknown a priori. For more specifics on the maze challenge see [2].


For the maze competition several considerations were made about the final competition. This paragraph presents an overview of the most important decisions that were concerning the final competition.

Hough transformation
The group decided that the detection method used during the corridor challenge was inefficient and inaccurate; by only using bundles of the LRF-data, large amount of valuable data was omitted. The Houghline transformation provides a solution. This method uses all data to attempt to represent the complete walls. More about this function can be seen in the code overview.

Improved cornering strategy
Since there is limited time for the maze challenge the corner strategy was improved. Instead of driving to the middle of a junction and rotate while standing still, an improved cornering strategy is developed using set points and trajectory planning. The new cornering strategy follows a smooth arc, and rotation is performed during the cornering. For more information on the cornering strategy see Rotation at Code overview.

Topology mapping
For mapping the maze and being able to base our decision making strategy on this map, the group decided to develop a method for topology maping. This should be able to store the most important data of previous encountered states, or events, and provide a base for solving the maze. In combination with global localization coordinates it can be ensured a previous encountered event is recognized. More information on Toplogy mapping can be seen in decision making and mapping at Code overview.

Only LRF data
The group decided to only use LRF data for detection as well as mapping. Using odemetry data for global localization (for mapping) was considered but solutions using the LRF data were suggested by group members.

Code overview

Code structure
The code structure for the maze challenge should contain the structure described in the Software Design. The main loop should consist of a scheduler presented in the code overview, and the task-skill-motion overview and composition pattern should guide the group in designing the lay-out of the code. This brings structure to the code, and decoupling of code parts improves the ability to develop the code with multiple teams at the same time.

    while ( io.ok ){
          communicate();  	     	//Receive data
          detect_event();  	     	//Recognize events
          worldmodel();	                //Update the worldmodel
          decision_making();	     	//Decides which task needs to be executed
          exec_decision();	     	//Execute decision	
          orientation_tracking();       //Orientation of PICO relative to the start
          worldmodel();	                //Memorize taken decision
          prevent_collision();          //To prevent a collision, send the right velocity parameters to the robot 

Hough Transform

In order to interpret the data obtained from the LRF, the laser data is processed using a Hough transformation. This transforms the data points into lines, therefore the robot can interpret these lines as walls of the maze.

The Hough transform algorithm from the OpenCV library plots for a given point [math]\displaystyle{ (x_0,y_0) }[/math] the family of possible lines that goes through that point. Each line which goes through point [math]\displaystyle{ (x_0,y_0) }[/math] can be described as: [math]\displaystyle{ r_{\theta} = x_0 cos(\theta) + y_0 sin(\theta) }[/math]. This results in a plot with a sinusoid for each point, for three points, this will result in the figure below. Here, the sinusoids for three points are plotted. The three plots intersect in one single point, which means that one line is represented by three data points. When using the Hough transform function a threshold is defined, which indicates the minimum number of intersections needed to detect a line.

Hough transformation

We used the Probabilistic Hough transform function, which returns the extremes of the detected lines [math]\displaystyle{ (x_0,y_0,x_1,y_1) }[/math].

Unfortunately, the output of this algorithm describes one wall in the maze with multiple lines. This is a redundant and (for some other functions) unwanted phenomenon. With only altering the resolution of [math]\displaystyle{ r }[/math], the problem was not solved and the duplicate lines were still present. In order to make sure that all visible walls are always represented by a single line, a filtering algorithm is used.

Hough Lines Filter
As mentioned, the output of OpenCV often consists of multiple redundant lines. To easily distinguish which lines are similar (and possibly represent the same wall), the cartesian coordinates of the lines are transformed to polar coordinates. If the radius to a line and the angle to a line are similar, it is assumed that the lines represent the same wall. All similar lines but the longest are thrown away. So the output has the same number of lines as the amount of visible walls.

Unfiltered lines
Filtered lines

Detect Junction

Junction types

The filtered Hough lines are used to detect the different types of junctions, e.g. T-junction, dead end or right turn. vertices of the lines are used to distinguish the different junctions:

  • Corridor, No vertices.
  • Dead end, Two connected vertices and PICO is in-between the x-locations of the points.
  • Left turn, One vertex to the right of PICO, PICO is in between the x-locations of the horizontal line.
  • Right turn, One vertex to the left of PICO, PICO is in between the x-locations of the horizontal line.
  • T-junction left, One vertex to the left of PICO, both connected lines are to the left of PICO.
  • T-junction right, One vertex to the right of PICO, both connected lines are to the right of PICO.
  • T-junction, No vertices and PICO is able to detect a horizontal line in front.
  • Crossing, Two vertices, one to the right and one to the left of PICO that are not connected.

Decision making
When an event has been detected (detect_event) and the worldmodel has been updated, a decision is made by the decision_making function. If the current node has not been encountered before, a random choice is made between all possibilities. However, if the node has been encountered before, PICO is either on its way back to the last node with undiscovered paths or it has made a loop. PICO's current coordinates are compared to all nodes in the node list. If the coordinates match another node within a tolerance, the node type is compared to the current node type. Depending on the orientation of the current- and of the other node, it is decided whether they can be the same node or not.

If the nodes are the same and PICO is returning to the last node, a random decision is made between undiscovered paths. When no undiscovered paths are left, return in the correct direction. If the nodes are the same and PICO is not returning to the last node, the function will check for undiscovered paths in the loop form the node list. If there are still undiscovered paths in the loop, the loop will be entered again. Otherwise, the path is followed back until an undiscovered path is encountered.

Since the decision making function is part of the main loop, the function runs at the same frequency as main does. Therefore, a decision is only made right after an event has been detected. On other time instances, the function returns 0.

The function is described in pseudocode below.

int decision_making
    if ( coordinates match == true )
        if ( node types and orientation match == true )
            if ( returning to last node == true )
                 choose a random undiscovered path or return even further
                if ( possibilities within the loop  == true )
                     enter the loop again
                    return on the path that did have possibilities
        make a random decision out of the possibilities

Orientation tracking

This function tracks PICO's orientation relative to its starting position in perpendicular directions (forward, right, left, backward). It is used by the worldmodel function to store the orientation of the robot at arrival of a node.

Every time a new decision has been made and executed, the orientation stored by the software changes. The directions defined by the function are as seen in the figure below.

Orientation directions relative to the start


In order to solve the maze efficiently, a map of the maze is created in our worldmodel. The function 'worldmodel' runs twice in every iteration of the main loop. A topological map is created as

PICO detects junctions and the like. This map contains all nodes (corners, junctions, doors, dead ends and four-way crossings) that have been encountered and information on the connection between

these nodes. If a new event has been detected, the node is added to the topological map as can be seen in the figure below.

Example of a topological map

The topological map contains information on the approximate location of nodes, but does not store the exact surroundings of PICO throughout the maze. This way, storing and reading the data is much less complex compared to maps that store the entire surroundings of the robot. Several pieces of information are stored in every node and each node is added to a list that represents the map. The data structure of a node is explained in the code section below.

struct node {
	int ID; 		//ID of the current node
	int forward_possible;	//True if going straight at this node is possible and not taken yet
	int right_possible;	//True if going right at this node is possible and not taken yet
	int left_possible;	//True if going left at this node is possible and not taken yet
	int node_back;		//The ID of the node PICO encountered before this one
	int node_returned;	//THE ID of the node that is encountered again (when returning or looping
	int node_type;		//The node type is stored: corner/corridor/dead end etc.
	int orientation;	//PICO's orientation when it arrives at the node is stored
	float x_coor;		//X-coordinate of PICO relative to the start
	float y_coor;		//Y-coordinate of PICO relative to the start

Worldmodel will undertake actions whenever a new event has been detected. It adds a new node to the list and all information listed above is stored with it. After a decision has been made and

executed, worlmodel runs again and adjusts the newly added node so that the right possibilities are stored in it.

Unfortunately, due to unforeseen difficulties in event detection as well as the worldmodel itself, we were not able to implement this function into our code before the maze challenge.

The function rotation can be divided in three parts, namely: right rotation, left rotation and dead-end. Through the decision making algorithm a right-rotation, left-rotation or a dead-end is indicated. All the kind of junctions can be declared to one of these parts, because the robot eventually has to take a right turn, left turn or a 180 degrees turn. The right and left rotation will be explained separately from the dead-end.

The rotation function for a right-turn and a left-turn is identical, except for the initial conditions on which the set point trajectory is determined. Therefore the right rotation will be used as example to clarify the approach.
a. The turns are made by indicating the environment of the junction. First a vertical line on the right side and left side of PICO needs to be detected, in combination with a horizontal line at the right side in front of PICO (for a right turn).
b. By defining the width of the corridor and the width of the junction the radius rotation is set to the value of the smallest width of those two corridors divided by two.
c. The setpoint at the begin of the junction and the setpoint in the corridor can be defined with the radius. The distance to the first setpoint is used for calculating the velocity in x and y.
d. At the first setpoint PICO makes a 90 degrees turn. The radius is used for determination of the velocity of theta so setpoint 2 can be reached.

Rotation setpoint
Rotation setpoint movie

Dead-end and door detection
When a dead end is detected, PICO wants to check whether it is a door or not. PICO drives into the dead end and stops about 0.7 meters in front of it. After that, PICO starts to request to open the door. When the situation in the dead end does not change over 7 seconds, PICO makes a 180 degree turn whereafter it drives out of the dead-end and returns to the previous node. If in the 7 seconds a door opens in the dead end, PICO waits until the door is completely open and will then drive through the door. A simulation of PICO waiting for a door and then driving through it can be seen in the image below.



During the final maze challenge PICO was not able to autonomously finish the maze. This was in contradiction to the earlier made simulations, where PICO was able to drive through a maze. The fact that PICO did not solve the maze during the real challenge can be explained by the following points:

  • In the simulations, the maze dimensions where very large in comparison to the eventual maze. A corridor in the simulation maze was approximate 2m long whereby the distance between consecutive junctions is sufficient. The real maze was much smaller scaled, causing the junctions to be placed closer together. Because of this PICO did not always have enough time and distance to recognize the upcoming junction. Causing it to drive straight forward and ended up in the function collision prevention. If PICO, for example, rotates through a corner, he will not try to detect another event at the same time. If junctions consecutively appear, the robot cannot detect the second junction since he comes out of the rotating state when is is already in the next junction. The robot cannot make anything of its environment then, and just drives forward and eventually ends up into the collision prevention function.

Probabilistic Hough Transformation
The Hough transformation at first seemed an ideal function to create lines out of the LRF data. But while using it we bumped into some problems and we had to use a filter to delete the duplicate lines. This filter was not robust enough do be sure that the robot at all times sees the right lines and thus the right junctions. Afterwards it would be a good idea to consider create a function our self to create the line, so we had better influence on the output of the function.

Corridor Challenge Corridor Challenge

Course evaluation

Learning points

During the course of Embedded motion control our group learned a lot. At the beginning of the course we were asked to hand in a design document which described the requirements, specifications but also the functions, components and interfaces of our design. This forced the group to really think about what we wanted to achieve before starting head-on with programming. This structured approach really provided insight into tackling a design problem.

The groups were asked to give short presentations about progress and software design. This, together with the wiki-page, learned us that sharing information benefits the design process a lot. Receiving feedback from other groups and of course the lecturers was insightful. The feedback that all other groups received was useful as well. Having to present your work ( and especially your software design) during the design progress really helps you to have a structured approach to the design problem.

The heavy focus on software design during the lectures, provided insight into the importance of structuring your code and design. We learned that actually applying this knowledge as early as possible in the design process benefits the outcome a lot. Our first code design, used for the corridor challenge, was not that structured at all. As we improved the structure of the code, the team members got more and better ideas for solutions in the code.

Another important lesson for us was making decisions. During the project we switched our detection method from using bundles of laser data to using the Hough transformation. This decision had a major impact on our progress, since a lot of the coding had to be reworked. The implementation of the Hough transformation was quite a task and caused a lot of delay in the project. We learned that making decisions and evaluating them properly and timely is of major importance for the progress of your project.

Finally, we ofcourse learned alot of new skills and techniques. Programming in C++ was a new experience for some of us. The emc environment provided on Ubuntu gave us the tools to develop our program skills, and gave us hand on experience of embedding the code on an actual robot which was really awesome to experience. This also showed us that simulation provides a mere indication of your software performance. Testing your software embedded on the hardware is 'the real deal', and proved to be challenging. The oppertunity to experience this during the 'test-hours' was insightful and learned us alot.


During the first lecture of Embedded Motion Control, the participating students were asked to form groups. A nicely mixed group was formed: starting master graduates as well as students closer to graduation. Some finished a pre-master while others had followed the normal Mechanical Engineering Bachelor. However, the majority of the group was not very familiar with coding, let alone C++. Luckily everybody was eager to learn and major improvements were clearly noticeable.

The Bachelor Mechanical Engineering is focused, for a large part, on group assignments, Design Based Learning (DBL). Our group decided to use some techniques learned from DBL to keep the meetings structured, by appointing a dedicated board writer and minutes secretary every meeting. To keep the pace in the project Self Study Assignments were appointed, which were mainly performed in groups to stimulate creativity. All these SSA’s were specifically directed towards the next deadline, like the Corridor and Maze challenge, but also the intermediate presentations.

The group turned out to work very well together: no problems were encountered during the project. An interesting dynamic in this group was the fact that every member took a certain role during the project. For example, one of the group members was the structure specialist, maintaining the integrity of the entire code; there were some specialists for certain specific functions, but there also were all-rounders, helping others with debugging, function strategy or writing smaller parts of functions.


Hough lines filter

As mentioned, a filter is used to exclude redundant duplicate lines from the OpenCV hough transform library. The code snippet of the filter can be found here.[3].

Setpoints rotation

A code snippet of the piece of code that is used to create the parametric setpoint 1 used for the rotation of the robot can be found here.[4].