Mobile Robot Control 2024 Wall-E

Group members:

Caption

Name	student ID
Coen Smeets	1615947
Sven Balvers	1889044
Joris Janissen	1588087
Rens Leijtens	1859382
Tim de Wild	1606565
Niels Berkers	1552929

Exercise 1

Instructions

1. Create a folder called exercise1 in the mrc folder. Inside the exercise1 folder, create an src folder. Within the src folder, create a new file called dont_crash.cpp.
2. Add a CMakeLists.txt file to the exercise1 folder and set it up to compile the executable dont_crash.
3. Q1: Think of a method to make the robot drive forward but stop before it hits something. Document your designed method on the wiki.
4. Write code to implement your designed method. Test your code in the simulator. Once you are happy with the result, make sure to keep these files as we will need them later.
5. Make a screen capture of your experiment in simulation and upload it to your wiki page.

Implementations:

Coen Smeets:

This section has been written with the following techniques. Firstly, I describe in rudementary terms what should be in a paragraph and let Copilot convert it to Markdown, README.md file on git. This is then checked that it is still correct. Afterwards, I let copilot convert the Markdown to Mediawiki Markup for this page where I double check and manually change incorrect formatting. Is this method allowed within the course?

The provided C++ code is designed to prevent a robot from colliding with obstacles by using laser scan data and odometry data. Here's a step-by-step explanation of how it works:

1. Initialization: The code starts by initializing the emc::IO object for input/output operations and the emc::Rate object to control the loop rate. It also initializes some variables for later use.
2. Main Loop: The main part of the code is a loop that runs as long as the io.ok() function returns true. This function typically checks if the program should continue running, for example, if the user has requested to stop the program.
3. Reading Data: In each iteration of the loop, the code reads new laser scan data and odometry data. If there's new data available, it processes this data.
4. Processing Odometry Data: If there's new odometry data, the code calculates the linear velocity vx of the robot by dividing the change in position by the change in time. It also updates the timestamp of the last odometry data.
5. Collision Detection: If there's new laser scan data, the code calls the collision_detection function, which checks if a collision is likely based on the laser scan data. If a collision is likely and the robot is moving forward (vx > 0.001), the code sends a command to the robot to stop moving. It also prints a message saying "Possible collision avoided!".
6. Sleep: At the end of each iteration, the code sleeps for the remaining time to maintain the desired loop rate.

This code ensures that the robot stops moving when a collision is likely, thus preventing crashes.

Collision detection function

The collision_detection function is a crucial part of the collision avoidance code. It uses laser scan data to determine if a collision is likely. Here's a step-by-step explanation of how it works:

The collision_detection function is a crucial part of the collision avoidance code. It uses laser scan data to determine if a collision is likely. Here's a step-by-step explanation of how it works:

1. Function Parameters: The function takes four parameters: scan, angle_scan, distance_threshold, and verbose. scan is the laser scan data, angle_scan is the range of angles to consider for the collision detection (default is 1.7), distance_threshold is the distance threshold for the collision detection (default is 0.18), and verbose is a boolean that controls whether the minimum value found in the scan data is printed (default is false).
2. Zero Index Calculation: The function starts by calculating the index of the laser scan that corresponds to an angle of 0 degrees. This is done by subtracting the minimum angle from 0 and dividing by the angle increment.
3. Minimum Distance Calculation: The function then initializes a variable min_value to a large number. It loops over the laser scan data in the range of angles specified by angle_scan, and updates min_value to the minimum distance it finds.
4. Verbose Output: If the verbose parameter is true, the function prints the minimum value found in the scan data.
5. Collision Detection: Finally, the function checks if min_value is less than distance_threshold and within the valid range of distances (scan.range_min to scan.range_max). If it is, the function returns true, indicating that a collision is likely. Otherwise, it returns false.

This function is used in the main loop of the code to check if a collision is likely based on the current laser scan data. If a collision is likely, the code sends a command to the robot to stop moving, thus preventing a crash.

Potential improvements:

While the current collision detection system is effective, it is sensitive to outliers in the sensor data. This sensitivity can be problematic in real-world scenarios where sensor noise and random large outliers are common. Safety is about preparing for the worst-case scenario, and dealing with unreliable sensor data is a part of that. Here are a couple of potential improvements that could be made to the system:

1. Rolling Average: One way to mitigate the impact of outliers is to use a rolling average of the sensor data instead of the raw data. This would smooth out the data and reduce the impact of any single outlier. However, this approach would also introduce a delay in the system's response to changes in the sensor data, which could be problematic in fast-moving scenarios.
2. Simplifying Walls to Linear Lines: Given that the assignment specifies straight walls, another approach could be to simplify the walls to linear lines in the data processing. This would make the collision detection less sensitive to outliers in the sensor data. However, this approach assumes that the walls are perfectly straight and does not account for any irregularities or obstacles along the walls.

   "- All walls in the restaurant will be approximately straight. No weird curving walls." [1](https://cstwiki.wtb.tue.nl/wiki/Mobile_Robot_Control_2024)

   This process involves several steps that may not be immediately intuitive. Here's a breakdown:
   1. Data Cleaning: Remove outliers from the data. These are individual data points where the sensor readings are clearly incorrect for a given time instance.
   2. Data Segmentation: Divide the data into groups based on the angle of the sensor readings. A group split should occur where there is a significant jump in the distance reading. This helps differentiate between unconnected walls.
   3. Corner Detection: Apply the Split-and-Merge algorithm to each wall. This helps identify corners within walls, which may not be detected by simply looking for large jumps in distance.

Keep in mind that the complexity of the scene can significantly impact the computation time of this algorithm. However, given the scenes shown on the wiki, the additional computation time required to generate clean lines should be minimal. This is because the number of walls is relatively small, and the algorithm can be optimized in several ways. For instance, instead of processing each data point, you could select a subset of evenly spaced points. On average, these selected points should accurately represent the average location of the wall.

Based on my experience, prioritizing safety enhancements in the system is crucial. Establishing a strong safety foundation from the outset is key. By implementing robust safety measures early in the development process, we can effectively mitigate potential risks and issues down the line.