### Utku Olcar

#### is open to work

##### Job titles

Robotics Engineer · Robotics Software Engineer · Robotics Specialist · Mechatronics Engineer · Embedded Software Engineer

##### Start date

Immediately, I’m actively applying

##### Job types

Full-time (I can work remotely)

### Details

I am going to explain my work about programming a navigation code inspired by the Bug0 Algorithm for the Turtlebot3 robot in the GAZEBO Simulation platform. I have programmed this algorithm in Robotic Operating System using Python programming language.

Robust control algorithms were not used in this work. This algorithm is programmed with position control only. The main idea is to use ROS development tools and systems like Action Server, Launch Files, rViz Visualization, TF Transformations, Gazebo Simulation, etc.

# What is the Bug0 Algorithm?

The Bug0 Algorithm is an autonomous navigation algorithm that uses only the sensor's data without any map. The algorithm has basically two different behaviors;

• Behaviour 1: Head to goal straight
• Behaviour 2: Follow the obstacle boundary The algorithm tells that the robot implements the "Behavior 1" and goes towards the goal position until an obstacle detected in front. If there is an obstacle, the robot changes behavior to "Behavior 2" and follows the obstacle boundary. When there is no obstacle between the robot and the obstacle, the robot executes the "Behavior 1" again. So, this loop continues until the robot reaches the goal position.

## Behavior 1 (Head to Goal)  Angular Velocity Calculation:

HeadingAngleToGoal = \arctan { \frac {GoalPositionY { \atop robot }} {GoalPositionX { \atop robot }} }

Linear Velocity Calculation:

DistanceToGoal = \sqrt{\smash[b]{({GoalPositionY_{robot}})^2 + ({GoalPositionX_{robot}})^2 }}
LinearVelocity = f(DistanceToGoal) = \begin{cases} {DistanceToGoal * k_1} &\text{if } | {DistanceToGoal * k_1}|< MaxVelocity \\ MaxVelocity &\text{if } | {DistanceToGoal * k_1}| >=MaxVelocity \\ { } \end{cases}
• The robot is not moving until the heading of the robot is close enough towards the goal position:
LinearVelocity = f(HeadingAngleToGoal) = \begin{cases} {LinearVelocity } &\text{if } | {HeadingAngleToGoal}|< AngleLimit \\ {0} &\text{if } | {HeadingAngleToGoal}| >=AngleLimit \\ { } \end{cases}

## Behavior 2 (Follow Obstacle Boundary) • Following Obstacle Boundary:

Angular Velocity Calculation:

AngularVelocity = (FrontLeft -BackLeft) * k_2
DistanceStabilizer = (FollowDistanceConstant - FrontLeft ) * k_3
AngularVelocity = (AngularVelocity + DistanceStabilizer )
AngularVelocity = \begin{cases} {AngularVelocity } &\text{if } | {AngularVelocity}|< MaxAngularVelocity \\ MaxAngularVelocity &\text{if } | {AngularVelocity}| >=MaxAngularVelocity \\ { } \end{cases}

Linear Velocity Calculation: The robot's linear velocity is proportional to the robot's parallelism to the obstacle.

AngleError = |(FrontLeft - BackLeft)| * k_4
LinearVelocity = \begin{cases} { MaxLinearVelocity - AngleError } &\text{if } {AngleError} < {MinLinearVelocity} \\ MinLinearVelocity &\text{if } {AngleError} >= {MinLinearVelocity} \\ { } \end{cases}

## Analysis of Lidar Sensor Data ### Related Posts

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