Design of an Autonomous Mobile Robot based on the Sensor Data Fusion of Lidar 360, Ultrasonic sensor and Wheel Speed Encoder

Premnath, S.P.; Mukund, Smruthi; Sivasankaran, K.; Sidaarth, R; Adarsh, S.

doi:10.1109/icacc48162.2019.8986199

Cited by 15 publications

(2 citation statements)

References 11 publications

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“…The researchers praised the logic algorithm Fuzziness in making decisions to know the human environment and avoid obstacles in the led robots. Future vision Development of an algorithm based on fuzzy logic using behaviors for a mobile robot [15]. In 2022, the researchers Lukas Lachmayer, Tobias Recker and Annika Raatz used a mobile robot in industry by installing a robotic arm and using an optical sensor to measure distance and its effects on the kinematics of a mobile and stationary robot.…”

Section: Introductionmentioning

confidence: 99%

Electronic steering of a mobile robot for wall following and obstacle avoidance

Abdulghani

Hussein

Mohammed

2023

J. Phys.: Conf. Ser.

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Robots, including mobile robots, provide many services in military, industrial and space applications. They shorten time, reduce expenses, and reduce danger to humans in places that may pose a threat to human life. Although the use of mobile robots in civil applications is few, it is in an increasing growth. Such as using it to transport goods in small neighbourhoods or to clean streets. Most of the algorithms in robot navigation are wall or path tracking through conventional sensors directly or through optical or physical sensors. Mobile robots are of different types, and these types depend on the number of wheels and the way they are distributed and directed to add to the robot the number of degrees of freedom. One of the problems facing the mobile robot is the multitasking of self-driving, such as tracking a path and discovering and avoiding an obstacle. Multi-tasking caused confusion for the mobile robot, as its processing of one task in tracking the wall makes it deal with the distance from the wall as a reference point to keep it in the alignment position regardless of the speed of its wheels or the angle of deviation of the wall from it. This process requires the provision of many additional data to enable it to perform the task. This research presents the design and construction of a mobile robot that tracks the wall, analyses the obstacles, and then takes action. The work is to build a mathematical model and calculate the mathematical effects on all the available information from the sensors measuring distance and freedom, speed and real time information. The idea entered on separating the robot programmatically into two parts. The first part is a mobile robot that has electronic guidance based on speed sensors connected to the front wheels, which are included as part of a closed control system to control the speed of the wheels accurately. At the end, this robot can walk straight for a distance that can be determined and implement deviations at a specific angle with the given value, as well as it can walk in the form of a square, triangle, and circular as well as it can walk on an inclined road up or down without decreasing or increasing its speed. This method makes the mobile robot more flexible. As for the second part, it is measuring the angle of deflection of the wall and exploring obstacles by measuring the distances surrounding the robot at an angle of 90 degrees. The deflection angle of the wall is calculated using an algorithm that depends on calculating the distance between two points within the path of the robot. These two points have information about the measured distance between the wall and the robot, the speed of the robot, and time. Through this information, the deflection angle of the wall is calculated relative to the mobile robot. The wheel speed control algorithms and the electronic steering algorithm were implemented on a processor connected to the wheel marketer and considered the first part, then algorithms were implemented to calculate the wall deviation measurement and analyse the obstacles and considered the second part. Information was exchanged between the two processors to carry out all the tasks, as the first processor provides information about the speed of the wheels and time, and through which the second processor can calculate all the distances that it wants to calculate as virtual points. The most important conclusions that emerged through practical experience are the effect of the distribution of sensors on the robot’s structure and the presence of a time difference in the wheels’ transition from one speed to another and even from a static state to a moving state, despite its being proven around the reference point, and this required solving it programmatically in equalizing this timing to ensure that the robot remains in Forward direction when changing its speed. The project has proven high accuracy with high information management.

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Section: Introductionmentioning

confidence: 99%

Electronic steering of a mobile robot for wall following and obstacle avoidance

Abdulghani

Hussein

Mohammed

2023

J. Phys.: Conf. Ser.

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“…Rui Wang et al [12] developed multi-sensor ultrasonic to become more accurate. To optimize the environment reading, Denysyuk et al [13], Y. Phang, W. et al [14], M. Takahashi et al [16], and S. Premnath et al [17] used LIDAR. But this method increases the cost of robot development.…”

Section: Introductionmentioning

confidence: 99%

Position correction of mobile robot by detecting line landmark using Hough line transform

Anggoro,

Setiawan,

Nurhayati

2023

J. Phys.: Conf. Ser.

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Mobile robots are used in a laboratory automation system to transport specimens or experiments to their destinations. When moving, the robot has some error positions because of the wheels skid on the floor and the inaccuracy direction of the robot. The robot needs to correct its position and return to the track. By reading the landmarks in the environment, the robot can correct its position. This research proposes to do the position correction of the robot using a camera by reading the orange lines as an artificial landmark on the laboratory environment floor. The camera detects the orange lines as two parallel edges. And by Hough line transform (HLT) it produces Δρ value between line edges. The robot calculates the error position using the Δρ value and the biggest ρ (ρMAX) as the distance prediction robot to the line. Compared to the original robot movement on the Webots simulator application, this method can help the robot keep its position on the track. On the goal position, the robot can reduce 90% the error position. This error improvement makes the error position of the robot becomes around 5 mm from the target position.

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LiDAR-Based Obstacle Avoidance With Autonomous Vehicles: A Comprehensive Review

Leong,

Ahmad

2024

IEEE Access

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Design of an Autonomous Mobile Robot based on the Sensor Data Fusion of Lidar 360, Ultrasonic sensor and Wheel Speed Encoder

Cited by 15 publications

References 11 publications

Electronic steering of a mobile robot for wall following and obstacle avoidance

Electronic steering of a mobile robot for wall following and obstacle avoidance

Position correction of mobile robot by detecting line landmark using Hough line transform

LiDAR-Based Obstacle Avoidance With Autonomous Vehicles: A Comprehensive Review

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