Why when walking in the city we do not enter blind alleys, or at least it happens to us very rarely, rather only when we lose our train of thought? Thanks to our perception (i.e., seeing appropriate signs and assessing distances) we are able to predict that there is an obstacle in front of us - we do not have to check it at all while walking to the end of the street. We are able to move onto the right track at the right moment, without having to waste time turning back.
- This is exactly the behavior we based on when developing the path planning algorithm that allows mobile robots to avoid dead ends – explains dr habil. Tomasz Tarczewski, NCU Prof. from the Institute of Engineering and Technology.
In addition to Prof. Tomasz Tarczewski, the team working on the algorithm includes mgr inz. Rafal Szczepanski, and inz. Artur Bereit, from the Institute of Engineering and Technology (a part of the Faculty of Physics, Astronomy and Informatics at the Nicolaus Copernicus University). They received a grant for their research in the INCOOP competition as part of the Excellence Initiative - Research University. The results have been published in the prestigious journal " Energies" in the article "Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality ".
Why did they take an interest and decide to solve this particular problem?
One way to solve this problem is to implement local path planning algorithms to ease the avoidance of various types of obstacles. Thanks to them, it is possible for the robot to autonomously unblock itself and continue its route.
- The principle of operation of most of these mechanisms involves the introduction of an additional algorithm leading the robot out of a dead end. In such an approach, the distance travelled by the mobile robot increases due to the necessity of retreating and causes unnecessary power consumption - explains Rafal Szczepanski. - For this purpose, we have applied a LiDAR sensor, i.e., a laser scanner providing information on distances from obstacles around the robot.
Then, based on the readings, the team of researchers at the Institute of Engineering and Technology developed a dead-end prediction mechanism: the robot compares real-time information about the available space with its own dimensions and makes a decision about passability based on that information. In order for the robot to give up exploring the impassable section, it must be provided with a mechanism to bypass it.
For this task, the researchers used augmented reality technology (AR for short).
Precisely how do you program the dead-end entry blocking mechanism into the robot?
The team of researchers from our university implemented the developed algorithm of the potential fields method supported by augmented reality technology in the mobile robot Husarion ROSbot 2.0 PRO and conducted a series of laboratory tests to demonstrate the effectiveness of the proposed solution. The results are very promising - the researchers demonstrated a significant improvement in the properties of the potential fields algorithm commonly used in mobile robotics.
Energies
Experimental study
Not applicable
Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality
14-Sep-2021