Stelian BRAD, Naomi Damaris DOLHA


Autonomous mobile robots are required in many applications, from industry to services, security, and defense. The capacity to navigate autonomously in a smooth way in environments with various obstacles is essential for adoption in the practice of these types of robotic systems. Smooth navigation and obstacle avoidance significantly depend on the right correlation between the obstacle avoidance algorithm and the robotic system design (type of sensors, sensor location, type of control, mechanical construction of the mobile platform). This paper investigates the possibility to design the obstacle avoidance algorithm from the perspective of the particular design of the robot. Early validation of the algorithm is a cost-effective approach. In this respect, this paper also introduces an architectural construct of various open-source technologies to test and validate the algorithm via a digital prototype (or digital twin) that embeds the physical properties of the real robot and of the obstacles within the simulation environment. Tests run in the virtual environment show that the proposed algorithm, embedded in a wider Simultaneous Localization and Mapping (SLAM) algorithm, is capable to ensure a smooth avoidance of obstacles. Results can be easily implemented in a physical mobile robot for intelligent autonomous navigation.

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Akiyoshi, K., Chugo, D., Muramatsu, S., Yokota, S., Hashimoto, H., Autonomous mobile robot navigation considering the pedestrian flow intersections, 2020 IEEE/SICE International Symposium on System Integration (SII), pp. 428-433, doi: 10.1109/SII46433.2020.9026277, Honolulu, USA, (2020).

Alatise, M.B, Hancke, G.P., A review on challenges of autonomous mobile robot and sensor fusion methods, IEEE Access, Vol. 8, pp. 39830-39846, (2020).

Iqbal, J., Xu, R., Sun, S., Li, C., Simulation of an autonomous mobile robot for LiDAR-based in-field phenotyping and navigation, Robotics, Vol. 9, No. 46, pp. 1-19, (2020).

Shen, M., Wang, Y., Jiang, Y., Ji, H., Wang, B., Huang, Z., A new positioning method based on multiple ultrasonic sensors for autonomous mobile robot, Sensors, Vol. 20, No. 17, pp. 1-15, (2020).

Mohanty, P.K., Parhi, D.R., Controlling the motion of an autonomous mobile robot using various techniques: a review, Journal of Advanced Mechanical Engineering, Vol. 1, pp. 24-39, (2013).

Du, Y.C., Ai, C.S., Feng, Z.Q., Research on navigation system of AMR based on ROS, 2020 IEEE International Conference on Real-Time Computing and Robotics, pp. 117-121, Shandong, China, (2020).

Elkilany, B.G., Abouelsoud, A.A., Fathelbab, A.M.R., Ishii, H., A proposed decentralized formation control algorithm for robot swarm based on an optimized potential field method, Neural Computing & Applications, Vol. 33, No. 1, pp. 487-499, (2020).

Brad, S., Complex system design technique, International Journal of Production Research, Vol. 46, No. 21, pp. 5979-6008, (2008).

Altshuller, G., The Innovation Algorithm. TRIZ. Technical Innovation Center, Worcester, USA, (2000).

Brad, S., Structured activation of vertex entropy (SAVE): another way around creative problem solving for non-technical applications, Innovator Journal of the European TRIZ Association Vol. 1, pp. 76-81, (2017).

Bailey, M., Gebis, K., Žefran, M., Simulation of Closed Kinematic Chains in Realistic Environments Using Gazebo. Springer, Chicago, USA, (2016).

Arruda, M., Exploring ROS with a 2 wheeled robot #5: obstacle avoidance. Retrieved from The Construct: /exploring-ros-2-wheeled-robot-part-5/, (2019).


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