Low-cost ultrasonic based object detection and collision avoidance method for autonomous robots

• Published in: International journal of information technology (Singapore. Online) Vol. 13; no. 1; pp. 97 - 107
• Main Authors: Yasin, Jawad N., Mohamed, Sherif A. S., Haghbayan, Mohammad-Hashem, Heikkonen, Jukka, Tenhunen, Hannu, Plosila, Juha
• Format: Journal Article
• Language: English
• Published: Singapore Springer Singapore 2021
• Subjects: Artificial Intelligence; Computer Imaging; Computer Science; Image Processing and Computer Vision; Machine Learning; Original Research; Pattern Recognition and Graphics; Software Engineering; Vision; Fault tolerance; Unmanned vehicles; Mobile robots; Collision avoidance; Ultrasonic
• ISSN: 2511-2104; 2511-2112
• DOI: 10.1007/s41870-020-00513-w

This work focuses on the development of an effective collision avoidance algorithm that detects and avoids obstacles autonomously in the vicinity of a potential collision by using a single ultrasonic sensor and controlling the movement of the vehicle. The objectives are to minimise the deviation from the vehicle’s original path and also the development of an algorithm utilising one of the cheapest sensors available for very lost cost systems. For instance, in a scenario where the main ranging sensor malfunctions, a backup low cost sensor is required for safe navigation of the vehicle while keeping the deviation to a minimum. The developed algorithm utilises only one ultrasonic sensor and approximates the front shape of the detected object by sweeping the sensor mounted on top of the unmanned vehicle. In this proposed approach, the sensor is rotated for shape approximation and edge detection instead of moving the robot around the encountered obstacle. It has been tested in various indoor situations using different shapes of objects, stationary objects, moving objects, and soft or irregularly shaped objects. The results show that the algorithm provides satisfactory outcomes by entirely avoiding obstacles and rerouting the vehicle with a minimal deviation.