FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots

• Type: Article in International Conference Proceedings Book
• Date: 2022

Authors

Sarmento, J (Author) Other; Filipe Neves Santos (Author) FEUP; Aguiar, AS (Author) Other; Sobreira, H (Author) Other; Regueiro, CV (Author) Other; Valente, A (Author) Other

Conference proceedings International

Title: IEEE International Conference on Autonomous Robot Systems and Competitions, ICARSC 2022, Santa Maria da Feira, Portugal, April 29-30, 2022

Pages: 179-185

Indexing

ISI Web of Knowledge - 6 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00W-KW5

DOI: 10.1109/icarsc55462.2022.9784791

Abstract (EN): In Industry 4.0 and Agriculture 4.0, there are logistics areas where robots can play an important role, for example by following a person at a certain distance. These robots can transport heavy tools or simply help collect certain items, such as harvested fruits. The use of Ultra Wide Band (UWB) transceivers as range sensors is becoming very common in the field of robotics, i.e. for localising goods and machines. Since UWB technology has very accurate time resolution, it is advantageous for techniques such as Time Of Arrival (TOA), which can estimate distance by measuring the time between message frames. In this work, UWB transceivers are used as range sensors to track pedestrians/operators. In this work we propose the use of two algorithms for relative localization, between a person and robot. Both algorithms use a similar 2dimensional occupancy grid, but differ in filtering. The first is based on a Extended Kalman Filter (EKF) that fuses the range sensor with odometry. The second is based on an Histogram Filter that calculates the pedestrian position by discretizing the state space in well-defined regions. Finally, a controller is implemented to autonomously command the robot. Both approaches are tested and compared on a real differential drive robot. Both proposed solutions are able to follow a pedestrian at speeds of 0.1m/s, and are promising solutions to complement other solutions based on cameras and LiDAR.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 7