Odometry during object transport: A Study with Swarm of Physical Robots

Muhanad Hayder Mohammed Alkilabi, Timoteo Carletti, Elio Tuci

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Object transport by a single robot or by a swarm of robots can be considered a very challenging scenario for odometry since wheel slippage caused by pushing forces exerted on static objects and/or by relatively frequent collisions with other robots (for the cooperative transport case) tend to undermine the precision of the position and orientation estimates. This paper describes two sets of experiments aimed at evaluating the effectiveness of different sensory apparatuses in order to support odometry in autonomous robots engaged in object transport scenarios. In the first set of experiments, a single robot has to track its position while randomly moving in a flat arena with and without an object physically attached to its chassis. In the second set of experiments, a member of a swarm of physical robots is required to track its position while collaborating with the group-mates to the collective transport of a heavy object. In both sets, odometry is performed with either wheel encoders or with an optic-flow sensor. In the second set of experiments, both methods are evaluated with and without gyroscope corrections for angular displacements. The results indicate that odometry based on optic-flow sensors is more precise than the classic odometry based on wheel encoders. In particular, this research suggests that by using an appropriate sensory apparatus (i.e., an optic-flow sensor with gyroscope corrections), odometry can be achieved even in extreme odometry conditions such as those of cooperative object transport scenarios.
Original languageEnglish
Title of host publicationAdvances in Swarm Intelligence - 12th International Conference, ICSI 2021, Proceedings
EditorsYing Tan, Yuhui Shi
Number of pages10
ISBN (Print)9783030788100
Publication statusPublished - 2021
EventThe 12th Internatioanl Conference on Swarm Intelligence - Qingdao, China
Duration: 17 Jul 202121 Jul 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12690 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Scientific committee

Scientific committeeThe 12th Internatioanl Conference on Swarm Intelligence
Abbreviated titleICSI'2021


  • Cooperative transport
  • Odometry
  • Optic-flow sensor
  • Swarm robotics


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