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

Muhanad Hayder Mohammed Alkilabi, Timoteo Carletti, Elio Tuci

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Résumé

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.
langue originaleAnglais
titreAdvances in Swarm Intelligence - 12th International Conference, ICSI 2021, Proceedings
rédacteurs en chefYing Tan, Yuhui Shi
EditeurSpringer
Pages92-101
Nombre de pages10
ISBN (imprimé)9783030788100
Les DOIs
Etat de la publicationPublié - 2021
EvénementThe 12th Internatioanl Conference on Swarm Intelligence - Qingdao, Chine
Durée: 17 juil. 202121 juil. 2021

Série de publications

NomLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12690 LNCS
ISSN (imprimé)0302-9743
ISSN (Electronique)1611-3349

Comité scientifique

Comité scientifiqueThe 12th Internatioanl Conference on Swarm Intelligence
Titre abrégéICSI'2021
Pays/TerritoireChine
La villeQingdao
période17/07/2121/07/21

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