Paper Info
Title
Mobile robots cooperation with biased exteroceptive measurements
Abstract
When mobile robots need to cooperate, mutual localization is a key issue. The objective is to enable cooperative localization capabilities, such that each robot determines the partners positions in a common frame with reliable confidence estimates. Exteroceptive sensors can measure distances to known beacons in order to provide absolute information. It often exists biases that affect these measurements because of particular environment conditions or because of an inaccurate knowledge of the beacons positions. In this work, each robot is also equipped with proprioceptive sensors, but no sensor can measure the inter-distance between the robots. The method that we consider is fully distributed between the robots, which share positions and biases estimates. In order to handle the data incest problem, we use constraint propagation techniques on intervals. The distributed cooperative localization method gives sets that always contain the true positions of the robots without any over-convergence. Simulation results show that the so-called method improves localization performance compared to standalone methods.
Year
DOI
Venue
2014
10.1109/ICARCV.2014.7064595
Control Automation Robotics & Vision
Keywords
Field
DocType
constraint handling,mobile robots,path planning,sensors,biased exteroceptive measurements,constraint propagation technique,cooperative localization,data incest problem,distributed cooperative localization method,exteroceptive sensors,mobile robot localization,mobile robots cooperation,mutual localization,proprioceptive sensors
Beacon,Computer vision,Robot control,Local consistency,Computer science,Robot kinematics,Artificial intelligence,Robot,Mobile robot
Conference
ISSN
Citations 
PageRank 
2474-2953
2
0.44
References 
Authors
8
4
Name
Order
Citations
PageRank
Khaoula Lassoued120.44
Oana Stanoi220.44
Philippe Bonnifait345255.82
Isabelle Fantoni420.44