Self-adaptive Monte Carlo localization for mobile robots using range finders

Zhang, Lei; Zapata, René; Lépinay, P.

doi:10.1017/s0263574711000567

Cited by 73 publications

(39 citation statements)

References 48 publications

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“…The current observation is also included in the proposal distribution in [22]. Zhang et al [23] have developed the Self-Adaptive MC localization method. They have defined the concept of similar energy region to distribute more efficiently the samples.…”

Section: Related Workmentioning

confidence: 99%

Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

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A key challenge for an autonomous mobile robot is to estimate its location according to the avail able information. A particular aspect of this task is the global localization problem. In our previous work, we developed an algorithm based on the Differential Evo lution method that solves this problem in 2D and 3D environments. The robot's pose is represented by a set of possible location estimates weighted by a fitness function. The Markov Chain Monte Carlo algorithms have been successfully applied to multiple fields such as econometrics or computing science. It has been demonstrated that they can be combined with the Dif ferential Evolution method to solve efficiently many optimization problems. In this work, we have com bined both approaches to develop a global localization filter. The algorithm performance has been tested in simulated and real maps. The population requirements have been reduced when compared to the previous version.

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Section: Related Workmentioning

confidence: 99%

Differential Evolution Markov Chain Filter for Global Localization

Moreno

Alonso-Martín

Muñoz

et al. 2015

J Intell Robot Syst

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“…Today, the most efficient robot system is not able to have the perfect behavior 50 necessary to execute all robotic missions that people could imagine. A fault may generate relatively dramatic effects such as a significant decrease in the robot's performance, an aborted mission, loss or crash of the robot, or human injuries.…”

Section: Motivationmentioning

confidence: 99%

Enhancing fault tolerance of autonomous mobile robots

Crestani

Godary-Dejean

Lapierre

2015

Robotics and Autonomous Systems

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Experience demonstrates that autonomous mobile robots running in the field in a dynamic environment often breakdown. Generally, mobile robots are not designed to efficiently manage faulty or unforeseen situations. Even if some research studies exist, there is a lack of a global approach that really integrates dependability and particularly fault tolerance into the mobile robot design. This paper presents an approach that aims to integrate fault tolerance principles into the design of a robot real-time control architecture. A failure mode analysis is firstly conducted to identify and characterize the most relevant faults.Then the fault detection and diagnosis mechanisms are explained. Fault detection is based on dedicated software components scanning faulty behaviors.Diagnosis is based on the residual principle and signature analysis to identify faulty software or hardware components and faulty behaviors. Finally, the recovery mechanism, based on the modality principle, proposes to adapt the robot's control loop according to the context and current operational functions of the robot.This approach has been applied and implemented in the control architecture of a Pioneer-P3DX mobile robot.

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“…In general, several techniques proposed to overcome the effect of process noise on SLAM performance could be grouped into three: (a) Splitting the area into sub-maps as well as re-observing the sub-map more than once (L. Paz and Neira, 2006), (Blanco et al, 2007), (Huang et al, 2006) and (Castellanos et al, 2007); (b) an appearance-based approach which tries to avoid the use of odometer data in estimating the robot position (Seadan et al, 2007), (L. M. Paz et al, 2008), (Davison and Murray, 2002), (Porta and Krose, 2006) and (Koenig et al, 2008); (c) engaging an adaptive strategy as a means of controlling the motion of the robot (Cho et al, 2002), (Zhang et al, 2012), (Harter, 2005) and (Härter and Campos Velho, 2008). It appears that none of these approaches has thoroughly investigated the manner in which the process noise affects predictions and estimations in SLAM with the view of addressing it.…”

Section: Slam Have Been Found Helpful In Several Application Domainsmentioning

confidence: 99%

Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

Ihemadu

Naeem

Ferguson

2014

Transactions of the Institute of Measurement and Control

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Abstract.A technique for optimising the efficiency of sub-map method for large-scale simultaneous localisation and mapping (SLAM) is proposed. It optimises the benefits of the sub-map technique to improve the accuracy and consistency of an Extended Kalman Filter (EKF) based SLAM. Error models were developed and engaged to investigate some of the outstanding issues in employing sub-map technique in SLAM. Such issues include: the size (distance) of an optimal sub-map, the acceptable error effect caused by the process noise covariance on the predictions and estimations made within a sub-map, when to terminate an existing sub-map and start a new one and the magnitude of the process noise covariance that could produce such an effect. Numerical results obtained from the study and an error correcting process was engaged to optimise the accuracy and convergence of the Invariant Information Local Sub-map Filter previously proposed. Applying this technique to the EKF-based SLAM algorithm: (a) reduces the computational burden of maintaining the global map estimates and (b) simplifies transformation complexities and data association ambiguities usually experienced in fusing sub-maps together. A Monte-Carlo analysis of the system is presented as a means of demonstrating the consistency and efficacy of the proposed technique.

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Self-adaptive Monte Carlo localization for mobile robots using range finders

Cited by 73 publications

References 48 publications

Differential Evolution Markov Chain Filter for Global Localization

Differential Evolution Markov Chain Filter for Global Localization

Enhancing fault tolerance of autonomous mobile robots

Optimizing the efficiency of the sub-map technique for large-scale simultaneous localization and mapping

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