A Fast Algorithm of SLAM Based on Combinatorial Interval Filters

Luo, Jingwen; Qin, Shiyin

doi:10.1109/access.2018.2838112

Cited by 11 publications

(4 citation statements)

References 42 publications

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“…However, with these platforms the instructor can not introduce more advanced multi-robots experiments like multi-agents systems (cooperation and collaboration of a group of robots to solve complex problems) [10]. Therefore, some of the following interesting experiments could not be implemented: simultaneous localization and mapping [11], navigation with computer vision, formation control [12], event-based control [13], among others.…”

Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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The use of mobile robots for teaching automatic control is becoming more popular in engineering curricula. Currently, many robot simulators with high-graphical capabilities can be easily used by instructors to teach control engineering. However, the use of real robots is not as straightforward as simulations. There are many hardware and software details that must be considered before applying control. This paper presents the development of an easy-to-use platform for teaching control of mobile robots. The laboratory has been carefully designed to conceal all technical issues, such as communications or the localization that do not address the fundamental concepts of control engineering. To this end, a position sensor based on computer vision has been developed to provide the positions of the robots on the platform in real time. The Khepera IV robot has been selected for this platform because of its flexibility and advanced built-in sensors but the laboratory could be easily adapted for similar robots. The platform offers the opportunity to perform laboratory practices to test many different control strategies within a real experimental multi-agent environment. A methodology for using the platform in the lab is also provided.INDEX TERMS Robotics education, mobile robot laboratory, vision-based indoor positioning sensor.

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Section: Introductionmentioning

confidence: 99%

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

et al. 2019

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“…A prediction stage is a process in which future readings are predicted to obtain state and covariance values ranging from 𝑘 − 1 to 𝑘 [92]. Two equations at the Prediction Stage were State Prediction and Error Covariance Prediction [93,94].…”

Section: -1-prediction Stage (Time Update)mentioning

confidence: 99%

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

et al. 2022

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COVID-19 is a very dangerous respiratory disease that can spread quickly through the air. Doctors, nurses, and medical personnel need protective clothing and are very careful in treating COVID-19 patients to avoid getting infected with the COVID-19 virus. Hence, a medical telepresence robot, which resembles a humanoid robot, is necessary to treat COVID-19 patients. The proposed self-balancing COVID-19 medical telepresence robot is a medical robot that handles COVID-19 patients, which resembles a stand-alone humanoid soccer robot with two wheels that can maneuver freely in hospital hallways. The proposed robot design has some control problems; it requires steady body positioning and is subjected to disturbance. A control method that functions to find the stability value such that the system response can reach the set-point is required to control the robot's stability and repel disturbances; this is known as disturbance rejection control. This study aimed to control the robot using a combination of Proportional-Integral-Derivative (PID) control and a Kalman filter. Mathematical equations were required to obtain a model of the robot's characteristics. The state-space model was derived from the self-balancing robot's mathematical equation. Since a PID control technique was used to keep the robot balanced, this state-space model was converted into a transfer function model. The second Ziegler-Nichols's rule oscillation method was used to tune the PID parameters. The values of the amplifier constants obtained were Kp=31.002, Ki=5.167, and Kd=125.992128. The robot was designed to be able to maintain its balance for more than one hour by using constant tuning, even when an external disturbance is applied to it. Doi: 10.28991/esj-2021-SP1-016 Full Text: PDF

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“…FastSLAM performs feature update with EKF. For each landmark, 2x2 EKFs are created and predictions are made [35]. According to the estimation in (22), feature estimation is calculated as follows.…”

Section: Feature Updatementioning

confidence: 99%

An improved adaptive FastSLAM algorithm with time‐varying noise estimator

Karaçam

Navruz

2022

Asian Journal of Control

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In this study, an adaptive FastSLAM (AFastSLAM) algorithm, which is obtained by estimating the time-varying noise statistics and improving FastSLAM algorithm, is proposed. This improvement was accomplished by using maximum likelihood estimation and expectation maximization criterion and a one-step smoothing algorithm in importance sampling. In addition, innovation covariance estimation (ICE) method was used to prevent loss of positive definiteness of the process and measurement noise covariance matrices. The proposed method was compared with FastSLAM by calculating the root mean square error (RMSE) using different particle numbers at varying initial process and measurement noise values. Simulation studies have shown that AFastSLAM provides much more accurate, consistent, and successful estimates than FastSLAM for both robot and landmark positions.

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A Fast Algorithm of SLAM Based on Combinatorial Interval Filters

Cited by 11 publications

References 42 publications

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Development of an Easy-to-Use Multi-Agent Platform for Teaching Mobile Robotics

Using a Combination of PID Control and Kalman Filter to Design of IoT-based Telepresence Self-balancing Robots during COVID-19 Pandemic

An improved adaptive FastSLAM algorithm with time‐varying noise estimator

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