Intelligent Range-Only Mapping and Navigation for Mobile Robots

Miah, Suruz; Knoll, Jacob; Hevrdejs, Kyle

doi:10.1109/tii.2017.2780247

Cited by 21 publications

(4 citation statements)

References 30 publications

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“…We then implement a local Extended Kalman filter simultaneous localization and mapping (EKF-SLAM) algorithm onboard each robot. Therefore, each robot implements a simulation localization and mapping (SLAM) algorithm to locally estimate the network for determining the Voronoi partition at each time instant k. More details on EKF-SLAM algorithm can be sought in (Miah, Knoll, and Hevrdejs 2018). The EKF-SLAM algorithm is locally implemented onboard each robot deliberately taking into account the real scenario where the sensory measurements are noisy in nature.…”

Section: Area Coverage and State Estimationmentioning

confidence: 99%

Area Coverage Optimization using Networked Mobile Robots with State Estimation

Chism,

Plante,

Miah

2023

FLAIRS

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In this paper, we present a solution to the area coverage problem using a team of mobile robots with state estimation. A group of autonomous mobile robots is deployed in a two-dimensional area of interest, for example, where communication among robots is limited or noisy as expected in a real life scenario. Each robot estimates its own state (position and orientation) using noisy range and bearing information received from other robots in its operating range. The area of interest is then divided into multiple sub-area using a voronoi tessellation. Using the classical Lloyd's algorithm, each robot employs distributed action command to move towards the centroid of the voronoi cell that it belongs yielding the maximum coverage of the area. Here we emphasize that the network of autonomous robots deployed in the environment is unknown a priori. A set of computer experiments is conducted to validate the fact that the area coverage is still possible under noisy communication among robots deployed in a two-dimensional area.

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Section: Area Coverage and State Estimationmentioning

confidence: 99%

Area Coverage Optimization using Networked Mobile Robots with State Estimation

Chism,

Plante,

Miah

2023

FLAIRS

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“…The computed position is saved as a relative position. Robots' movement toward the captured data and it will be compared with already saved mapped data for localization [16,17]. In case of its matching with the data in its localization position, the orientation of the robot position automatically gets data from the global position.…”

Section: Figure 8 Mapping With the Ir Range Findermentioning

confidence: 99%

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Karupusamy¹,

Maruthachalam²,

Mayilswamy³

et al. 2021

RIA

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Numerous challenges are usually faced during the design and development of an autonomous mobile robot. Path planning and navigation are two significant areas in the control of autonomous mobile robots. The computation of odometry plays a major role in developing navigation systems. This research aims to develop an effective method for the computation of odometry using low-cost sensors, in the differential drive mobile robot. The controller acquires the localization of the robot and guides the path to reach the required target position using the calculated odometry and its created new two-dimensional mapping. The proposed method enables the determination of the global position of the robot through odometry calibration within the indoor and outdoor environment using Graphical Simulation software.

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“…(2) Topological Structure Research of Particle Swarm Optimization Algorithm. As shown in Figure 5, the common population topology structure of particle swarm optimization algorithm, Figure 5(d), this topology structure can make the performance of particle swarm optimization algorithm better than full topology and other topological structures [19].…”

Section: Particle Swarm Optimization Algorithm (1) Introduction To Pa...mentioning

confidence: 99%

Effect of Intelligent Navigation Control-Assisted Running on Promoting the Mental Health of College Students

Jiang

Peng

Wan

2022

Mobile Information Systems

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Psychological wellness is a significant piece of the cutting edge idea of well-being, and a decent degree of emotional well-being is the assurance for understudies to study and live. The purpose of this paper is to study how to promote college students to form a good view of health and establish a healthy, civilized, and scientific way of life. This paper proposes the use of intelligent navigation control to assist running to promote the mental health of college students and proposes methods of intelligent navigation control to assist running. The experimental results in this paper show that E three − dimensional feeling = 10.13, E action response time = 4.12, and E sense of balance = 3.35. It can be seen that the mental health indicators of college students after practice are better than those before practice.

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Intelligent Range-Only Mapping and Navigation for Mobile Robots

Cited by 21 publications

References 30 publications

Area Coverage Optimization using Networked Mobile Robots with State Estimation

Area Coverage Optimization using Networked Mobile Robots with State Estimation

Efficient Computation for Localization and Navigation System for a Differential Drive Mobile Robot in Indoor and Outdoor Environments

Effect of Intelligent Navigation Control-Assisted Running on Promoting the Mental Health of College Students

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