An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

Panah, Amir; Motameni, Homayun; Ebrahimnejad, Ali

doi:10.1007/s12555-020-0430-9

Cited by 4 publications

(3 citation statements)

References 20 publications

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“…Because of the complexity of the real world, this assumption is hard to be tenable. Incorrect a priori knowledge about the control and the observation noise matrices can seriously degrade the accuracy of these algorithms [27][28][29][30]. Based on the previous research [31], a dynamic fractional order and alpha stable distribution particle swarm optimization method is adopted, and the prior knowledge Q and R are adjusted dynamically by a fitness function.…”

Section: Parallel Computingmentioning

confidence: 99%

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

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Simultaneous localization and mapping (SLAM) is a typical computing-intensive task. Based on its own computing power, a mobile robot has difficult meeting the real-time performance and accuracy requirements for the SLAM process at the same time. Benefiting from the rapid growth of the network data transmission rate, cloud computing technology begins to be applied in the robotics. There is the reliability problem caused by solely relying on cloud computing. To compensate for the insufficient airborne capacity, ensure the real-time performance and reliability, and improve the accuracy, a SLAM algorithm based on edge-cloud collaborative computing is proposed. The edge estimates the mobile robot pose and the local map using a square root unscented Kalman filter (SR-UKF). The cloud estimates the mobile robot pose and the global map using a distributed square root unscented particle filter (DSR-UPF). By using sufficient particles in the cloud, DSR-UPF can improve the SLAM accuracy. The cloud returns the particle with the largest posteriori probability to the edge, and the edge performs edge-cloud data fusion based on probability. Both the simulation and the experimental results show that the proposed algorithm can improve the estimation accuracy and reduce the execution time at the same time. By transferring the heavy computation from robots to the cloud, it can enhance the environmental adaptability of mobile robots.

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Section: Parallel Computingmentioning

confidence: 99%

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Zhang

Chen³

2022

Journal of Sensors

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“…Finally, in the control process, SLAM regulates its motions by following the reference path generated in the decision-making process. Many studies have been conducted on SLAM-based autonomous driving [ 10 ]; however, there is a limitation in the complexity of implementation due to the necessity of multiple sensors or deep learning for accurate surrounding perception and navigation in environments with unexpected dynamic obstacles. To alleviate the issue associated with dynamic obstacles, Dang et al [ 11 ] modified the SLAM by implementing sensor fusion and dynamic object removal methods.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay

Park,

Kwon

2024

Biomimetics

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In this paper, we propose a reinforcement learning-based end-to-end learning method for the autonomous driving of a mobile robot in a dynamic environment with obstacles. Applying two additional techniques for reinforcement learning simultaneously helps the mobile robot in finding an optimal policy to reach the destination without collisions. First, the multifunctional reward-shaping technique guides the agent toward the goal by utilizing information about the destination and obstacles. Next, employing the hindsight experience replay technique to address the experience imbalance caused by the sparse reward problem assists the agent in finding the optimal policy. We validated the proposed technique in both simulation and real-world environments. To assess the effectiveness of the proposed method, we compared experiments for five different cases.

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“…In very recent years, the online analysis is carried out to predict the gas fuel by making ANN controller with multilayer perceptron in bubbling fluidized bed gasifier based on biomass technology [10]. Authors in [23,24] efficiently implemented the fuzzy logic control on autonomous wheeled mobile robot and photovoltaic application to produce maximum power through maximum power point tracking (MPPT). Adaptive Neuro-Fuzzy Inference System (ANFIS) approach has been applied for MPPT of variable speed wind turbine system, bearing fault identification and financial and welding system which gives more accurate results as compared to traditional techniques [25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm

et al. 2021

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Circulating Fluidized Bed gasifiers are widely used in industry to convert solid fuel into liquid fuel. The Artificial Neural Network and neuro-fuzzy algorithm have immense potential to improve the efficiency of the gasifier. The main focus of this article is to implement the Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System modeling approach to estimate solid circulation rate at high pressure in the Circulating Fluidized Bed gasifier. The experimental data is obtained on a laboratory scale prototype in the Chemical Engineering laboratory at COMSATS University Islamabad. The Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System use four input features—pressure, single mean diameter, total valve opening and riser dp—and one output feature mass flow rate with multiple neurons in the hidden layers to estimate the flow of solid particles in the riser. Both Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System model worked on 217 data samples and output results are compared based on their Mean Square Error, Regression analysis, Mean Absolute Error and Mean Absolute Percentage Error. The experimental results show the effectiveness of Adaptive Neuro-Fuzzy Inference System (Mean Square Error is 0.0519 and Regression analysis R2=1.0000), as it outperformed Artificial Neural Network in terms of accuracy (Mean Square Error is 1.0677 and Regression analysis R2=0.9806).

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An Efficient Computational Hybrid Filter to the SLAM Problem for an Autonomous Wheeled Mobile Robot

Cited by 4 publications

References 20 publications

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

A SLAM Algorithm Based on Edge-Cloud Collaborative Computing

Autonomous Driving of Mobile Robots in Dynamic Environments Based on Deep Deterministic Policy Gradient: Reward Shaping and Hindsight Experience Replay

Estimation of the Solid Circulation Rate in Circulating Fluidized Bed System Using Adaptive Neuro-Fuzzy Algorithm

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