Application of probability to enhance the performance of fuzzy based mobile robot navigation

Patle, B.K.; Parhi, Dayal R.; Jagadeesh, A.; Kashyap, Sunil Kumar

doi:10.1016/j.asoc.2018.11.026

Cited by 39 publications

(17 citation statements)

References 27 publications

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“…When the optimal parameter adjustment is achieved, the calculation efficiency of the system and the output accuracy can be improved. Related studies using ANFIS include the following: Oliveira et al combined signal strength and Link Quality Indication (LQI) to calculate distance and then used a fuzzy inference system to find the target location [19]; Lee et al applied the parameters considered in the computational process to a fuzzy inference system to generate a new filter, which was then combined with a prototype filter to find the location of the target [20]; and other related practical applications of the relevant literature [21,22].…”

Section: Layer 5: Output Layermentioning

confidence: 99%

An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

Chiu

et al. 2021

Mathematical Problems in Engineering

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In recent years, how to improve the performance of smart factories and reduce the cost of operation has been the focus of industry attention. This study proposes a new type of location-based service (LBS) to improve the accuracy of location information delivered by self-propelled robots. Traditional localization algorithms based on signal strength cannot produce accurate localization results because of the multipath effect. This study proposes a localization algorithm that combines the Kalman filter (KF) and the adaptive-network-based fuzzy inference system (ANFIS). Specifically, the KF was adopted to eliminate noise during the signal transmission process. Through the learning of the ANFIS, the environment parameter suitable for the target was generated, to overcome the deficiency of traditional localization algorithms that cannot obtain real signal strength. In this study, an experiment was conducted in a real environment to compare the proposed localization algorithm with other commonly used algorithms. The experimental results show that the proposed localization algorithm produces minimal errors and stable localization results.

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Section: Layer 5: Output Layermentioning

confidence: 99%

An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

Chiu

et al. 2021

Mathematical Problems in Engineering

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“…According to the outcomes of the comparison, the fuzzy logic system pruned based on human experience outperformed the proposed learning-based fuzzy logic system. Similarly, much work conducted for the point-to-point navigation of mobile robots while avoiding obstacles can be seen in the literature [ 42 , 43 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot

Parween

Muthugala

Heredia

et al. 2021

Sensors

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The inspection and maintenance of drains with varying heights necessitates a drain mapping robot with trained labour to maintain community hygiene and prevent the spread of diseases. For adapting to level changes and navigating in the narrow confined environments of drains, we developed a self-configurable hybrid robot, named Tarantula-II. The platform is a quadruped robot with hybrid locomotion and the ability to reconfigure to achieve variable height and width. It has four legs, and each leg is made of linear actuators and modular rolling wheel mechanisms with bi-directional movement. The platform has a fuzzy logic system for collision avoidance of the side wall in the drain environment. During level shifting, the platform achieves stability by using the pitch angle as the feedback from the inertial measuring unit (IMU) mounted on the platform. This feedback helps to adjust the accurate height of the platform. In this paper, we describe the detailed mechanical design and system architecture, kinematic models, control architecture, and stability of the platform. We deployed the platform both in a lab setting and in a real-time drain environment to demonstrate the wall collision avoidance, stability, and level shifting capabilities of the platform.

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“…Patle et al presented a new path planning algorithm based on probability and fuzzy logic (PFL), which uses distance and speed as combination rules. e method is suitable for static and dynamic environments [27]. Fuzzy control depends on human experience, and its fixed rules are difficult to adapt to complex real environments.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

Guo

Gao

et al. 2021

Mathematical Problems in Engineering

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Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

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Application of probability to enhance the performance of fuzzy based mobile robot navigation

Cited by 39 publications

References 27 publications

An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

An Adaptive Location-Based Tracking Algorithm Using Wireless Sensor Network for Smart Factory Environment

Collision Avoidance and Stability Study of a Self-Reconfigurable Drainage Robot

A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

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