Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach

Er, Meng Joo; Deng, Chao

doi:10.1109/tie.2005.847576

Cited by 91 publications

(35 citation statements)

References 13 publications

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“…However, the design of a fuzzy controller is difficult and highly time consuming task, as there are lots of parameters values to define [10]. Therefore, many evolutionary algorithms [11][12][13] were applied to tune the parameters of FLC.…”

Section: Introductionmentioning

confidence: 99%

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Findi¹,

Marhaban²,

Kamil³

et al. 2017

Journal of Electrical Engineering and Technology

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-The problem of determining a smooth and collision-free path with maximum possible speed for a Mobile Robot (MR) which is chasing a moving target in a dynamic environment is addressed in this paper. Genetic Network Programming with Reinforcement Learning (GNP-RL) has several important features over other evolutionary algorithms such as it combines offline and online learning on the one hand, and it combines diversified and intensified search on the other hand, but it was used in solving the problem of MR navigation in static environment only. This paper presents GNP-RL based on predicting collision positions as a first attempt to apply it for MR navigation in dynamic environment. The combination between features of the proposed collision prediction and that of GNP-RL provides safe navigation (effective obstacle avoidance) in dynamic environment, smooth movement, and reducing the obstacle avoidance latency time. Simulation in dynamic environment is used to evaluate the performance of collision prediction based GNP-RL compared with that of two state-of-the art navigation approaches, namely, Q-Learning (QL) and Artificial Potential Field (APF). The simulation results show that the proposed GNP-RL outperforms both QL and APF in terms of smooth movement and safer navigation. In addition, it outperforms APF in terms of preserving maximum possible speed during obstacle avoidance.

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Section: Introductionmentioning

confidence: 99%

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Findi¹,

Marhaban²,

Kamil³

et al. 2017

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

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“…Literature [10] design three fuzzy controllers which consist of attitude adjustment, steering control and parking decision, and in addition to the on-board controller it design the ground control station, achieving the auxiliary control function in large parking lot. At the same time, some studies combine the fuzzy control and other algorithms to improve the control ability, such as neural networks [11] and genetic algorithms [12].…”

Section: Introductionmentioning

confidence: 99%

Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA

Wang¹,

Yu²,

Guan³

et al. 2016

MATEC Web Conf.

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Abstract. Because of many common scenes of reverse parking in real life, this paper presents a fuzzy controller which accommodates front and back adjustment of vehicle's body attitude, and based on chaotic-genetic arithmetic to optimize the membership function of this controller, and get a vertical parking fuzzy controller whose simulation result is good .The paper makes the hardware-software embedded design for system based on Field-Programmable Gate Array (FPGA), and set up a 1:10 verification platform of smart car to verify the fuzzy garage parking system with real car. Verification results show that, the system can complete the parking task very well.

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“…erefore, the research on the coordination of robotic swarms has attracted considerable attention. Taking the advantages of distributed sensing and actuation, a robotic swarm can perform some cooperative tasks such as moving a large object that is usually not executable by a single robot [3][4][5][6][7]. Applications about the analysis and design of robotic swarms included autonomous unmanned aerial vehicles, congestion control of communication networks, and distributed sensor networks autonomous, and so forth [1,2,[8][9][10].…”

Section: Introductionmentioning

confidence: 99%

An Adaptive -Based Formation Control for Multirobot Systems

2013

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We describe a decentralized formation problem for multiple robots, where an ∞ formation controller is proposed. e network of dynamic agents with external disturbances and uncertainties are discussed in formation problems. We �rst describe how to design social potential �elds to obtain a formation with the shape of a polygon. en, we provide a formal proof of the asymptotic stability of the system, based on the de�nition of a proper Lyapunov function and ∞ technique. e advantages of the proposed controller can be listed as robustness to input nonlinearity, external disturbances, and model uncertainties, while applicability on a group of any autonomous systems with -degrees of freedom. Finally, simulation results are demonstrated for a multiagent formation problem of a group of six robots, illustrating the effective attenuation of approximation error and external disturbances, even in the case of agent failure or leader tracking.

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Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach

Cited by 91 publications

References 13 publications

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Collision Prediction based Genetic Network Programming-Reinforcement Learning for Mobile Robot Navigation in Unknown Dynamic Environments

Research and Implementation of Automatic Fuzzy Garage Parking System Based on FPGA

An Adaptive -Based Formation Control for Multirobot Systems

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