Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

Wang, Yaonan; Yang, Yimin; Xiao-fang, Yuan; Zuo, Yuefei; Yuan-li, Zhou; Yin, Feng; Tan, Liang

doi:10.1016/j.measurement.2011.05.010

Cited by 24 publications

(17 citation statements)

References 38 publications

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“…By appropriate procedures and definitions, the robot dynamics can be transformed as Yaonan et al [3] Predictive control strategy based on ELM 3…”

Section: Intelligent Automation and Soft Computingmentioning

confidence: 99%

“…Recently, using a kinematic model of nonholonomic mobile robots, various approaches such as backstepping [1], neural networks [2][3][4][5], neural-fuzzy [6] have been proposed to solve the tracking problem. Considering the dynamics model, research has been proposed to achieve trajectory tracking of a mobile robot with nonholonomic constraints [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

Yang

Lin

Miao

et al. 2014

Intelligent Automation & Soft Computing

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In this paper, we propose a novel nonlinear predictive control strategy based on an extreme learning machine to address the path-tracking control problem of wheeled mobile robots in the presence external disturbances. The hybrid chaotic optimization algorithm (HCOA), which can avoid being trapped in local minima and improve convergence in dealing with the large space and highdimension optimization problems, is used to perform real-time nonlinear minimization of the cost function of a mobile robot to enhance the control accuracy. The proposed improved bidirectional extreme learning machine is employed to model the mobile robot plant and estimate future plant output. The experimental results of tracking the automation mobile robot indicate that the proposed controller can provide more accuracy and faster tracking performance than traditional neural network predictive controllers.

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“…By appropriate procedures and definitions, the robot dynamics can be transformed as Yaonan et al [3] Predictive control strategy based on ELM 3…”

Section: Intelligent Automation and Soft Computingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

Yang

Lin

Miao

et al. 2014

Intelligent Automation & Soft Computing

View full text Add to dashboard Cite

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“…Artificial potential field (APF) [6,14,[19][20][21][22][23][24] is one of the most commonly used schemes in the field of MR navigation [18]. In the work of Ge and Cui [20], the authors apply APF in a dynamic environment containing a moving target.…”

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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“…However, the problem of avoiding collisions in dynamic environment is much harder. Several works have been developed for dynamic environments like velocity obstacles (Fiorini and Shiller, 1998;Large et al, 2005), collision cones (Chakravarthy and Ghose, 1998), the rolling window method (Zhang and Xi, 2003), inevitable collision state (Fraichard and Asama, 2004), the prediction model for beam curvature method (Shi et al, 2010), and other methods (Jaradata et al, 2011;Yaonana and Yimin, 2011;Zhu and Hua, 2011;Zhong et al, 2014;Bis et al, 2012). This paper is organised as follows.…”

Section: Introductionmentioning

confidence: 99%

Autonomous mobile robot navigation based on an integrated environment representation designed in dynamic environments

Boujelben

Jaiem

Boutereaa

et al. 2017

IJAAC

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Abstract:The collision avoidance concept is widely applied for developing and designing in autonomous robotic applications. In an unknown environment, the challenge in mobile robot navigation or path planning is to find the path from the starting point to the target avoiding obstacles. This paper investigates the mobile robot navigation based on an integrated environment representation of the environment. It presents a developed algorithm for solving the obstacle avoidance problem in dynamic environment. The proposed algorithm ensures that the mobile robot whenever senses the obstacle, changes his direction without being collided and moves smoothly to the designed target when the path is free. The simulation results and experimental ones are presented to show performances and the effectiveness of the presented approach. These results illustrate that the developed algorithm can be well applied in the mobile robot navigation.

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Autonomous mobile robot navigation system designed in dynamic environment based on transferable belief model

Cited by 24 publications

References 38 publications

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

Predictive Control Strategy Based on Extreme Learning Machine for Path-Tracking of Autonomous Mobile Robot

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

Autonomous mobile robot navigation based on an integrated environment representation designed in dynamic environments

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