Elman Fuzzy Adaptive Control for Obstacle Avoidance of Mobile Robots Using Hybrid Force/Position Incorporation

Wen, Shuhuan; Zheng, Wei; Zhu, Jinghai; Li, Xiaoli; Chen, Shengyong

doi:10.1109/tsmcc.2011.2157682

Cited by 64 publications

(7 citation statements)

References 19 publications

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“…In Wen, 75 Elman neural network and fuzzy logic integrated control algorithm is proposed to enhance the performance of the robot in obstacle avoidance. A virtual force field is built between the robot and obstacles by the hybrid force/position algorithm.…”

Section: Collision Avoidance Methodsmentioning

confidence: 99%

“…Using Elman neural network to compensate uncertainty effect of the dynamic model of the WMA, and which is integrated with the fuzzy control method to self-tune the exact distance between the WMR and the obstacle online. Further application of the method proposed in Wen 75 can be found in Yang et al 76 In Zhu and Yang, 77 neuro fuzzy-based method is integrated for the WMR under unknown environment. A fuzzy logic system with both target tracking and obstacle avoidance performances is designed.…”

Section: Collision Avoidance Methodsmentioning

confidence: 99%

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Review of wheeled mobile robot collision avoidance under unknown environment

Wang¹,

Zhang³

et al. 2021

Science Progress

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Recently, the working scenes of the robot have been emerging as diversity and complexity with gradually mature of robotic control technology. The challenge of robot adaptability emerges, especially in complicated and unknown environments. Among the numerous researches on improving the adaptability of robots, aiming at avoiding collision between robot and external environment, obstacle avoidance has drawn much attention. Compared to the global circumvention requiring the environmental information that is known, the local obstacle avoidance is a promising method due to the environment is possibly dynamic and unknown. This study is aimed at making a review of research progress about local obstacle avoidance methods for wheeled mobile robots (WMRs) under complex unknown environment in the last 20 years. Sensor-based obstacle perception and identification is first introduced. Then, obstacle avoidance methods related to WMRs’ motion control are reviewed, mainly including artificial potential field (APF)-based, population-involved meta heuristic-based, artificial neural network (ANN)-based, fuzzy logic (FL)-based and quadratic optimization-based, etc. Next, the relevant research on Unmanned Ground Vehicles (UGVs) is surveyed. Finally, conclusion and prospection are given. Appropriate obstacle avoidance methods should be chosen based on the specific requirements or criterion. For the moment, effective fusion of multiple obstacle avoidance methods is becoming a promising method.

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Section: Collision Avoidance Methodsmentioning

confidence: 99%

Section: Collision Avoidance Methodsmentioning

confidence: 99%

Review of wheeled mobile robot collision avoidance under unknown environment

Wang¹,

Zhang³

et al. 2021

Science Progress

View full text Add to dashboard Cite

show abstract

“…The case of iterative learning HMFC for contour tracking of an object with unknown shape is analysed in [49]. Neural Networks were used in [50] while Elman Fuzzy control was used in [51].…”

Section: Applications Of Hmfcmentioning

confidence: 99%

Hybrid motion/force control: a review

2017

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This paper reviews hybrid motion/force control (HMFC), a control scheme which enables robots to perform tasks involving both motion, in the free space, and interactive force, at the contacts. Motivated by the large amount of literature on this topic, we facilitate comparison and elucidate the key differences among different approaches. An emphasis is placed on the study of the decoupling of motion control and force control. And we conclude that it is indeed possible to achieve a complete decoupling, however this feature can be relaxed or sacrificed to reduce the robot's joint torques while still completing the task.

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“…Most of the state-of-the-art path planning algorithms assume that accurate localization is available and at least the environment information is partially known (Wen et al , 2012). However, the real-world environment is always unknown, it is an interesting challenge to realize autonomous navigation of agents in unknown environments.…”

Section: Introductionmentioning

confidence: 99%

Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment

Wen

Lam

et al. 2021

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Purpose This paper aims to use the Monodepth method to improve the prediction speed of identifying the obstacles and proposes a Probability Dueling DQN algorithm to optimize the path of the agent, which can reach the destination more quickly than the Dueling DQN algorithm. Then the path planning algorithm based on Probability Dueling DQN is combined with FastSLAM to accomplish the autonomous navigation and map the environment. Design/methodology/approach This paper proposes an active simultaneous localization and mapping (SLAM) framework for autonomous navigation under an indoor environment with static and dynamic obstacles. It integrates a path planning algorithm with visual SLAM to decrease navigation uncertainty and build an environment map. Findings The result shows that the proposed method offers good performance over existing Dueling DQN for navigation uncertainty under the indoor environment with different numbers and shapes of the static and dynamic obstacles in the real world field. Originality/value This paper proposes a novel active SLAM framework composed of Probability Dueling DQN that is the improved path planning algorithm based on Dueling DQN and FastSLAM. This framework is used with the Monodepth depth image prediction method with faster prediction speed to realize autonomous navigation in the indoor environment with different numbers and shapes of the static and dynamic obstacles.

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Elman Fuzzy Adaptive Control for Obstacle Avoidance of Mobile Robots Using Hybrid Force/Position Incorporation

Cited by 64 publications

References 19 publications

Review of wheeled mobile robot collision avoidance under unknown environment

Review of wheeled mobile robot collision avoidance under unknown environment

Hybrid motion/force control: a review

Probability Dueling DQN active visual SLAM for autonomous navigation in indoor environment

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