An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning

Yung, N.H.C.; Ye, Cang

doi:10.1109/3477.752807

Cited by 78 publications

(51 citation statements)

References 17 publications

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“…Global path planning methods are usually conducted off-line in a completely known environment [27]. In the global path planning approach, an exact model of the environment has to be used to plan the path.…”

Section: Methods Used To Develop Robotic Agents 41 Path Planningmentioning

confidence: 99%

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Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hagras

Sobh

2002

Information Sciences

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The control of autonomous intelligent robotic agent operating in unstructured changing environments includes many objective difficulties. One major difficulty concerns the characteristics of the environment that the agent should operate in. In unstructured and changing environments the inconsistency of the terrain, the irregularity of the product and the open nature of the working environment result in complex problems of identification, sensing and control.Problems can range from the effects of varying environmental conditions on the robot sensors and traction performance through to the need to deal with the presence of unexpected situations. Another major challenge is the large amounts of uncertainty that characterises real-world environments. On the one hand, it is not possible to have exact and complete prior knowledge of these environments. On the other hand, knowledge acquired through sensing is affected by uncertainty and imprecision. The quality of sensor information is influenced by sensor noise, the limited field of view, the conditions of observation, and the inherent difficulty of the perceptual interpretation process.Because environments and users of systems continuously change, robotic agents have to be adaptive. Intelligence helps because it gives systems the capacity to adapt more rapidly to environmental changes or to handle much more complex functions. In his paper we introduce this special issue and introduce the difficulty robots are facing in unstructured environments and how learning and computational intelligence can help the robots to adapt and give them the necessary intelligence they to face the challenges they encounter in their environments.

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Section: Methods Used To Develop Robotic Agents 41 Path Planningmentioning

confidence: 99%

“…However real environments are never simple enough. On the other hand local path planning techniques, also known as the obstacle avoidance methods, are potentially more efficient in robot navigation when the environment is unknown (which is our case) or only partially known [27].…”

Section: Methods Used To Develop Robotic Agents 41 Path Planningmentioning

confidence: 99%

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Hagras

Sobh

2002

Information Sciences

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“…In primary learning stage, we use a simple channel environment as Fig.6 to train the model [11], as regular as channel environment is, the robot's track keeps unchanged in the learning process, so we can set a stopping learning standards for it.…”

Section: B the Twice Learning By Learning Automatamentioning

confidence: 99%

“…Based on fuzzy inference system, S. Kermiche combined artificial potential field theory with supervised learning to adjust the fuzzy controller, successfully realize the robot's obstacle avoidance and target navigation, the curve is closer to the optimal path [4], Tan and Lee applied genetic algorithm to regular fuzzy controller, also achieved good results [5,6], meanwhile, there are many scholars combined reinforcement learning with fuzzy inference system to accomplish different navigation tasks [7][8][9][10][11], Meng improved reinforcement by proposing a dynamic fuzzy Q-learning, greatly improved the speed of operation and control accuracy [12,13], in 2012, Gao based on the fuzzy inference, introduced bionics control mechanism, through continuous interaction with the external environment enable the robot features of self-learning and adaptability [14], however, the fuzzy rule base established by expert knowledge increases the uncertainty and imprecision of the model.…”

Section: Introductionmentioning

confidence: 99%

Research on a New Path Planning Method for Mobile Robot

Zhang¹,

Ruan²,

Zhu³

et al. 2013

Proceedings of the 3rd International Conference on Electric and Electronics

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Abstract-Aim at the path planning problem of a mobile vehicle in an unknown environment, a new hybrid learning method is proposed, which is based on the fuzzy inference system and assisted by the combination of supervised learning and bionic automata, the bionic learning endows the robot the capabilities of self-learning and self-organization, the supervised learning is used to tune the input membership function of the fuzzy inference system, that avoid the expert knowledge's disadvantage of inaccurate estimation to the actual model. The simulation results show that the robot has learned the ability of obstacle avoidance and goal seeking and performs a better robustness in overcoming the local minima.

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“…While it is possible to use reinforcement learning [3,17,18] or supervised learning [19] to automatically learn the parameters of membership functions or behavior modules, both techniques may be impractical for use on many real robots. Reinforcement learning can require a prohibitively long learning period, and the success of supervised learning is strongly dependent on having su cient and appropriate input/output training data.…”

Section: Mobile Robot Planning and Controlmentioning

confidence: 99%

Fuzzy Control of a Log Carrying Robot on Tree-Filled Steep-Sloping Terrains

Beranek

Morbi

Ahmadi

2012

Paladyn, Journal of Behavioral Robotics

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A modular robotic system and its fuzzy logic based controller are proposed for use in logging operations in forest environments with steep slopes. The Log-Carrying Robot (LCR) concept is composed of two modular wheeled robotic agents with individual wheel steering that connect to the ends of a log to a form a centrally controlled robot. A fuzzy controller specifies the desired direction of travel using four factors: the presence of obstacles, boundaries limiting the robot' s travel space, the heading of the goal position relative to the robot, and the slope of the terrain. The capabilities of the proposed controller are demonstrated in simulation using a rectangular robot with four individually actuated and steered wheels. Results indicate that the controller successfully steers the robot towards the goal position while avoiding obstacles using only eleven fuzzy rules. Additionally, the simple rules are shown to be e ective at automatically compensating for sloped terrain by avoiding direct travel down hills, as well as adapting for various robot lengths.Keywords mobile robot · fuzzy control · behavior based control · log carrying · modular robot

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An intelligent mobile vehicle navigator based on fuzzy logic and reinforcement learning

Cited by 78 publications

References 17 publications

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Intelligent learning and control of autonomous robotic agents operating in unstructured environments

Research on a New Path Planning Method for Mobile Robot

Fuzzy Control of a Log Carrying Robot on Tree-Filled Steep-Sloping Terrains

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