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首页> 外文期刊>IEEE Transactions on Industrial Electronics >Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach
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Obstacle Avoidance of a Mobile Robot Using Hybrid Learning Approach

机译：使用混合学习方法的移动机器人避障

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摘要

In this paper, a hybrid learning approach for obstacle avoidance of a mobile robot is presented. The key features of the approach are, firstly, innate hardwired behaviors which are used to bootstrap learning in the mobile robot system. A neuro-fuzzy controller is developed from a pre-wired or innate controller based on supervised learning in a simulation environment. The fuzzy inference system has been constructed based on the Generalized Dynamic Fuzzy Neural Networks learning algorithm of Wu and Er, whereby structure and parameters identification are carried out automatically and simultaneously. Secondly, the neuro-fuzzy controller is capable of re-adapting in a new environment. After carrying out the learning phase on a simulated robot, the controller is implemented on a real robot. A reinforcement learning method based on the Fuzzy Actor-Critic Learning algorithm is employed so that the system can re-adapt to a new environment' without "liuman intervention. In this phase, the structure of the fuzzy inference system and the parameters of the antecedent parts of fuzzy rules are frozen, and reinforcement learning is applied to further tune the parameters in the consequent parts of the fuzzy rules. Through the hybrid learning approach, an efficient and compact neuro-fuzzy system is generated for obstacle avoidance of a mobile robot in the real world.
机译：在本文中，提出了一种混合学习方法来避免移动机器人的障碍。该方法的关键特征是，首先，固有的硬连线行为被用于引导移动机器人系统中的学习。基于仿真环境中的监督学习，从预接线或先天控制器开发了神经模糊控制器。基于Wu和Er的广义动态模糊神经网络学习算法构建了模糊推理系统，从而自动，同时进行结构和参数辨识。其次，神经模糊控制器能够重新适应新的环境。在模拟机器人上执行学习阶段后，控制器将在真实机器人上实现。采用基于模糊Actor-Critic学习算法的强化学习方法，使系统无需“ Alan干预”即可重新适应新环境。在此阶段，模糊推理系统的结构和先验参数冻结了部分模糊规则，并应用强化学习进一步调整了模糊规则后续部分中的参数，通过混合学习方法，生成了一种高效紧凑的神经模糊系统，以避开移动机器人的障碍。现实中。

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