移动机器人动态避障的调节发育学习
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郑州大学 电气与信息工程学院,郑州 450001

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E-mail: wangdongshu@zzu.edu.cn.

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TP242

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国家自然科学基金项目(62173309);河南省自然科学基金项目(202300410483).


Motivated developmental learning of mobile robots in dynamic collision-avoidance
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School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China

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    摘要:

    在环境认知的动态避障过程中,除了预期不确定性事件,移动机器人还可能会遇到非预期不确定性事件.如何高效、灵活地应对非预期不确定性事件是移动机器人动态避障中面临的一个重要挑战.目前关于这方面的研究相对较少,且基于这些研究的移动机器人普遍缺乏自主学习能力,难以快速、灵活地应对突变的外部环境. 鉴于此,首先,设计一个新的碰撞危险度指标,该指标不仅考虑障碍物的距离,同时也考虑障碍物速度对移动机器人运动的影响.模拟人脑中乙酰胆碱和去甲肾上腺素在应对环境不确定性时的反应机理,通过碰撞危险度指标引导移动机器人的注意力网络在关注预期刺激的背侧注意力网络和关注新刺激的腹侧注意网络之间切换,使得机器人灵活应对环境中的不确定性事件;然后,设计新的神经元学习率,以增强调节发育网络隐含层神经元的学习能力,提高机器人应对突变环境的快速响应能力;接着,修改突触权值更新规则,以提高移动机器人行为决策的准确性;最后,通过在两种不同场景下的仿真实验以及物理环境中的实验,验证所提出的应对环境中非预期不确定性事件的移动机器人调节发育学习方法的可行性.

    Abstract:

    In dynamic collision-avoidance of the environmental cognition, except the expected uncertainty, mobile robots may also encounter unexpected uncertainty. Studying how to deal with the unexpected uncertainty efficiently and flexibly is an important challenge for mobile robots. At present, there are relatively few studies on this aspect, and the mobile robots based on these studies generally lack the ability of autonomous learning, and it is difficult to quickly and flexibly respond to the abrupt external environment. In this paper, a novel collision risk index is designed, which not only considers the influence of the distance of the obstacle, but also that of the speed of the obstacle on the motion of the mobile robots. Simulating the reaction mechanism of the acetylcholine and norepinephrine in human brain in response to environmental uncertainty, through the collision risk index, the attention network of the mobile robot is guided to switch between the dorsal attention network which focuses on the expected stimulus, and the ventral attention network which focuses on new stimulus, and make the robot flexible response to the uncertainty in the environment. At the same time, a new neuronal learning rate is designed to enhance the learning ability of neurons in the hidden layer of the motivated developmental network and improve the robot's ability to respond quickly to the abrupt environment. In addition, the synaptic weight updating rule is modified to improve the accuracy of the mobile robot's behavioral decision. Simulation results in two different scenarios, as well as the physical experiment, verify the feasibility of the proposed motivated development learning method in response to unexpected uncertainty in the environment.

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王东署,赵红燕.移动机器人动态避障的调节发育学习[J].控制与决策,2023,38(11):3112-3120

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  • 在线发布日期: 2023-10-08
  • 出版日期: 2023-11-20
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