Inspired by a viewpoint that complex/chaotic dynamics would play an important role in biological systems including the brain, chaotic dynamics introduced in a recurrent neural network was applied to robot control in ill-posed situations. By computer experiments we show that a model robot arm without an advanced visual processing function can catch a target object and bring it to a set position under ill-posed situations (e.g., in the presence of unknown obstacles). The key idea in these works is adaptive switching of a system parameter (connectivity) between a chaos regime and attractor regime in a neural network model, which generates, depending on environmental circumstances, either chaotic motions or definite motions corresponding to embedded attractors. The adaptive switching results in useful functional motions of the robot arm. These successful experiments indicate that chaotic dynamics is potentially useful for practical engineering control applications. In addition, this novel autonomous arm system is implemented in a hardware robot arm that can avoid obstacles and reach for a target in a situation where the robot can get only rough target information, including uncertainty, by means of a few sensors, as indicated in the appendix, A1 and A2.

中文翻译：

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混沌在递归神经网络中控制不适定问题的应用：新型自主机器人手臂。

受复杂/混沌动力学将在包括大脑在内的生物系统中扮演重要角色的观点启发，将循环神经网络中引入的混沌动力学应用于不适定情况下的机器人控制。通过计算机实验，我们表明，不具有高级视觉处理功能的模型机械手可以捕获目标对象，并在不适当地情况下（例如，存在未知障碍物）将其置于设定位置。这些工作的关键思想是在神经网络模型中的混沌状态和吸引子状态之间自适应地切换系统参数（连接性），取决于环境条件，它会生成与嵌入吸引子相对应的混沌运动或确定运动。自适应切换导致机器人手臂进行有用的功能运动。这些成功的实验表明，混沌动力学对于实际的工程控制应用可能是有用的。此外，这种新颖的自主手臂系统在硬件机器人手臂中实施，可以避免障碍物并在机器人只能通过几个传感器仅获得包括不确定性在内的粗略目标信息的情况下到达目标的情况，如附录A1和A2。