This paper proposes the optimization of a fully connected recurrent neural network (FCRNN) using advanced multiobjective continuous ant colony optimization (AMO-CACO) for the multiobjective gait generation of a biped robot (the NAO). The FCRNN functions as a central pattern generator and is optimized to generate angles of the hip roll and pitch, the knee pitch, and the ankle pitch and roll. The performance of the FCRNN-generated gait is evaluated according to the walking speed, trajectory straightness, oscillations of the body in the pitch and yaw directions, and walking posture, subject to the basic constraints that the robot cannot fall down and must walk forward. This paper formulates this gait generation task as a constrained multiobjective optimization problem and solves this problem through an AMO-CACO-based evolutionary learning approach. The AMO-CACO finds Pareto optimal solutions through ant-path selection and sampling operations by introducing an accumulated rank for the solutions in each single-objective function into solution sorting to improve learning performance. Simulations are conducted to verify the AMO-CACO-based FCRNN gait generation performance through comparisons with different multiobjective optimization algorithms. Selected software-designed Pareto optimal FCRNNs are then applied to control the gait of a real NAO robot.

中文翻译：

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使用先进的连续蚁群优化循环神经网络的双足机器人步态的多目标进化


本文提出使用先进的多目标连续蚁群优化 (AMO-CACO) 来优化全连接循环神经网络 (FCRNN)，以生成双足机器人 (NAO) 的多目标步态。 FCRNN 充当中央模式生成器，并经过优化以生成髋部滚动和俯仰、膝盖俯仰以及脚踝俯仰和滚动的角度。根据机器人不能摔倒且必须向前行走的基本约束，根据行走速度、轨迹直线度、身体在俯仰和偏航方向上的振荡以及行走姿势来评估FCRNN生成的步态性能。本文将此步态生成任务表述为约束多目标优化问题，并通过基于 AMO-CACO 的进化学习方法解决该问题。 AMO-CACO通过蚂蚁路径选择和采样操作找到帕累托最优解，将每个单目标函数中解的累积排名引入到解排序中，以提高学习性能。通过与不同多目标优化算法的比较，进行仿真来验证基于 AMO-CACO 的 FCRNN 步态生成性能。然后应用选定的软件设计的帕累托最优 FCRNN 来控制真实 NAO 机器人的步态。