论文标题
基于神经网络临时量化拉格朗日动力学（NNTQLD）在楼梯上实现的环形轨迹实现，并对9-Link Bipedal机器人优化了蚂蚁菌落
Cycloidal Trajectory Realization on Staircase based on Neural Network Temporal Quantized Lagrange Dynamics (NNTQLD) with Ant Colony Optimization for a 9-Link Bipedal Robot
论文作者
论文摘要
在本文中，提出了一种针对带有脚趾脚的双头机器人的能量优化的关节角轨迹跟踪控制的新型最佳技术。为了通过9链接双头模型爬楼梯的任务，提出了一个用于挥杆阶段的轨迹轨迹，以使旋转变量取决于楼梯尺寸。零矩（ZMP）标准是为了满足稳定性约束。本文主要可以分为3个步骤：1）计划稳定的循环轨迹，以便使用无监督的人工神经网络爬上楼上和反向运动学，并具有结节转换程序，以使混蛋最小化。 2）使用拉格朗日动力学对脚趾脚的模型进行建模动力学以及使用弹簧式抑制系统的接触建模，然后开发神经网络时间量化量化的lagrange动力学，该动力学将逆动力学从神经网络输出作为输入。 3）使用蚂蚁菌落优化调整PD（比例导数）控制器参数和躯干角度，以最大程度地减少关节空间轨迹误差和消耗的总能量。已经采用了三种具有可变楼梯维度的病例，并进行了简短的比较，以验证我们提出的工作生成模式的有效性已在MATLAB中模拟。
In this paper, a novel optimal technique for joint angles trajectory tracking control with energy optimization for a biped robot with toe foot is proposed. For the task of climbing stairs by a 9-link biped model, a cycloid trajectory for swing phase is proposed in such a way that the cycloid variables depend on the staircase dimensions. Zero Moment Point(ZMP) criteria is taken for satisfying stability constraint. This paper mainly can be divided into 3 steps: 1) Planning stable cycloid trajectory for initial step and subsequent step for climbing upstairs and Inverse Kinematics using an unsupervised artificial neural network with knot shifting procedure for jerk minimization. 2) Modeling Dynamics for Toe foot biped model using Lagrange Dynamics along with contact modeling using spring-damper system followed by developing Neural Network Temporal Quantized Lagrange Dynamics which takes inverse kinematics output from neural network as its inputs. 3) Using Ant Colony Optimization to tune PD (Proportional Derivative) controller parameters and torso angle with the objective to minimize joint space trajectory errors and total energy consumed. Three cases with variable staircase dimensions have been taken and a brief comparison is done to verify the effectiveness of our proposed work Generated patterns have been simulated in MATLAB .
