用神经网络生成可定制的机械臂轨迹，保证精准重复。
Generating and Customizing Robotic Arm Trajectories using Neural Networks
- 用神经网络计算正运动学并生成关节角序列
- 在人工数据集上训练，实现高精度线性运动
- 适合需要精准交互的机器人场景
我们提出一种基于神经网络的机械臂轨迹生成与定制方法，确保动作的精确性和可重复性。为验证该方法的潜力，我们设计并实现了该技术，并在认知机器人实验中应用。在此场景中，NICO 机器人能够以精确的直线运动指向空间中的特定点，提升了与人类交互时动作的可预测性。为此，神经网络计算了机械臂的正运动学，并结合关节角生成器，利用从合适起始和终止姿态生成的人工数据集进行训练。通过计算角速度，机器人实现了平滑运动，其动作质量以轨迹形状和精度评估。由于方法具有广泛适用性，本方案成功生成了可定制形状且能适应不同场景的高精度轨迹。
原文摘要 · Abstract (English)
We introduce a neural network approach for generating and customizing the trajectory of a robotic arm, that guarantees precision and repeatability. To highlight the potential of this novel method, we describe the design and implementation of the technique and show its application in an experimental setting of cognitive robotics. In this scenario, the NICO robot was characterized by the ability to point to specific points in space with precise linear movements, increasing the predictability of the robotic action during its interaction with humans. To achieve this goal, the neural network computes the forward kinematics of the robot arm. By integrating it with a generator of joint angles, another neural network was developed and trained on an artificial dataset created from suitable start and end poses of the robotic arm. Through the computation of angular velocities, the robot was characterized by its ability to perform the movement, and the quality of its action was evaluated in terms of shape and accuracy. Thanks to its broad applicability, our approach successfully generates precise trajectories that could be customized in their shape and adapted to different settings.
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