Qi Jiaming, Zhou Peng, Ran Guangtao, Gao Han, Wang Pengyu, Li Dongyu, Gao Yufeng, Navarro-Alarcon David
Centre for Transformative Garment Production, The Hong Kong University, NT, Hong Kong.
Harbin Institute of Technology, Department of Control Science and Engineering, Heilongjiang, China.
ISA Trans. 2024 Jul;150:359-373. doi: 10.1016/j.isatra.2024.05.015. Epub 2024 May 13.
The manipulation of compliant objects by robotic systems remains a challenging task, largely due to their variable shapes and the complex, high-dimensional nature of their interaction dynamics. Traditional robotic manipulation strategies struggle with the accurate modeling and control necessary to handle such materials, especially in the presence of visual occlusions that frequently occur in dynamic environments. Meanwhile, for most unstructured environments, robots are required to have autonomous interactions with their surroundings.
To solve the shape manipulation of compliant objects in an unstructured environment, we begin by exploring the regression-based algorithm of representing the high-dimensional configuration space of deformable objects in a compressed form that enables efficient and effective manipulation. Simultaneously, we address the issue of visual occlusions by proposing the integration of an adversarial network, enabling guiding the shaping task even with partial observations of the object. Afterwards, we propose a receding-time estimator to coordinate the robot action with the computed shape features while satisfying various performance criteria. Finally, model predictive controller is utilized to compute the robot's shaping motions subject to safety constraints. Detailed experiments are presented to evaluate the proposed manipulation framework.
Our MPC framework utilizes the compressed representation and occlusion-compensated information to predict the object's behavior, while the multi-objective optimizer ensures that the resulting control actions meet multiple performance criteria. Through rigorous experimental validation, our approach demonstrates superior manipulation capabilities in scenarios with visual obstructions, outperforming existing methods in terms of precision and operational reliability. The findings highlight the potential of our integrated approach to significantly enhance the manipulation of compliant objects in real-world robotic applications.
机器人系统对柔顺物体的操作仍然是一项具有挑战性的任务，这主要是由于它们形状可变，以及其相互作用动力学具有复杂的高维特性。传统的机器人操作策略在处理此类材料所需的精确建模和控制方面存在困难，尤其是在动态环境中经常出现视觉遮挡的情况下。同时，对于大多数非结构化环境，要求机器人能够与周围环境进行自主交互。
为了解决非结构化环境中柔顺物体的形状操作问题，我们首先探索基于回归的算法，以压缩形式表示可变形物体的高维配置空间，从而实现高效且有效的操作。同时，我们通过提出整合对抗网络来解决视觉遮挡问题，即使在对物体进行部分观察的情况下也能指导塑形任务。之后，我们提出一种滚动时间估计器，以在满足各种性能标准的同时，将机器人动作与计算出的形状特征进行协调。最后，利用模型预测控制器在安全约束下计算机器人的塑形运动。我们进行了详细的实验来评估所提出的操作框架。
我们的MPC框架利用压缩表示和遮挡补偿信息来预测物体的行为，而多目标优化器确保所产生的控制动作满足多个性能标准。通过严格的实验验证，我们的方法在存在视觉障碍物的场景中展示出卓越的操作能力，在精度和操作可靠性方面优于现有方法。这些发现凸显了我们的集成方法在显著增强实际机器人应用中对柔顺物体操作的潜力。
