基于扩散变分自编码器的快速运动目标实时拦截与避障统一控制框架
Unified Control Framework for Real-Time Interception and Obstacle Avoidance of Fast-Moving Objects with Diffusion Variational Autoencoder
AI总结：
本文提出基于扩散变分自编码器的统一控制框架，通过离线稠密轨迹图与扩展卡尔曼滤波实现机械臂对快速运动目标的实时拦截与动态避障。
摘要：
在动态环境中，机械臂对快速运动目标的实时拦截面临严峻挑战，因为需要在毫秒级时间内做出快速反应，同时还要应对动态障碍物。本文提出了一种统一控制框架，通过同时拦截动态目标并避开运动障碍物来应对上述挑战。我们方法的核心是使用基于扩散的变分自编码器进行运动规划，以同时实现目标拦截和障碍物避让。我们首先将来自流式事件的高维时间信息编码到一个二维隐流形中，从而能够区分安全轨迹与碰撞轨迹，最终构建出一个离线的稠密连接轨迹图。随后，我们采用扩展卡尔曼滤波器实现对运动目标的精确实时跟踪。利用在已建立的离线稠密图上的图遍历策略，我们生成编码的机器人电机控制指令。最后，我们对这些指令进行解码，以实现机器人电机的实时运动，确保对快速运动目标的有效避障和高拦截精度。在计算机仿真和自主七自由度机械臂上的实验验证证明了所提框架的有效性。结果表明，该机械臂能够在多个不同大小和形状的障碍物之间导航，同时成功拦截从不同角度手动抛出的快速运动目标。我们实验的完整视频演示可在 https://sites.google.com/view/multirobotskill/home 查看。
英文摘要：
Real-time interception of fast-moving objects by robotic arms in dynamic environments poses a formidable challenge due to the need for rapid reaction times, often within milliseconds, amidst dynamic obstacles. This paper introduces a unified control framework to address the above challenge by simultaneously intercepting dynamic objects and avoiding moving obstacles. Central to our approach is using diffusion-based variational autoencoder for motion planning to perform both object interception and obstacle avoidance. We begin by encoding the high-dimensional temporal information from streaming events into a two-dimensional latent manifold, enabling the discrimination between safe and colliding trajectories, culminating in the construction of an offline densely connected trajectory graph. Subsequently, we employ an extended Kalman filter to achieve precise real-time tracking of the moving object. Leveraging a graph-traversing strategy on the established offline dense graph, we generate encoded robotic motor control commands. Finally, we decode these commands to enable real-time motion of robotic motors, ensuring effective obstacle avoidance and high interception accuracy of fast-moving objects. Experimental validation on both computer simulations and autonomous 7-DoF robotic arms demonstrates the efficacy of our proposed framework. Results indicate the capability of the robotic manipulator to navigate around multiple obstacles of varying sizes and shapes while successfully intercepting fast-moving objects thrown from different angles by hand. Complete video demonstrations of our experiments can be found in https://sites.google.com/view/multirobotskill/home.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
