论文标题
与束缚的机器人二重奏聚集的物体聚集
Object Gathering with a Tethered Robot Duo
论文作者
论文摘要
我们设计了一个合作计划框架，以生成一个束缚的机器人二人组的最佳轨迹，该二重奏的任务是使用灵活的网络收集在大面积的散落物体。具体而言，提出的计划框架首先为每个机器人生成一组密集的航路点，作为优化的初始化。接下来，我们制定了一种迭代优化方案，以生成平滑且无冲突的轨迹，同时确保机器人二重奏内的合作有效地收集对象并正确避免障碍。我们使用模型参考自适应控制器（MRAC）来验证模拟中生成的轨迹，并在物理机器人中实现它们，以处理未知的有效载荷动力学。在一系列研究中，我们发现：（i）U形成本功能在计划合作机器人二重奏中有效，并且（ii）任务效率并不总是与束缚的净长度成正比。给定环境配置，我们的框架可以评估最佳的净长度。据我们所知，我们的是第一个为束缚的机器人二重奏提供的估计。
We devise a cooperative planning framework to generate optimal trajectories for a tethered robot duo, who is tasked to gather scattered objects spread in a large area using a flexible net. Specifically, the proposed planning framework first produces a set of dense waypoints for each robot, serving as the initialization for optimization. Next, we formulate an iterative optimization scheme to generate smooth and collision-free trajectories while ensuring cooperation within the robot duo to efficiently gather objects and properly avoid obstacles. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Controller (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function is effective in planning cooperative robot duo, and (ii) the task efficiency is not always proportional to the tethered net's length. Given an environment configuration, our framework can gauge the optimal net length. To our best knowledge, ours is the first that provides such estimation for tethered robot duo.
