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.
翻译:我们设计了一个合作规划框架,为系绳式机器人双人座创造最佳轨迹,它的任务是利用灵活网收集分散在大片地区的分散物体。具体地说,拟议的规划框架首先为每个机器人制作一套密集的路径点,作为优化的初始化。接下来,我们制定一个迭代优化计划,以产生光滑和无碰撞的轨迹,同时确保在机器人双人架内开展合作,以便有效地收集物体和适当避免障碍。我们验证模拟中生成的轨迹,并利用模型参考适应控制器(MRC)在物理机器人中实施这些轨迹,以便处理携带的载荷的未知动态。在一系列研究中,我们发现:(一) U形状的成本功能在规划合作机器人双人座时有效,以及(二) 任务效率并不总是与系绳式网长度成正比。根据环境配置,我们的框架可以测量最佳的净长度。据我们所知,我们是第一个为系绳式机器人提供这种估计的第一个。