Object Gathering with a Tethered Robot Duo
This addresses the problem of efficient object collection in large areas for robotics applications, but it is incremental as it builds on existing tethered robot and planning methods.
The paper tackles the problem of gathering scattered objects with a tethered robot duo using a flexible net, by developing a cooperative planning framework that generates optimal trajectories, validated in simulation and on physical robots with adaptive control, and finds that a U-shape cost function is effective and task efficiency depends on net length, with the framework estimating optimal net length.
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.