We devise a cooperative planning framework to generate optimal trajectories for a robot duo tethered by a flexible net to gather scattered objects spread in a large area. 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 gather objects efficiently and avoid obstacles properly. We validate the generated trajectories in simulation and implement them in physical robots using Model Reference Adaptive Control (MRAC) to handle unknown dynamics of carried payloads. In a series of studies, we find that: (i) a U-shape cost function for maintaining separation distance 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.