In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots sense the local surrounding environment and react to obstacles in their proximity. We consider no explicit communication, so the local environment information and the control actions are not shared between the robots. At any given time step, the leader solves a model predictive control (MPC) problem with its known set of obstacles and plans a feasible trajectory to complete the task. The follower estimates the inputs of the leader and uses a policy to assist the leader while reacting to obstacles in its proximity. The leader infers obstacles in the follower's vicinity by using the difference between the predicted and the real-time estimated follower control action. A method to switch the leader-follower roles is used to improve the control performance in tight environments. The efficacy of our approach is demonstrated with detailed comparisons to two alternative strategies, where it achieves the highest success rate, while completing the task fastest.

翻译：在本文中,我们为两个机器人提出了一个领导者-追随者等级战略,在部分已知的环境中合作运输一个物体,同时设置障碍。两个机器人都感知周围的环境,并对周围的障碍作出反应。我们认为没有明确的沟通,因此当地的环境信息和控制行动不会在机器人之间分享。在任何特定的时间步骤中,领导者用已知的一系列障碍来解决模型预测控制(MPC)问题,并计划一条完成这项任务的可行轨迹。追随者估计了领导者的投入,并使用一项政策协助领导者应对附近的障碍。领导者用预测的和实时估计的跟踪者控制行动之间的差别来推断追随者周围的障碍。一种改变领导者-追随者作用的方法被用来改善紧凑环境中的控制性能。我们的方法的有效性通过对两种替代战略进行详细比较而得到证明,在这些战略中,它取得了最高的成功率,同时以最快的速度完成任务。