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A trajectory reconstruction approach is proposed for accurate leader-following with a desired separation distance on the trajectory. The approach aims to reconstruct the relative trajectory of the leader within a sliding time window based on the on-board sensors. The needed trajectory information of the leader relative to the follower is acquired by combining the noisy and intermittently available visual measurement with the odometry information. The situation that the leader is out of filed of view (FOV) is also taken into consideration. Moreover, instead of following the current pose of the leader, the trajectory information of a historical point on the trajectory is used as the reference input for controller, thereby avoiding the cutting-corner issue. Simulation results demonstrate the effectiveness of the proposed trajectory reconstruction approach.

The paper is concerned with the multi-robot leader-following problem in the presence of frequent dropouts in vision detection. In many scenarios, for instance a structured environment, it is inevitable to experience outage of vision detection due to reasons such as the target moving out of view, vision occlusion, motion blurring, etc. The paper proposes a Bayesian trajectory estimation based leader-following approach that can offer accurate path following given intermittent vision observations. The follower robot estimates the trajectory of the leader robot based on the noise-corrupted odometry information of both robots, and inter-robot relative observations based on detection of fiducial markers using an RGBD camera. A linear trajectory-following control method is employed to track a historical pose of the leader robot on the estimated trajectory. Results are obtained based on evaluating the proposed leader-following approach in tests with a zig-zag shaped trajectory and with a trajectory that contains sharp turns. Accepted version