Monkey see, monkey do: Constant time delay leader following for wheeled mobile robots using uncertainty-tuned model predictive control

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Wang, Kai
Givigi, Sidney
Marshall, Joshua A.
This paper presents a constant time delay leader-follower system for wheeled mobile robots that can be used in a military vehicle convoy. It allows a follower vehicle to follow the same trajectory travelled by a leader with a specified time delay by estimating a sequence of waypoints travelled by the leader. This paper focuses on the design of the two major components of the leader-follower system: the estimator and the controller. Different from previous works on constant time delay leader-follower systems, where the two components are designed relatively independently, we propose a design that connects the two components by having a model predictive controller (MPC) whose tuning is based on the uncertainties of the estimated leader waypoints. By weighting the waypoint states in the MPC cost function as inversely proportional to their uncertainties, we achieve better tracking performance under the condition of noisy odometry sensor data compared to the traditional practice equally-weighted state errors. This approach reduces the dependence on empirical tuning and performance was studied both in simulation and on real robots in representative off-road environments. Currently, the system is designed to track the linear, longitudinal trajectories of the leader, as a starting point for tracking nonlinear trajectories in future work.