Tracking Lagrangian coherent structures in dynamical systems is important for many applications such as oceanography and weather prediction. In this paper, we present a collaborative robotic control strategy designed to track stable and unstable manifolds. The technique does not require global information about the fluid dynamics, and is based on local sensing, prediction, and correction. The collaborative control strategy is implemented on a team of three robots to track coherent structures and manifolds on static flows as well as a noisy time-dependent model of a wind-driven double-gyre often seen in the ocean. We present simulation and experimental results and discuss theoretical guarantees of the collaborative tracking strategy.

We present an ensemble framework for the design of distributed control and communication strategies for the dynamic allocation of a team of robots to a set of tasks. In this work, we assume individual robot controllers are sequentially composed of individual task controllers. This assumption enables the representation of the robot ensemble dynamics as a class of stochastic hybrid systems that can be modeled as continuous-time Markov jump processes where feedback strategies can be derived to control the team's distribution across the tasks. Since the distributed implementation of these feedback strategy requires the estimation of certain population variables, we show how the ensemble model can be expanded to incorporate the dynamics of the information exchange. This then enables us to optimize the individual robot control policies to ensure overall system robustness given some likelihood of resource failures. We consider the assignment of a team of homogeneous robots to a collection of spatially distributed tasks and validate our approach via high-fidelity simulations.

We present an investigation of dynamic team formation strategies for robot ensembles performing a collection of single and two-robot tasks. Specifically, we consider the abstract “stick and pebble” problem, as a variation of the “stick pulling” problem discussed in the literature. We present a formulation of the dynamic team formation problem that is independent of ensemble size and develop a macroscopic analytical description of the ensemble dynamics. The macroscopic model is then used to determine the optimal teaming strategy for two different performance metrics. We present agent-based simulation results to support the validity of our macroscopic analysis.