Constraint-Driven Coordinated Control of Multi-Robot Systems
This work addresses the need for resilient and adaptable control in long-term autonomy applications like environmental monitoring, though it appears incremental as it builds on existing optimization frameworks.
The paper tackled the problem of coordinating multi-robot systems by reformulating tasks as a constrained optimization problem to minimize a cost function, resulting in a decentralized controller with finite-time convergence using only local information, as tested on ground mobile robots for environmental monitoring.
In this paper we present a reformulation--framed as a constrained optimization problem--of multi-robot tasks which are encoded through a cost function that is to be minimized. The advantages of this approach are multiple. The constraint-based formulation provides a natural way of enabling long-term robot autonomy applications, where resilience and adaptability to changing environmental conditions are essential. Moreover, under certain assumptions on the cost function, the resulting controller is guaranteed to be decentralized. Furthermore, finite-time convergence can be achieved, while using local information only, and therefore preserving the decentralized nature of the algorithm. The developed control framework has been tested on a team of ground mobile robots implementing long-term environmental monitoring.