期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 67, 期 8, 页码 4170-4177出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2021.3108672
关键词
Robots; Robot kinematics; Aerospace electronics; Robustness; Mathematical model; Estimation error; Swarm robotics; Input-to-state stability (ISS); partial differential equation (PDE) control systems; swarm robotics
资金
- National Science Foundation [IIS-1724070, CNS-1830335, IIS-2007949]
With the rapid development of AI and robotics, deploying a large swarm of networked robots in the future is becoming possible. Existing research in swarm robotics has focused on bottom-up approaches, but it is difficult to verify global requirements and analyze performance. This study pursues a top-down approach and develops a control strategy to achieve a desired global configuration using mean-field partial differential equations.
With the rapid development of Artificial Intelligence (AI) and robotics, deploying a large swarm of networked robots has foreseeable applications in the near future. Existing research in swarm robotics has mainly followed a bottom-up philosophy with predefined local coordination and control rules. However, it is arduous to verify the global requirements and analyze their performance. This motivates us to pursue a top-down approach, and develop a provable control strategy for transporting a robotic swarm to achieve a desired global configuration. Specifically, we use mean-field partial differential equations (PDEs) to model the swarm and control its mean-field density (i.e., probability density) over a bounded spatial domain using mean-field feedback. The presented control law uses density estimates as feedback signals and generates corresponding velocity fields that, by acting locally on individual robots, guide their global distribution to a target profile. The design of the velocity field is therefore centralized, but the implementation of the controller can be fully distributed-individual robots sense the velocity field and derive their own velocity control signals accordingly. The key contribution lies in applying the concept of input-to-state stability (ISS) to show that the perturbed closed-loop system (a nonlinear and time-varying PDE) is locally ISS with respect to density estimation errors. The effectiveness of the proposed control laws is verified using agent-based simulations.
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