Core Concepts
The core message of this article is to develop a nonlinear model predictive control framework that can efficiently coordinate multiple agents to optimally cover an area, even in the presence of unknown environmental conditions.
Abstract
The article presents two control architectures for coverage control of multiple agents with nonlinear dynamics and constraints:
-
Two-Layers Approach:
- A central server calculates the optimal Voronoi partitions and centroid references, which are then tracked by individual agent MPCs.
- For known environments, the approach guarantees convergence to an optimal centroidal Voronoi configuration while ensuring recursive feasibility and collision avoidance.
- For unknown environments, an active learning strategy is incorporated to balance exploration and exploitation, allowing the agents to learn the unknown density function while converging to the optimal coverage.
-
One-Layer Approach:
- The reference optimization is directly integrated into the MPC formulation of each agent, avoiding the hierarchical structure.
- This approach leverages assumptions on the target cost function to ensure convergence to the optimal coverage configuration.
- The one-layer framework aims to reduce the time and energy required for exploration compared to the two-layers approach.
Both architectures are rigorously analyzed, and their theoretical properties, such as recursive feasibility, constraint satisfaction, and convergence, are proven. The proposed methods are also validated experimentally using a miniature car platform.
Stats
The article does not contain any explicit numerical data or metrics to support the key arguments. It focuses on the theoretical development and analysis of the proposed control frameworks.
Quotes
"The problem of coverage control, i.e., of coordinating multiple agents to optimally cover an area, arises in various applications."
"To cope with this second challenge, "exploitation" targeted to the coverage control task needs to be combined with "exploration" given by data collection performed via active learning [10], [11] to improve the estimate of the initially unknown density function."
"The general coverage control problem encompassing an unknown environment and nonlinear constrained dynamics, while ensuring persistent collision avoidance, has not yet been addressed in the literature."