Can MPC optimize multi-agent weighted coverage path planning?
On the Application of Model Predictive Control to a Weighted Coverage Path Planning Problem
This paper explores using Model Predictive Control (MPC) to guide an agent (e.g., a drone) through a space with varying rewards, like a probability map for search and rescue. The goal is to maximize collected rewards, with each reward collectable only once. Unlike traditional path planning, the agent isn't required to visit every location. A key innovation is the introduction of "Coverage Constraints" to prevent the agent from repeatedly collecting the same reward. The researchers also propose using a Traveling Salesperson Problem (TSP) based heuristic derived from Gaussian Mixture Models to initialize the MPC solver and improve performance by giving it a good starting trajectory through key high-reward areas. This hierarchical approach helps overcome limitations of local optima in the highly non-linear optimization problem. Although not explicitly multi-agent, the core concepts of reward maximization with coverage constraints and using heuristics for initialization are readily adaptable to LLM-based multi-agent systems navigating complex environments and making decisions based on dynamic information.