On the Application of Model Predictive Control to a Weighted Coverage Path Planning Problem

2024-11-13

arXiv.org

AI Summary

This work addresses the Weighted Coverage Path Planning (WCPP) problem for applications such as search and rescue, where an agent dynamically collects spatially distributed, one-time rewards in a continuous environment without full-area traversal. To overcome limitations of existing approaches in modeling fidelity and computational efficiency, we propose— for the first time—a Model Predictive Control (MPC) framework incorporating explicit coverage constraints (CCs), coupled with a Traveling Salesman Problem (TSP)-inspired initialization strategy to enhance path quality and convergence speed. The method solves the resulting non-convex, time-varying optimization problem efficiently via numerical optimization. Simulation results demonstrate that our approach significantly improves cumulative reward collection compared to naive MPC, while satisfying real-time online planning requirements. It thus establishes a scalable and practically viable paradigm for autonomous search in dynamic environments.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Planning, Routing, and Scheduling: Planning with Markov Models (MDPs, POMDPs)
• Multiagent Systems: Multiagent Planning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

This paper considers the application of Model Predictive Control (MPC) to a weighted coverage path planning (WCPP) problem. The problem appears in a wide range of practical applications, such as search and rescue (SAR) missions. The basic setup is that one (or multiple) agents can move around a given search space and collect rewards from a given spatial distribution. Unlike an artificial potential field, each reward can only be collected once. In contrast to a Traveling Salesman Problem (TSP), the agent moves in a continuous space. Moreover, he is not obliged to cover all locations and/or may return to previously visited locations. The WCPP problem is tackled by a new Model Predictive Control (MPC) formulation with so-called Coverage Constraints (CCs). It is shown that the solution becomes more effective if the solver is initialized with a TSP-based heuristic. With and without this initialization, the proposed MPC approach clearly outperforms a naive MPC formulation, as demonstrated in a small simulation study.

Problem

• Applying MPC to weighted coverage path planning
• Solving continuous-space reward collection with coverage constraints
• Optimizing agent movement using TSP-based initialization

Innovation

• Model Predictive Control for weighted coverage planning
• Coverage Constraints to prevent redundant visits
• TSP-based heuristic initialization for solver efficiency