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

TL;DR

This paper introduces a novel MPC-based approach with coverage constraints for weighted coverage path planning, improving efficiency in search tasks like rescue missions by leveraging TSP heuristics.

Contribution

It presents a new MPC formulation with coverage constraints for WCPP, incorporating TSP-based heuristics to enhance solution effectiveness.

Findings

MPC with coverage constraints outperforms naive MPC in simulations.

Initialization with TSP heuristics improves solution quality.

The approach is effective for search and rescue applications.

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, including 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.