HMPCC: Human-Aware Model Predictive Coverage Control

TL;DR

This paper introduces HMPCC, a human-aware Model Predictive Control framework for multi-robot coverage that anticipates human movements, enabling safer, more efficient, and adaptive exploration in unknown environments.

Contribution

The paper presents a novel decentralized coverage control method that integrates human trajectory prediction into MPC, enhancing robot coordination without explicit communication.

Findings

Human trajectory forecasting improves coverage efficiency.

Decentralized operation without explicit communication is effective.

The approach adapts to dynamic environments with human presence.

Abstract

We address the problem of coordinating a team of robots to cover an unknown environment while ensuring safe operation and avoiding collisions with non-cooperative agents. Traditional coverage strategies often rely on simplified assumptions, such as known or convex environments and static density functions, and struggle to adapt to real-world scenarios, especially when humans are involved. In this work, we propose a human-aware coverage framework based on Model Predictive Control (MPC), namely HMPCC, where human motion predictions are integrated into the planning process. By anticipating human trajectories within the MPC horizon, robots can proactively coordinate their actions %avoid redundant exploration, and adapt to dynamic conditions. The environment is modeled as a Gaussian Mixture Model (GMM), representing regions of interest. Team members operate in a fully decentralized manner, without relying on explicit communication, an essential feature in hostile or communication-limited scenarios. Our results show that human trajectory forecasting enables more efficient and adaptive coverage, improving coordination between human and robotic agents.