HMPCC: Human-Aware Model Predictive Coverage Control

2025-12-14

AI Summary

This work addresses the dual challenge of multi-robot cooperative coverage in unknown environments and safe, real-time avoidance of non-cooperative human agents. Method: We propose a decentralized model predictive control (MPC)-based dynamic coverage framework. For the first time, human motion trajectory prediction is integrated into the MPC receding-horizon optimization to enable human-robot collaborative coverage. A Gaussian Mixture Model (GMM) is employed to represent the dynamic environment, and distributed optimization ensures fully decentralized operation without explicit inter-robot communication. Contribution/Results: The approach breaks away from conventional coverage algorithms’ assumptions of static, convex environments and fixed density functions. It significantly improves coverage efficiency and environmental adaptability. In dynamic human-robot coexistence scenarios, it reduces collision risk and enhances suppression of redundant exploration, thereby improving both safety and task effectiveness.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Coordination and Collaboration

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Coordinating robots to cover unknown environments safely
• Integrating human motion predictions into robot planning
• Enabling decentralized operation without explicit communication

Innovation

• Integrates human motion predictions into MPC planning
• Models environment as Gaussian Mixture Model for interest regions
• Enables fully decentralized operation without explicit communication