Intent Prediction-Driven Model Predictive Control for UAV Planning and Navigation in Dynamic Environments

2024-09-24

arXiv.org

AI Summary

This work addresses safety and real-time navigation challenges for drones operating near humans in construction environments—specifically, failures of narrow-field-of-view visual tracking, difficulty in predicting dynamic obstacles’ intentions, and rapid trajectory obsolescence due to environmental changes. We propose an intention-driven closed-loop navigation framework. Our key contributions are: (1) a novel obstacle intention prediction module formulated as a Markov Decision Process (MDP) to explicitly model the motion uncertainty of human workers; and (2) an intention-guided nonlinear Model Predictive Control (MPC) algorithm enabling predictive, responsive, and real-time trajectory replanning. Extensive simulations and real-world flight experiments demonstrate that our approach significantly reduces collision counts compared to baseline methods, achieving superior safety and real-time adaptability in dynamic human-robot collaborative construction settings.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Aerial robots can enhance construction site productivity by autonomously handling inspection and mapping tasks. However, ensuring safe navigation near human workers remains challenging. While navigation in static environments has been well studied, navigating dynamic environments remains open due to challenges in perception and planning. Payload limitations restrict the robots to using cameras with limited fields of view, resulting in unreliable perception and tracking during collision avoidance. Moreover, the rapidly changing conditions of dynamic environments can quickly make the generated optimal trajectory outdated.To address these challenges, this paper presents a comprehensive navigation framework that integrates perception, intent prediction, and planning. Our perception module detects and tracks dynamic obstacles efficiently and handles tracking loss and occlusion during collision avoidance. The proposed intent prediction module employs a Markov Decision Process (MDP) to forecast potential actions of dynamic obstacles with the possible future trajectories. Finally, a novel intent-based planning algorithm, leveraging model predictive control (MPC), is applied to generate navigation trajectories. Simulation and physical experiments demonstrate that our method improves the safety of navigation by achieving the fewest collisions compared to benchmarks.

Problem

• Ensuring safe UAV navigation near human workers in dynamic environments
• Overcoming unreliable perception due to limited camera fields of view
• Addressing outdated trajectories in rapidly changing dynamic conditions

Innovation

• Integrates perception, intent prediction, and planning
• Uses MDP for dynamic obstacle action forecasting
• Applies intent-based MPC for trajectory generation