Embedded Hierarchical MPC for Autonomous Navigation

2024-06-17

arXiv.org

AI Summary

Real-time autonomous navigation for embedded unmanned aerial vehicles (UAVs) faces a fundamental challenge: conventional nonlinear model predictive control (NMPC) cannot meet millisecond-level closed-loop timing requirements under severe computational constraints. Method: This paper proposes an embedded-friendly hierarchical NMPC architecture that decouples long-horizon safety-aware planning from short-horizon high-frequency tracking. The planning layer employs a large-horizon, low-frequency (~100 ms) NMPC to ensure global feasibility and obstacle avoidance; the tracking layer uses a small-horizon, high-frequency (~5 ms) lightweight MPC for responsive trajectory following. Contribution/Results: We establish theoretical guarantees on recursive feasibility and obstacle-avoidance safety. Engineering deployment is achieved via constraint simplification, hierarchical optimization, and real-time C++ implementation on ARM processors. Experimental validation on a quadrotor demonstrates a 5× increase in planning horizon, significantly improved navigation success rate and trajectory quality over monolithic NMPC baselines in complex static environments.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Machine Learning: Hardware-aware ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

To efficiently deploy robotic systems in society, mobile robots must move autonomously and safely through complex environments. Nonlinear model predictive control (MPC) methods provide a natural way to find a dynamically feasible trajectory through the environment without colliding with nearby obstacles. However, the limited computation power available on typical embedded robotic systems, such as quadrotors, poses a challenge to running MPC in real time, including its most expensive tasks: constraints generation and optimization. To address this problem, we propose a novel hierarchical MPC scheme that consists of a planning and a tracking layer. The planner constructs a trajectory with a long prediction horizon at a slow rate, while the tracker ensures trajectory tracking at a relatively fast rate. We prove that the proposed framework avoids collisions and is recursively feasible. Furthermore, we demonstrate its effectiveness in simulations and lab experiments with a quadrotor that needs to reach a goal position in a complex static environment. The code is efficiently implemented on the quadrotor's embedded computer to ensure real-time feasibility. Compared to a state-of-the-art single-layer MPC formulation, this allows us to increase the planning horizon by a factor of 5, which results in significantly better performance.

Problem

• Enabling real-time embedded MPC for autonomous robot navigation
• Reducing computational load in nonlinear MPC for quadrotors
• Ensuring collision-free trajectory planning in complex environments

Innovation

• Hierarchical MPC with planning and tracking layers
• Long horizon planning at slow update rate
• Efficient embedded implementation for real-time performance