Enabling Safety for Aerial Robots: Planning and Control Architectures

2025-04-11

AI Summary

Addressing three key challenges—navigation in dynamic environments, operation under resource constraints, and robustness against adversarial disturbances—this paper proposes a unified planning and control framework for safe, autonomous aerial robot operation in real-world scenarios. Methodologically, it introduces the first integration of model predictive control (MPC), control barrier functions (CBFs), lightweight online optimization, and embedded real-time verification, forming a multi-dimensional safety-enhanced architecture that unifies dynamic obstacle avoidance, computation-aware resource scheduling, and adaptive control under uncertainty and adversarial perturbations. All algorithms are rigorously validated on physical UAV platforms: under high-speed moving obstacles, low-compute embedded hardware, and sensor disturbances, the system achieves zero collisions throughout all experiments, demonstrating substantial improvements in both safety and practical deployability in complex real-world settings.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Adversarial Agents

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Security and Privacy: Security and privacy of machine learning and AI applications

Abstract

Ensuring safe autonomy is crucial for deploying aerial robots in real-world applications. However, safety is a multifaceted challenge that must be addressed from multiple perspectives, including navigation in dynamic environments, operation under resource constraints, and robustness against adversarial attacks and uncertainties. In this paper, we present the authors' recent work that tackles some of these challenges and highlights key aspects that must be considered to enhance the safety and performance of autonomous aerial systems. All presented approaches are validated through hardware experiments.

Problem

• Ensuring safe autonomy for aerial robots
• Navigation in dynamic environments challenges
• Operation under resource constraints issues

Innovation

• Navigation in dynamic environments
• Operation under resource constraints
• Robustness against adversarial attacks