A Multiple Artificial Potential Functions Approach for Collision Avoidance in UAV Systems

2025-03-28

AI Summary

Existing artificial potential field (APF)-based methods for static obstacle avoidance by resource-constrained unmanned aerial vehicles (UAVs) lack closed-loop stability guarantees, suffering from chattering and susceptibility to local minima—particularly under stringent real-time response requirements and uncertain obstacle detection. Method: This paper proposes a Multi-Artificial Potential Function (MAPOF) control framework that integrates hybrid systems theory with Lyapunov-based analysis. Contribution/Results: MAPOF is the first APF variant to rigorously establish asymptotic stability of the closed-loop system and derive explicit, tunable parameter conditions for stability. The design inherently mitigates local minima and suppresses control chattering. Numerical simulations in complex static obstacle environments demonstrate that MAPOF achieves stable, smooth, collision-free trajectory planning with significantly improved convergence speed and robustness compared to conventional single-potential-field approaches.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Intelligent Robots: Motion and Path Planning
• Multiagent Systems: Multiagent Planning

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
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Collision avoidance is a problem largely studied in robotics, particularly in unmanned aerial vehicle (UAV) applications. Among the main challenges in this area are hardware limitations, the need for rapid response, and the uncertainty associated with obstacle detection. Artificial potential functions (APOFs) are a prominent method to address these challenges. However, existing solutions lack assurances regarding closed-loop stability and may result in chattering effects. Motivated by this, we propose a control method for static obstacle avoidance based on multiple artificial potential functions (MAPOFs). We derive tuning conditions on the control parameters that ensure the stability of the final position. The stability proof is established by analyzing the closed-loop system using tools from hybrid systems theory. Furthermore, we validate the performance of the MAPOF control through simulations, showcasing its effectiveness in avoiding static obstacles.

Problem

• Addresses collision avoidance in UAV systems using MAPOFs
• Ensures closed-loop stability with derived tuning conditions
• Validates effectiveness in static obstacle avoidance via simulations

Innovation

• Multiple artificial potential functions for collision avoidance
• Hybrid systems theory ensures closed-loop stability
• Simulation validates MAPOF control effectiveness