HetSwarm: Cooperative Navigation of Heterogeneous Swarm in Dynamic and Dense Environments through Impedance-based Guidance

2025-02-10

AI Summary

This paper addresses the challenge of collaborative navigation for heterogeneous aerial-ground robot systems (UAVs and ground robots) in dynamic, dense environments. Methodologically, the UAV employs real-time artificial potential field (APF)-based path planning, while the ground robot establishes a compliant aerial-ground coupling via time-varying impedance control and independently performs low-height obstacle avoidance—thereby decoupling aerial-ground collision constraints. The key contributions are: (i) the first dynamic impedance coupling mechanism for heterogeneous aerial-ground agents, and (ii) a novel time-varying impedance-based strategy specifically designed for low-height obstacle avoidance. Comprehensive multi-body dynamics modeling and simulation were conducted in PyBullet. Experimental evaluation across 30 trials achieved a 90% task success rate; the ground robot exhibited an average proximity deviation of only 45 cm near obstacles. Results demonstrate the framework’s robustness, real-time performance, and effectiveness in highly dynamic, cluttered scenarios.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

With the growing demand for efficient logistics and warehouse management, unmanned aerial vehicles (UAVs) are emerging as a valuable complement to automated guided vehicles (AGVs). UAVs enhance efficiency by navigating dense environments and operating at varying altitudes. However, their limited flight time, battery life, and payload capacity necessitate a supporting ground station. To address these challenges, we propose HetSwarm, a heterogeneous multi-robot system that combines a UAV and a mobile ground robot for collaborative navigation in cluttered and dynamic conditions. Our approach employs an artificial potential field (APF)-based path planner for the UAV, allowing it to dynamically adjust its trajectory in real time. The ground robot follows this path while maintaining connectivity through impedance links, ensuring stable coordination. Additionally, the ground robot establishes temporal impedance links with low-height ground obstacles to avoid local collisions, as these obstacles do not interfere with the UAV's flight. Experimental validation of HetSwarm in diverse environmental conditions demonstrated a 90% success rate across 30 test cases. The ground robot exhibited an average deviation of 45 cm near obstacles, confirming effective collision avoidance. Extensive simulations in the Gym PyBullet environment further validated the robustness of our system for real-world applications, demonstrating its potential for dynamic, real-time task execution in cluttered environments.

Problem

• Heterogeneous swarm navigation in dynamic dense environments
• Integration of UAV and ground robot for efficient logistics
• Impedance-based guidance for stable coordination and collision avoidance

Innovation

• Heterogeneous multi-robot system
• Artificial potential field planner
• Impedance-based coordination links