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

TL;DR

HetSwarm addresses the challenge of coordinating a UAV and a ground robot for fast, safe navigation in dense, dynamic environments. The drone is guided by an artificial potential field (APF) for global path generation, while the ground robot follows via impedance-based connectivity, and additionally links with low-height obstacles to avoid collisions. A mass-spring-damper impedance model governs the drone-ground linkage, with a dedicated ground obstacle impedance term, and a PID path follower ensures accurate tracking. Experimental validation in a Gym PyBullet environment demonstrates high robustness (90% success over 30 trials), effective obstacle avoidance (average ground-robot deviation ~0.45 m near obstacles), and competitive performance against CBS in dynamic clutter. These results suggest HetSwarm’s potential for real-time, multi-robot logistics tasks in cluttered facilities.

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.

Figures (6)

• Figure 1: HetSwarm generates paths in a dense environment where the UAV (black line) navigates towards the goal and the mobile robot (blue dashed line) follows the UAV by maintaining an impedance link connection (red dashed line).
• Figure 2: System architecture and the pipeline of HetSwarm for global and local collision avoidance.
• Figure 3: Experimental setup in the Gym Pybullet environment showing the trajectory of the drone and mobile robot under a dense environment.
• Figure 4: Simulation results of two different cases: sparse (a, b) to dense (c, d). Each case is formed with two different initial and target positions. Black dots are the obstacles for both drones and mobile robots, green dots are the ground obstacles deflected only by the mobile robot. Gray and light green-colored circles show the safe deflection regions for both agents.
• Figure 5: Simulation examples of dynamic obstacle avoidance in sparse (a, b) and dense (c, d) environments. Dashed purple lines are the obstacle trajectories. Black dots are the obstacles for both robots, and green dots are the ground obstacles deflected only by the mobile robot. Gray and light green-colored circles show the safe deflection regions for both agents.