High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environments

TL;DR

The paper tackles high-speed, collision-free swarm navigation in unknown, cluttered environments by introducing HDSM, a decentralized and synchronous framework that operates mapping and planning in parallel. A voxel-based mapping module feeds a separate global path generator and a trajectory generator; the latter uses a Time-Aware Safe Corridor (TASC) built from prior trajectories and inter-agent hyperplanes to constrain an MIQP/MPC optimization, enabling fast, safe flight even with occluded obstacles. Key innovations include the TASC with time-local constraints, adaptive reference trajectories, and a robust communication strategy that tolerates delays and packet loss. Across extensive simulations and hardware tests, HDSM achieves substantial speedups ($97\%$ faster) and shorter flight times ($50\%$) with $100\%$ success, validating its practical applicability for real-time aerial swarm operations in unknown environments.

Abstract

Coordinated flight of multiple drones allows to achieve tasks faster such as search and rescue and infrastructure inspection. Thus, pushing the state-of-the-art of aerial swarms in navigation speed and robustness is of tremendous benefit. In particular, being able to account for unexplored/unknown environments when planning trajectories allows for safer flight. In this work, we propose the first high-speed, decentralized, and synchronous motion planning framework (HDSM) for an aerial swarm that explicitly takes into account the unknown/undiscovered parts of the environment. The proposed approach generates an optimized trajectory for each planning agent that avoids obstacles and other planning agents while moving and exploring the environment. The only global information that each agent has is the target location. The generated trajectory is high-speed, safe from unexplored spaces, and brings the agent closer to its goal. The proposed method outperforms four recent state-of-the-art methods in success rate (100% success in reaching the target location), flight speed (97% faster), and flight time (50% lower). Finally, the method is validated on a set of Crazyflie nano-drones as a proof of concept.

Figures (11)

• Figure 1: Multiple agents (green spheres) moving towards each other in a simulation environment where the obstacles are occupied voxels in orange. The Safe Corridor of each agent is shown in red, the previous positions as a green line, and the predicted future positions (MIQP/MPC solution) as the yellow line.
• Figure 2: The mapping and planning modules are shown in 2D for the sake of clarity. The mapping framework takes a point cloud generated at any time $t$ by sensors and produces a voxel grid with free, occupied, and unknown voxels. It is run at constant period $T_{\text{map}}$. The voxel grid is then fed to the planning framework. The planning framework consists of 2 modules each running at a different frequency and on a different CPU thread: the global path generation module (running at period $T_{\text{path}}$) which takes the voxel grid and generates a global path to the goal; the trajectory generation module (running at period $T_{\text{traj}}$) which takes the voxel grid and the global path to generate a collision-free and dynamically-feasible optimal trajectory.
• Figure 3: In the proposed framework, the path planning is done on a separate thread (thread 1), and is run at a potentially different frequency than the trajectory planning thread (thread 0).
• Figure 4: The mapping module steps.
• Figure 5: A 2D example of a traversability check (Fig. \ref{['fig:traversability']}): Green arrows indicate the set of possible paths between adjacent voxels. A diagonal path, indicated by the black arrow, is only considered feasible when one of the (green) paths between adjacent voxels does not result in a collision. The results of JPS, DMP, and the shortened final path in an example 2D environment (Fig. \ref{['fig:path_planning']}): the final path improves the optimality of DMP while keeping the same margin distance from obstacles.