Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments (2303.06222v6)

Abstract: Communication delays can be catastrophic for multiagent systems. However, most existing state-of-the-art multiagent trajectory planners assume perfect communication and therefore lack a strategy to rectify this issue in real-world environments. To address this challenge, we propose Robust MADER (RMADER), a decentralized, asynchronous multiagent trajectory planner robust to communication delay. RMADER ensures safety by introducing (1) a Delay Check step, (2) a two-step trajectory publication scheme, and (3) a novel trajectory-storing-and-checking approach. Our primary contributions include: proving recursive feasibility for collision-free trajectory generation in asynchronous decentralized trajectory-sharing, simulation benchmark studies, and hardware experiments with different network topologies and dynamic obstacles. We show that RMADER outperforms existing approaches by achieving a 100% success rate of collision-free trajectory generation, whereas the next best asynchronous decentralized method only achieves 83% success.

• The paper introduces Robust MADER (RMADER), a decentralized trajectory planner for UAVs robust to communication delays in dynamic multiagent environments.
• RMADER incorporates unique features like a delay check process and two-step trajectory publication to maintain collision-free planning despite asynchronous communication.
• Empirical results show RMADER achieved 100% collision avoidance with communication delays up to 300ms, demonstrating its superior safety performance over existing decentralized planners.

Decentralized Multiagent Trajectory Planning in Dynamic Environments with Robust MADER

The paper "Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments" introduces RMADER, an advanced trajectory planning framework for unmanned aerial vehicles (UAVs). This framework aims to address the pervasive issue of communication delays in multiagent systems, which most existing planners overlook by presuming ideal communication environments. The authors tackle the dual challenges of asynchronous communication and dynamic obstacles, which are critical for effectively deploying UAV trajectory planners in real-world scenarios.

Core Contributions and Methodology

RMADER enhances the core capabilities of its predecessor, MADER, by incorporating mechanisms explicitly designed to handle communication delay. These include:

1. Delay Check Process: This introduces a reliable mechanism to verify trajectory feasibility despite potential delays in receiving updated trajectory data from other agents. It involves a sequential checking approach—check, opt, and comm steps—where trajectories are repeatedly evaluated for collision risks.
2. Two-Step Trajectory Publication: Distinguishing between optimized and committed trajectory phases ensures only trajectories that can be safely executed and synchronized are shared with peers. This separation helps maintain collision-free path generation across the network.
3. Trajectory Storing and Checking: Agents maintain records of both previously committed and newly optimized trajectories of other agents. This ensures all possible interactions are considered during trajectory optimization, significantly enhancing robustness against communication irregularities.

Empirical Results

The paper delineates extensive empirical evaluations of RMADER against several state-of-the-art decentralized planners, including MADER, EGO-Swarm, and EDG-Team. It successfully demonstrates RMADER's superiority in terms of safety, showcasing a 100% success rate in collision-free trajectory generation, even with introduced communication delays up to 300 ms. While RMADER offers robust safety guarantees, its thorough checking mechanisms may induce conservativeness, leading to higher travel completion times and occasional deadlocks.

Hardware experiments further validate RMADER's practicality, utilizing centralized and decentralized (mesh) network infrastructures. The robustness of RMADER facilitates high-speed UAV operations in complex environments—reaching velocities up to 5.8 m/s—while successfully avoiding collisions with dynamic obstacles.

Implications and Future Perspectives

The advances brought by RMADER are significant for domains where UAV coordination is crucial, such as logistics, surveillance, and disaster response. By ensuring safety even with significant communication delays, RMADER promotes the feasibility of deploying UAV swarms in real-world, uncontrolled environments.

Looking ahead, refining RMADER's mechanisms to reduce conservatism could help balance performance and safety. Further research into dynamic network topologies and enhancing communication protocols can improve the mesh network's performance, thus extending UAV operation limits. Additionally, exploring adaptive methods that dynamically adjust the trajectory checking processes based on real-time network conditions may yield efficiency gains without compromising safety.

In summary, this paper's contribution lies in addressing a critical gap in multiagent trajectory planning by providing a framework that is both robust to real-world communication delays and effective in dynamic environments. RMADER sets a promising direction for future research, paving the way for autonomous UAV operations to become mainstream in various complex applications.