Robust MADER: Decentralized Multiagent Drone Trajectory Planner

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.

Massachusetts Institute of Technology, Cambridge, MA

Due to its wide range of applications, multiagent UAV trajectory planning has been extensively studied. For reliable real-world deployment, it is essential that a trajectory planner be robust to both communication delays and dynamic environments; however, achieving robustness to both communication delays and dynamic environments has not been addressed in the literature. Multiagent trajectory planners can be centralized (one machine plans every agent's trajectory) or decentralized (each agent plans its own trajectory). Decentralized planners are more scalable and robust to failures of the centralized machine. Despite these advantages, a decentralized scheme requires communication between the agents, and communication delays could potentially introduce failure in the trajectory deconfliction between the agents. It is also worth noting that there are two layers of decentralization-decentralized planning and decentralized communication architecture. Even if the planning algorithm is decentralized, agents may still require a centralized communication architecture, such as Wi-Fi. Multiagent planners can also be classified according to whether or not they are asynchronous. Asynchronous planning enables each agent to independently trigger the planning step without considering the planning status of other agents. In contrast to synchronous planners, which require all agents to wait at a so-called synchronization barrier until planning can be globally triggered, asynchronous methods tend to be more scalable. They are, however, also more susceptible to communication delays since agents plan and execute trajectories independently.

Many state-of-the-art decentralized trajectory planners do not consider communication delays or explicitly state assumptions about communication. For example, SCP, decNS, and LSC are decentralized and synchronous, but SCP and decNS implicitly and LSC explicitly assume a perfect communication environment without any communication delays. The algorithm decMPC is decentralized, but it requires synchronicity and communication delays to be within a fixed planning period. EDG-Team is a decentralized semi-asynchronous planner, which solves joint optimization as a group. EDG-Team cooperatively tackles the path planning problem but implicitly assumes no communication delays.