In decentralized multiagent trajectory planners, agents need to communicate
and exchange their positions to generate collision-free trajectories. However,
due to localization errors/uncertainties, trajectory deconfliction can fail
even if trajectories are perfectly shared between agents. To address this
issue, we first present PARM and PARM*, perception-aware, decentralized,
asynchronous multiagent trajectory planners that enable a team of agents to
navigate uncertain environments while deconflicting trajectories and avoiding
obstacles using perception information. PARM* differs from PARM as it is less
conservative, using more computation to find closer-to-optimal solutions. While
these methods achieve state-of-the-art performance, they suffer from high
computational costs as they need to solve large optimization problems onboard,
making it difficult for agents to replan at high rates. To overcome this
challenge, we present our second key contribution, PRIMER, a learning-based
planner trained with imitation learning (IL) using PARM* as the expert
demonstrator. PRIMER leverages the low computational requirements at deployment
of neural networks and achieves a computation speed up to 5500 times faster
than optimization-based approaches.

通过利用感知信息，解决多智能体轨迹规划中的冲突问题，并使用基于模仿学习的低计算要求的规划器，提高计算效率。