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。

感知感知优化型基于学习的多智能体轨迹规划器

PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory  Planner

Recently, neural control policies have outperformed existing model-based
planning-and-control methods for autonomously navigating quadrotors through
cluttered environments in minimum time. However, they are not perception aware,
a crucial requirement in vision-based navigation due to the camera's limited
field of view and the underactuated nature of a quadrotor. We propose a
learning-based system that achieves perception-aware, agile flight in cluttered
environments. Our method combines imitation learning with reinforcement
learning (RL) by leveraging a privileged learning-by-cheating framework. Using
RL, we first train a perception-aware teacher policy with full-state
information to fly in minimum time through cluttered environments. Then, we use
imitation learning to distill its knowledge into a vision-based student policy
that only perceives the environment via a camera. Our approach tightly couples
perception and control, showing a significant advantage in computation speed
(10 times faster) and success rate. We demonstrate the closed-loop control
performance using hardware-in-the-loop simulation.

提出了一种基于学习的系统，通过模仿学习和强化学习的结合，实现了感知感知，在杂乱环境中实现了敏捷的飞行，并通过闭环控制演示了其性能。