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

arXiv: https://arxiv.org/abs/2406.10060 · HTML: https://mlsanity.com/paper/2406.10060/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 46 references that are in the map.
- First posted on arXiv 14 Jun 2024.

## Builds on (most cited first, 27 of 27)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Motion Planning Among Dynamic, Decision-Making Agents with Deep Reinforcement Learning](https://mlsanity.com/paper/1805.01956/) (2018)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [RAPTOR: Robust and Perception-aware Trajectory Replanning for Quadrotor Fast Flight](https://mlsanity.com/paper/2007.03465/) (2020)
- [Learning Perception-Aware Agile Flight in Cluttered Environments](https://mlsanity.com/paper/2210.01841/) (2022)
- [MADER: Trajectory Planner in Multi-Agent and Dynamic Environments](https://mlsanity.com/paper/2010.11061/) (2020)
- [CGD: Constraint-Guided Diffusion Policies for UAV Trajectory Planning](https://mlsanity.com/paper/2405.01758/) (2024)
- [Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning](https://mlsanity.com/paper/2109.07735/) (2021)
- [EGO-Swarm: A Fully Autonomous and Decentralized Quadrotor Swarm System in Cluttered Environments](https://mlsanity.com/paper/2011.04183/) (2020)
- [Online Trajectory Generation with Distributed Model Predictive Control for Multi-Robot Motion Planning](https://mlsanity.com/paper/1909.05150/) (2019)
- [Demonstration-Efficient Guided Policy Search via Imitation of Robust Tube MPC](https://mlsanity.com/paper/2109.09910/) (2021)
- [Deep-PANTHER: Learning-Based Perception-Aware Trajectory Planner in Dynamic Environments](https://mlsanity.com/paper/2209.01268/) (2022)
- [Efficient Multi-Agent Trajectory Planning with Feasibility Guarantee using Relative Bernstein Polynomial](https://mlsanity.com/paper/1909.10219/) (2019)
- [Meeting-Merging-Mission: A Multi-robot Coordinate Framework for Large-Scale Communication-Limited Exploration](https://mlsanity.com/paper/2109.07764/) (2021)
- [PANTHER: Perception-Aware Trajectory Planner in Dynamic Environments](https://mlsanity.com/paper/2103.06372/) (2021)
- [Perception-aware time optimal path parameterization for quadrotors](https://mlsanity.com/paper/2005.13986/) (2020)
- [Robust MADER: Decentralized and Asynchronous Multiagent Trajectory Planner Robust to Communication Delay](https://mlsanity.com/paper/2209.13667/) (2022)
- [Cooperative Multi-Agent Trajectory Generation with Modular Bayesian Optimization](https://mlsanity.com/paper/2206.00726/) (2022)
- [DREAM: Decentralized Real-time Asynchronous Probabilistic Trajectory Planning for Collision-free Multi-Robot Navigation in Cluttered Environments](https://mlsanity.com/paper/2307.15887/) (2023)
- [PUMA: Fully Decentralized Uncertainty-aware Multiagent Trajectory Planner with Real-time Image Segmentation-based Frame Alignment](https://mlsanity.com/paper/2311.03655/) (2023)
- [High-Speed Motion Planning for Aerial Swarms in Unknown and Cluttered Environments](https://mlsanity.com/paper/2402.19033/) (2024)
- [Robust Trajectory Planning for Spatial-Temporal Multi-Drone Coordination in Large Scenes](https://mlsanity.com/paper/2109.08403/) (2021)
- [Obstacle Avoidance of Resilient UAV Swarm Formation with Active Sensing System in the Dense Environment](https://mlsanity.com/paper/2202.13381/) (2022)
- [Robust MADER: Decentralized Multiagent Trajectory Planner Robust to Communication Delay in Dynamic Environments](https://mlsanity.com/paper/2303.06222/) (2023)
- [Perception-aware receding horizon trajectory planning for multicopters with visual-inertial odometry](https://mlsanity.com/paper/2204.03134/) (2022)
- [Decentralized Multi-Agent Planning for Multirotors: a Fully Online and Communication Latency Robust Approach](https://mlsanity.com/paper/2304.09462/) (2023)
- [MRNAV: Multi-Robot Aware Planning and Control Stack for Collision and Deadlock-free Navigation in Cluttered Environments](https://mlsanity.com/paper/2308.13499/) (2023)

## Abstract (arXiv)

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.