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

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 5614 times faster than optimization-based approaches.

Authors

• Kota Kondo
• Claudius T. Tewari
• Andrea Tagliabue
• Jesus Tordesillas
• Parker C. Lusk
• Mason B. Peterson
• Jonathan P. How

Keywords

• Location awareness
• Visualization
• Navigation
• Simulation
• Neural networks
• Trajectory
• Computational efficiency
• Planning
• Robotics and automation
• Optimization
• Trajectory Planning
• Multi-agent Trajectory
• Neural Network
• Computational Requirements
• Imitation Learning
• Optimization-based Approach
• Collision-free Trajectory
• Expert Demonstrations
• Scalable
• Computation Time
• Short-term Memory
• Dynamic Environment
• Travel Time
• Long Short-term Memory
• Multilayer Perceptron
• Number Of Agents
• Constrained Optimization
• PANTHER
• Fast Computation
• Limited Field Of View
• Position Trajectory
• Multiple Obstacles
• Trajectory Generation
• Violation Rate
• Central Entity
• Dynamic Obstacles