LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance

TL;DR

This paper introduces LDP, a local diffusion-based robot navigation method that combines diverse data training and global observation augmentation to improve collision avoidance and robustness in complex environments.

Contribution

The paper proposes a novel Local Diffusion Planner (LDP) that integrates global observations with diffusion models for enhanced robot navigation and collision avoidance.

Findings

LDP achieves effective collision avoidance in diverse scenarios.

Augmenting LDP with global observations reduces local optima trapping.

Diverse data generation improves diffusion model training for navigation.

Abstract

The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of policies. The intricate nature of real-world scenarios, characterized by dynamic obstacles and maze-like structures, underscores the complexity of robot local navigation decision-making as a conditional distribution problem. Nevertheless, leveraging the diffusion model for robot local navigation is not trivial and encounters several under-explored challenges: (1) Data Urgency. The complex conditional distribution in local navigation needs training data to include diverse policy in diverse real-world scenarios; (2) Myopic Observation. Due to the diversity of the perception scenarios, diffusion decisions based on the local perspective of robots may prove suboptimal for completing the entire task, as they often lack foresight. In certain scenarios requiring detours, the robot may become trapped. To address these issues, our approach begins with an exploration of a diverse data generation mechanism that encompasses multiple agents exhibiting distinct preferences through target selection informed by integrated global-local insights. Then, based on this diverse training data, a diffusion agent is obtained, capable of excellent collision avoidance in diverse scenarios. Subsequently, we augment our Local Diffusion Planner, also known as LDP by incorporating global observations in a lightweight manner. This enhancement broadens the observational scope of LDP, effectively mitigating the risk of becoming ensnared in local optima and promoting more robust navigational decisions.