Collision Avoidance in Pedestrian-Rich Environments with Deep Reinforcement Learning (1910.11689v4)

Abstract: Collision avoidance algorithms are essential for safe and efficient robot operation among pedestrians. This work proposes using deep reinforcement (RL) learning as a framework to model the complex interactions and cooperation with nearby, decision-making agents, such as pedestrians and other robots. Existing RL-based works assume homogeneity of agent properties, use specific motion models over short timescales, or lack a principled method to handle a large, possibly varying number of agents. Therefore, this work develops an algorithm that learns collision avoidance among a variety of heterogeneous, non-communicating, dynamic agents without assuming they follow any particular behavior rules. It extends our previous work by introducing a strategy using Long Short-Term Memory (LSTM) that enables the algorithm to use observations of an arbitrary number of other agents, instead of a small, fixed number of neighbors. The proposed algorithm is shown to outperform a classical collision avoidance algorithm, another deep RL-based algorithm, and scales with the number of agents better (fewer collisions, shorter time to goal) than our previously published learning-based approach. Analysis of the LSTM provides insights into how observations of nearby agents affect the hidden state and quantifies the performance impact of various agent ordering heuristics. The learned policy generalizes to several applications beyond the training scenarios: formation control (arrangement into letters), demonstrations on a fleet of four multirotors and on a fully autonomous robotic vehicle capable of traveling at human walking speed among pedestrians.

• The paper presents a deep reinforcement learning framework that efficiently mitigates collisions in crowded pedestrian scenarios.
• It employs advanced simulation environments to train agents, enabling robust navigation in diverse and dynamic urban settings.
• Results highlight significant safety improvements and underscore the approach's potential for autonomous navigation systems.

Overview of "Preparation of Papers for IEEE ACCESS"

The paper "Preparation of Papers for IEEE ACCESS" is a methodological guide designed to assist authors in the preparation and submission of manuscripts using \LaTeX for IEEE ACCESS publications. It serves as both a template for \LaTeX users and a comprehensive instruction set for authors irrespective of their preferred word processing tool. The document emphasizes adherence to specific IEEE standards to ensure coherence and consistency in published manuscripts.

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In summarizing, this paper acts as an essential resource for researchers and authors intent on contributing to IEEE ACCESS, ensuring that their scientific communication meets the high standards expected within the field of electronics and engineering.