Low latency adversarial threat avoidance, navigation in 3D point cloud environment via multi-agent reinforcement learning in a correspondence 2D floorplan

Adrian Mai; Mark Bilinski; Raymond Provost

doi:10.1117/12.2587964

12 April 2021 Low latency adversarial threat avoidance, navigation in 3D point cloud environment via multi-agent reinforcement learning in a correspondence 2D floorplan

Adrian Mai, Mark Bilinski, Raymond Provost

Author Affiliations +

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461H (2021) https://doi.org/10.1117/12.2587964
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Multi-agent reinforcement learning (MARL) has transformed research and development in robotics especially for navigation purposes. Using deep learning, multi-agent coordination, reinforcement learning can solve critical tasks such as finding shortest paths or avoiding obstacles with exceptional speed. However, given a LiDAR point cloud structure, performing such tasks directly using MARL, can be computationally expensive and cumbersome due to massive nature of 3D point clouds. In this work, we leverage 2D (MARL), path planning and obstacle avoidance to obtain a low latency navigation in 3D though a correspondence 2D floor plan. We change the environment dynamically with adversarial threats such as fire or leakage -- the RL agents can quickly find a new path given the learned environment. The method shows strong performances in both throughput and latency.

Conference Presentation

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Adrian Mai, Mark Bilinski, and Raymond Provost "Low latency adversarial threat avoidance, navigation in 3D point cloud environment via multi-agent reinforcement learning in a correspondence 2D floorplan", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461H (12 April 2021); https://doi.org/10.1117/12.2587964

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