Authors: Shathushan Sivashangaran, Apoorva Khairnar, Azim Eskandarian
Published on: February 07, 2024
Impact Score: 8.22
Arxiv code: Arxiv:2402.05066
Summary
- What is new: A novel method for training Deep Reinforcement Learning (DRL) for autonomous mobile robots (AMRs) navigation in unknown environments without maps, leading to efficient real-world application.
- Why this is important: Operating AMRs in dynamic, GPS-denied environments without prior maps, using conventional methods, is computationally inefficient and lacks generalization for wide deployment.
- What the research proposes: The paper introduces an efficient DRL training method for AMRs to learn navigation in new, map-less environments with dynamic obstacles, using a compact network architecture, enabling zero-shot transfer to the real world.
- Results: The DRL model with the compact architecture outperformed traditional navigation algorithms in efficiency and adaptability, demonstrating successful navigation in unstructured terrain and dynamic obstacle avoidance with reduced computational resources.
Technical Details
Technological frameworks used: Deep Reinforcement Learning (DRL)
Models used: 2 fully connected layers with 64 nodes each
Data used: Simulation data for training without the need for extensive real-world data collection and labeling
Potential Impact
Agriculture, manufacturing, disaster response, military, space exploration sectors, and companies in the realm of autonomous vehicles and robotics.
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