Deep Learning-based Obstacle Detection for Human Interaction Robots: A Review
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Abstract
Obstacle detection is the foundation of autonomous robotics, enabling robots to perceive and understand the world around them to move safely. Deep learning has emerged as one of the driving forces in today’s research, with various algorithms employed for learning and making effective decisions based on vast and complex datasets. In recent years, numerous deep learning methods have been developed and studied to detect obstacles. This paper provides an end-to-end overview of over 40 state-of-the-art deep learning models (from 50 papers) for obstacle detection in human-interacting robots, with a focus on deployment viability, real-time running, and energy efficiency. We also delve into the architecture of deep learning, highlight key challenges in real-world deployment, offer a comparative analysis of basic and advanced deep learning approaches, and examine the trade-offs between accuracy, speed, and power consumption, providing insights into practical considerations. This review categorizes obstacle detection techniques into two groups: Core CNN-based methods and Advanced Deep Learning Methods. Comparisons were made between these two groups, concentrating on computational requirements, deployment feasibility, and hardware configuration. Several key findings emerged. It was determined that models with high accuracy were computationally expensive and unsuitable for embedded deployment. While some models experience accuracy-speed trade-offs, others are limited by hardware constraints and power limitations. Finally, this review concludes with a structured discussion of real-world deployment considerations, prioritizing model efficiency, scalability, and potential future research directions in deep learning-based obstacle detection.
Manuscript received: 30 Jun 2025 | Revised: 28 Jul 2025 | Accepted: 11 Aug 2025 | Published: 30 Nov 2025
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