Bridging the Intelligence–Efficiency Gap: A Critical Review of Obstacle Avoidance in Human-Interactive Robots
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Abstract
Obstacle avoidance is considered a critical capability of Human Interaction Robot operating in dynamic shared environments with humans. Traditional and deep learning-based methods from 2019-2025 have been systematically reviewed in this paper to evaluate their applicability, performance, and real-world deployability in HIRs. Traditional approaches such as Bug algorithms, Vector Field Histogram (VFH+), and Dynamic Window Approach (DWA) provide real-time operation and low computational demands but lack adaptability to complex, human-centric scenarios. In contrast, deep learning techniques, including CNNs, LSTMs, and Deep Reinforcement Learning (DRL) deliver superior perception and adaptability yet impose high computational requirements that hinder edge deployment. Comparative analysis reveals that only about 18% meet the criteria of edge friendliness, while more than 59% require GPU support. However, gaps persist regarding sim-to-real transfer (over 70% sim testing only), benchmarks like BDD100K, and social compliance metrics’ under-measurement. The contribution is threefold: (1) the integration of HRI challenges with the capabilities and limitations of DL; (2) the taxonomy and comparison of the state of the art, emphasizing the intelligence-efficiency gap in terms of accuracy, latency, and edge feasibility; and (3) the roadmap of HRI research, proposing the benefits of hybrid modular architectures, hardware-aware design, and explainability of AI systems. Ultimately, the future of HIR navigation will be driven by a hybrid approach that combines lightweight AI vision systems and strong rule-based control architectures.
Manuscript received: 08 Feb 2026 | Revised: 27 Mac 2026 | Accepted: 26 Apr 2026 | Published: 31 Jul 2026
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