Underwater robotics has advanced fastest in its individual capabilities. Learned optical enhancement, sonar interpretation, and tightly coupled multimodal fusion each report steady gains on dedicated benchmarks, yet whether those gains translate into reliable operation under deployment constraints remains largely untested. We examine this question for confined inland waterways, where narrow geometry, persistent turbidity, dense engineered structures, and GNSS denial jointly decide what a robot can and cannot do. The Pinglu Canal in Guangxi, China, serves as the reference scenario, and the analysis is organized around a scenario-centric evaluation framework rather than an isolated capability survey. Across perception, multimodal fusion, SLAM, planning, communication, and platform design, we assess each technology for its functional adequacy under canal-specific conditions, then map the resulting capabilities onto bathymetric survey, infrastructure inspection, hazard detection, environmental monitoring, and lifecycle maintenance. The reviewed evidence points to one conclusion. No reviewed technology is adequate on all three axes of the scoring framework applied here, and no reviewed study demonstrates an integrated system operating under the combined canal constraints. The gap is system-level integration rather than algorithmic maturity. Each identified failure originates in a single module, escapes detection at that module’s own output, and becomes observable only after corrupting the layers above it. Reliable operation depends on adaptive multimodal perception paired with structure-relative localization and persistent mapping, a requirement that transfers directly to ports, bridges, tunnels, and other infrastructure-dense waterways.
Pei-Jun Shi, C. Chow, W. Wong· Journal of King Saud Univers...· 0 citations
Quadruped robots have attracted increasing attention because they can traverse uneven terrain, support field deployment, and perform tasks that are difficult for wheeled or tracked platforms. Recent advances in artificial intelligence (AI) have further expanded their capabilities from manually designed gait control toward learning-based locomotion, perception-aware adaptation, dynamic motion skills, autonomous recovery, manipulation, energy-aware operation, fault diagnosis, and human–robot interaction. However, the literature on AI-driven quadruped robotics is distributed across diverse technical topics, robot platforms, validation settings, and performance metrics, making it difficult to assess the maturity and practical value of different approaches. To address this need, this review provides an AI-centered and deployment-oriented overview of quadruped robotics. A systematic literature search was conducted using Web of Science, IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink, covering studies published approximately from 2000 to 2025. After screening and eligibility assessment, 287 studies were included for detailed review. The review first examines AI-driven locomotion, including reinforcement learning, non-RL machine-learning methods, model-based approaches, and hybrid strategies, with attention to robustness, sim-to-real transfer, sensor use, computational requirements, and hardware validation. It then summarizes AI-supported advanced behaviors, including jumping, fall prevention and recovery, and object manipulation, focusing on reported quantitative performance, impact management, and reliability. Finally, it discusses system-level topics that affect real-world deployment, including fault diagnosis, energy-efficient control, shared autonomy, trust-aware and explainable interaction, and safety-aware human–robot collaboration. By organizing the literature according to robot capabilities, validation maturity, and deployment challenges, this review helps clarify the current progress, limitations, and future directions of AI-driven quadruped robots.
Li-Kai Wu, C. Chow, W. Wong et al.· Frontiers in Neurorobotics· 0 citations
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