Aug 2026· Machines· Vol 14, pp. 950· 0 citations· 207 references
TL;DR
This work surveys how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0, and treats autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning.
Abstract
Reviews of artificial intelligence (AI) for mobile robots usually cover one competence—perception, SLAM, path planning, control, or reinforcement learning—and rarely show how these combine into a working system. We take the opposite view and treat autonomy as one pipeline: sensing, perception, localization and mapping, prediction, planning, visual servoing and control, high-level decision-making, and continual learning. We survey how AI has reshaped each stage for industrial and service robots across Industry 4.0, 5.0, and the emerging Industry 6.0. Using a structured, PRISMA-informed protocol with explicit search strings, inclusion criteria, and cross-embodiment transfer rules, we screen the literature, analyze a corpus drawn mainly from the last five years, and position it against prior surveys with a coverage matrix that exposes their single-block focus. Four findings stand out. Perception and localization approach engineering maturity through multimodal fusion and foundation vision models. Planning and control stay effective but computationally demanding. Decision-making, now driven by large language and vision–language–action models, is powerful yet unverifiable and fails under safety constraints. Lifelong learning is almost absent from deployed systems. The decisive weaknesses sit at the interfaces: at the perception–planning, planning–control, and control–decision handoffs the sim-to-real gap, limited on-robot compute, and scarce industrial data compound. We compare AI families by technology readiness, catalog datasets and benchmarks, examine the safety-certification barrier, and consolidate cross-cutting gaps. We close with a staged roadmap toward Industry 6.0 and a next-generation architecture coupling a foundation perception backbone, a world model and digital twin, a continual-learning memory, and a reasoning core wrapped by a safety monitor. The aim is to move from cataloging algorithms to engineering integrated autonomy.
The integration of computer vision and intelligent decision systems has completely changed the field of robot technology, enabling autonomous systems to perceive, reason and perform actions in complex environments. This paper reviews the evolution of robot decision-making methods from traditional rule-based methods to contemporary deep learning and Multi-Agent Reinforcement learning paradigms. Three types of decision-making frameworks are systematically analyzed: rule driven systems that rely on predefined logical conditions, deep learning methods that use neural networks for end-to-end strategy learning, and multi-agent systems that coordinate collective behavior through distributed intelligence. The results show that although the rule-based method provides interpretability and security, the deep learning method performs well in dealing with high-dimensional sensory input and an unstructured environment. In addition, Multi-Agent Reinforcement Learning shows great potential in the application of cooperative robots. This review identifies the current limitations, including sample efficiency, generalization ability and real-time performance constraints, and emphasizes future research directions such as hybrid architectures that combine the advantages of different methods.
Sheng-Hao Chen· ITM Web of Conferences· 0 citations
This review provides a comprehensive synthesis of traditional and artificial intelligence (AI)-based techniques across the complete autonomous UAV navigation pipeline, including environmental perception, localization and mapping, path planning and obstacle avoidance, and motion control, together with commonly used datasets, simulation platforms, and evaluation practices.
T. Mahmood, Ali Ahmed Mirza· Scientific Journal of Engine...· 0 citations
Flexible manufacturing, characterized by high-mix, low-volume, and highly variable production, demands robotic systems with strong adaptability, dexterity, and intelligence that conventional offline-programmed industrial robots cannot provide. This paper presents a systematic review of key technologies for robot embodied intelligence oriented toward flexible manufacturing, organized around the closed loop of perception, decision-making, and execution. The purpose is to clarify the current research landscape, identify core technical bottlenecks, and outline future directions for embodied-intelligent manufacturing. Adopting a literature-analysis and comparative-review method, the study examines representative advances at three levels: multimodal environmental perception and real-time modeling, flexible adaptive precision manipulation, and intelligent decision-making for process planning and scheduling. The review finds that multimodal fusion and semantic SLAM are overcoming perception bottlenecks, that deep learning and force/position hybrid control are balancing flexible adaptability with high-precision operation, and that deep reinforcement learning and large models are advancing intelligent process planning. It concludes that data scarcity, model reliability, software-hardware integration, and ethical-legal standards remain the principal challenges to large-scale industrial deployment.
Zheng-Yang Chen· Advances in Engineering Inno...· 0 citations
Currently, most commercial robots rely on fixed programs to perform repetitive tasks. The visual modules equipped on these devices have limited anti-interference capabilities, making it difficult to adapt to complex structures and variable environments, which in turn limits the practical effectiveness of robotic intelligence deployment. Drawing from hands-on experience in robot debugging and project implementation, this paper explores the integration of computer vision and embodied intelligence. Based on common application scenarios such as industrial sorting, power inspection, and intelligent services, it analyzes specific methods by which visual technologies assist robots in environmental perception, intelligent decision-making, motion adjustment, and model updating. The study identifies several common challenges encountered during industry deployment, including insufficient stability in recognizing complex scenes, difficulty balancing computational power with recognition accuracy, significant gaps between simulation training and real-world conditions, and a lack of unified application standards across industries. Addressing these practical issues, the paper proposes feasible improvement strategies from four perspectives: algorithm enhancement, computational power upgrade, simulation optimization, and the establishment of industry standards. The conclusions drawn from this research offer practical insights for advancing robot vision technology, expanding application scenarios, and promoting standardized development within the industry.
Zi-Chen Wang· Journal of Computer Science...· 0 citations
Deployable autonomy remains a key challenge for unmanned aerial vehicles (UAVs) operating in open-ended missions. Large language models (LLMs) and their multimodal variants, which can process visual and other sensory inputs, have introduced new capabilities for semantic perception, task reasoning, and language-conditioned control. However, these capabilities do not by themselves produce flight-ready autonomy. We structure our analysis around a Perception–Cognition–Action (P–C–A) framework. At each layer, we identify the capabilities contributed by LLM-based components and examine how they connect to existing flight modules through input specifications, output representations, architectural coupling patterns, and safety mechanisms. Across the surveyed systems, LLMs extend UAV autonomy beyond fixed perception categories, scripted task plans, and pre-programmed controllers. However, field deployment depends on whether model outputs can be transformed into representations that downstream modules can parse, verify, and safely execute. Without adequate validation, captions, task plans, code, waypoints, and control commands may become failure points that propagate across the P–C–A loop. Our analysis highlights structured output contracts, independent safety barriers, and deterministic fallback mechanisms as key design elements for the reliable integration of LLM capabilities into UAV platforms.
Ting-Quan Xiong, Jianning Zhan, Qiu-Wei Deng et al.· Drones· 0 citations
Autonomous mobile robots require reliable coordination among navigation, perception, tracking, and precision approach modules to complete indoor object-search missions. Existing systems often remain fragmented, treating navigation, detection, tracking, and docking as separate tasks rather than as an end-to-end pipeline. This study has two objectives: to validate a fault-tolerant coordination architecture for autonomous search-and-approach behavior and to compare two search strategies under controlled indoor target-position scenarios. The primary contribution is a methodological integration framework based on a Finite State Machine (FSM) that coordinates ROS 2 Navigation2 global navigation, YOLO11n object detection, centroid tracking, and Image-Based Visual Servoing (IBVS), while managing transitions among navigation, visual servoing, recovery, and mission-completion states. A quantitative Gazebo simulation experiment used 40 controlled trials to compare Random Exploration and Waypoint-Based Search. The integrated system achieved a 100% mission success rate without command conflicts, indicating effective FSM-based coordination between global navigation and local visual control. Waypoint-Based Search was more efficient when the target was aligned with predefined nodes, achieving a mean detection time of 50.55 s compared with 164.80 s for Random Exploration. Conversely, Random Exploration performed better when the target was away from predefined paths, reducing mean detection time to 87.00 s compared with 183.64 s. Fault-tolerant behavior was demonstrated in simulation through successful mission completion despite repeated LiDAR-triggered obstacle-recovery events during visual approach. These findings show that search efficiency depends on alignment between exploration design and spatial structure, not universal strategy superiority.
W. Ahmad, C. Dewa· bit-Tech· 0 citations
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