Generative AI-Assisted Robot Task Planning in Industrial Applications

AI-Based Adaptive Motion Planning for Autonomous Robotic Systems

Autonomous robotic systems are transforming industries such as automation, healthcare, transportation, defense, and intelligent services. A major challenge in robotics is motion planning in dynamic and uncertain environments, where robots must navigate safely and efficiently. Traditional algorithms like Dijkstra’s, A*, Probabilistic Road Maps, and Rapidly Exploring Random Trees perform well in static environments but struggle with moving obstacles, sensor uncertainty, and real-time decision-making. Artificial Intelligence (AI) has improved robotic motion planning through adaptive learning, predictive decision-making, reinforcement learning, fuzzy logic, neural networks, and evolutionary optimization. These techniques enable robots to learn from their environment and optimize navigation over time. This paper reviews AI-based adaptive motion planning methods developed before 2019 and examines their role in path optimization, obstacle avoidance, localization, and decision-making. It also proposes a hybrid framework combining sensor fusion, environment mapping, fuzzy inference systems, and reinforcement learning for real-time path optimization. Simulation results demonstrate that AI-based adaptive planning achieves better navigation accuracy, obstacle avoidance, computational efficiency, and environmental adaptability compared to traditional methods.

Review of key technologies for robot embodied intelligence oriented toward flexible manufacturing

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.

Survey on Embodied AI for Industry: From the Perspective of Industrial Robots

The quantified requirements of industrial robots enabled by EAI4I are analyzed and recent research progress is reviewed, covering core technologies for single- and multi-robot systems, dedicated hardware platforms, high-fidelity simulators, task-specific datasets, representative industrial application scenarios, and critical deployment challenges.

Autonomous Robotics with Vision-Language AI for Industrial Inspection and Maintenance

Experimental evaluation demonstrates significant improvements in inspection accuracy, fault classification, decision-making, maintenance prediction, reduced downtime, and lower human intervention, providing a foundation for next-generation intelligent industrial automation.

Multi-Agent Reinforcement Learning for Cooperative Manipulation in Industrial Robotics: A Systematic Review of Trends, Gaps and Research Drivers

Modern manufacturing faces increasing demands for flexibility, customization, and productivity under dynamic conditions. Multi-robot systems offer a promising solution by enabling cooperative execution of complex tasks, such as assembly and cooperative manipulation. In this context, Multi-Agent Reinforcement Learning (MARL) has emerged as a promising paradigm to enhance coordination and adaptability in industrial settings. MARL enables multiple agents to learn and interact in shared environments to achieve common goals within complex and dynamic industrial processes. In this paper, a deep analysis of MARL applied to industrial multi-robot systems based on a systematic review is presented, with particular focus on cooperative manipulation tasks. Following PRISMA guidelines, we analyze a total of 30 articles published between 2016 and 2026, selected independently by two of the authors from an initial pool of 102 records retrieved from Scopus and Web of Science. These articles were used to address five key questions regarding MARL algorithms, control architectures, industrial applications and validation practices. These research questions seek to examine gaps and trends at the research level which are important for the development of multi-agent control technologies. This review shows a clear prevalence of model-free algorithms under Centralized Training with Decentralized Execution (CTDE) architectures, with validation mainly performed in simulation. Despite promising results and high potential for impact, critical gaps remain in scalability, reproducibility, and sim-to-real transfer, limiting real deployment in manufacturing environments. To address these challenges and fill current gaps, we outline actionable research directions, such as hybrid MARL approaches, standardized industrial benchmarks, digital twin pipelines, and safety-aware deployment strategies, to accelerate MARL adoption in industrial environments.

A multimodal foundation model-enabled agent for human–robot collaboration in construction