Urban environments face increasing complexity due to rapid population growth, expanding infrastructure, and dynamic socio-economic demands. AI-driven autonomous systems and robotics have emerged as transformative technologies for smart cities, enabling intelligent, adaptive, and efficient management of urban services. These systems enhance mobility through autonomous vehicles and drone-based logistics, optimize infrastructure performance via integrated IoT sensors and robotic interventions, and improve public safety through AI-enabled surveillance and emergency response. Advanced algorithms support real-time decision-making, predictive maintenance, and adaptive control, facilitating resilient and sustainable urban operations. Ethical, privacy, and social acceptance challenges influence the deployment of autonomous systems, requiring robust frameworks, transparency, and human-centric design strategies. This chapter presents a comprehensive overview of applications, operational mechanisms, and challenges associated with AI-driven robotics in urban contexts, highlighting opportunities for future research and sustainable implementation in next-generation smart cities.
The rapid pace of urbanization has created unprecedented challenges for cities worldwide, including congestion, pollution, energy demand, and increased pressure on public services [1]. Traditional urban management strategies often rely on manual operations and fragmented systems, limiting the ability to respond effectively to dynamic urban conditions [2]. The integration of Artificial Intelligence (AI) and robotics offers transformative solutions for these challenges, enabling smart cities to operate with higher efficiency, reliability, and adaptability [3]. Autonomous systems, encompassing self-driving vehicles, drones, and service robots, provide advanced capabilities for real-time monitoring, decision-making, and execution of complex tasks across multiple urban domains [4]. The convergence of AI algorithms, sensor networks, cloud computing, and IoT infrastructure forms the backbone of intelligent urban systems capable of perceiving, analyzing, and responding to environmental and social dynamics. The adoption of such technologies enhances the sustainability and resilience of urban environments, supporting both economic and societal development [5].
Urban mobility represents one of the most critical domains benefiting from AI-driven autonomous systems [6]. Traffic congestion, inefficient public transport networks, and high accident rates contribute to economic losses and environmental degradation. Autonomous vehicles leverage AI for navigation, obstacle detection, predictive maintenance, and optimized routing, reducing travel time and energy consumption [7]. Drone-based systems provide flexible logistics and delivery solutions, minimizing dependence on conventional transportation modes while alleviating road congestion [8]. Intelligent traffic management platforms utilize real-time data from connected vehicles, road sensors, and environmental monitors to optimize traffic flow and enhance safety [9]. Coordination among autonomous vehicles and urban infrastructure, including smart intersections and signal systems, ensures synchronized operations and mitigates the risk of accidents. Such integration of mobility technologies promotes seamless transportation networks while contributing to emission reduction and sustainable urban development [10].
Infrastructure management has emerged as another area where AI-driven robotics play a pivotal role [11]. Urban systems, including energy grids, water distribution networks, and public facilities, require continuous monitoring, maintenance, and optimization [12]. IoT sensors embedded in critical infrastructure collect real-time data on system performance, environmental conditions, and resource utilization. Robotics platforms equipped with these sensors can autonomously perform inspections, detect anomalies, and execute targeted maintenance tasks, ensuring reliability and minimizing operational downtime [13]. Predictive analytics enabled by AI algorithms allows proactive identification of potential failures, efficient resource allocation, and data-driven decision-making [14]. The integration of robotics and IoT supports sustainable practices by optimizing energy consumption, reducing waste, and extending the lifespan of infrastructure components. Such intelligent urban systems facilitate resilience against environmental stresses, population growth, and evolving urban demands, enhancing overall city efficiency [15].
