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Key research themes
1. How can computational intelligence and adaptive methods improve autonomous robot navigation and control in dynamic, uncertain environments?
This theme investigates the use of computational intelligence (CI) approaches, such as neural networks, fuzzy logic, evolutionary algorithms, and learning-based adaptive systems, to address core robotics challenges including localization, path planning, obstacle avoidance, and controller tuning. These methods aim to provide robots with the capability to operate robustly and autonomously in complex, partially unknown, or dynamic environments where traditional algorithmic approaches fail or become inefficient. Understanding and quantifying the adaptability and intelligence of control systems in managing uncertainties is also central to this area.
Key finding: This chapter systematically reviews CI techniques applied to autonomous mobile robotics, emphasizing the roles of neural networks, fuzzy logic, and evolutionary computation in navigation tasks such as localization, path... Read more
Key finding: The paper surveys intelligent control methods including adaptive control, fuzzy logic, neural networks, and reinforcement learning applied in robotics and mechatronics. It underscores machine learning algorithms’... Read more
Key finding: Introduces a novel taxonomy quantifying the intelligence of control systems based on their capacity to handle uncertainties related to environment, controller, and goals. This work connects the degree of intelligence of... Read more
Key finding: Presents a Best First Search (BFS) based intelligent PID tuning algorithm that adapts control parameters for trajectory tracking in mobile robots without requiring an explicit system model. This data-driven tuning approach... Read more
Key finding: Develops an autonomous robot integrating supervised machine learning and fuzzy decision systems to monitor environmental conditions and adapt motor speed based on sensed data including light, temperature, and gas levels. It... Read more
2. What architectural and integration strategies best enable intelligent behavior in robots for complex perception, planning, and human-robot interaction?
This theme centers on the synthesis of AI and Robotics through architectural frameworks that support robust perception, planning, reasoning, and seamless human-robot communication. It reflects on historical divergences and recent trends towards integrated AI and robotics systems, highlighting middleware, layered control architectures, symbolic and reactive approaches, and natural language understanding as key enablers for robots operating in real-world, domestic, and service environments.
Key finding: Analyzes the historical divergence and contemporary reunion of AI and Robotics, underscoring the need for integrated approaches combining perception, reasoning, and actuation. The paper argues for interdisciplinary research... Read more
Key finding: Discusses the architectural frameworks in robotics contrasting deliberative, reactive, and hybrid control systems, noting challenges in creating timely and adaptive robot behavior in dynamic worlds. The paper stresses the... Read more
Key finding: Describes the ViRbot layered architecture integrating AI planning with reactive behaviors using Conceptual Dependency (CD) representations for natural language understanding and task execution in service robots. Demonstrates... Read more
Key finding: Details the hybrid ViRBot system combining virtual simulation and real robot layers to afford perception, planning, knowledge management, and execution in service robotics. Emphasizes the importance of modular architectures... Read more
3. How are intelligent systems and robotics transforming service applications and industrial automation through interdisciplinary integration?
This theme investigates how mechatronics, intelligent control methods, and automation are synergistically applied to revolutionize sectors such as healthcare, logistics, manufacturing, and public services. It explores the integration of mechanical, electrical, control, and computational technologies in mechatronic systems and their ethical, safety, and socio-economic implications for service robotics and smart automation.
Key finding: Provides an extensive overview of interdisciplinary mechatronics engineering integrating mechanical, electrical, and software components for service robotics applications. Presents case studies in healthcare, agriculture, and... Read more
Key finding: Examines broad application domains of AI and robotics including finance, manufacturing, and security, emphasizing the growing autonomy of robotic systems driven by big data and learning algorithms. Analyzes socio-economic... Read more
Key finding: Introduces a strategic funding initiative by EPSRC and industrial partners targeting autonomous intelligent systems research, including sensor exploitation, planning, verification, and adaptive model building. Emphasizes... Read more
Key finding: Surveys fundamental motivations and benefits driving automation adoption such as increased productivity, safety, labor cost reduction, and quality improvement. Highlights the crucial role of automation technologies including... Read more
Key finding: Explores the synergy of expert systems with robotics to embed human-like reasoning, knowledge representation, and problem-solving capabilities into mechanical systems. Reviews expert system types, characteristics, and... Read more