The current state of robotics development involves the discovery of incredibly effective computing algorithms that can, in fact, tackle complicated issues. The efficiency of all these is indeed an efficient data structure; it would therefore be for robots their basic apparatus for them to organize, gain access to, and manipulate data. Arrays, linked lists, trees, graphs, hash tables, and heaps aid in system optimization, memory conservation, and real-time decision-making. This paper examined the importance of data structure for robotics by clarifying its respective applications in different activities such as robotic navigation, sensor data processing, and behavioural modelling using AI. By using these examples, it shows how different structures have a distinct impact on the speed or time of operation and accuracy, especially in environments where resources might be constrained and dynamic. In addition to these practical uses, trade-offs related to specific data structures are discussed, taking into account task specificity, efficiency objectives, and system limitations when making a decision. Well-documented examples from real-world situations are provided to demonstrate how carefully considering structural choices can greatly enhance a robot's performance. The paper concludes by outlining future research directions in data structures to meet the demands of intelligent, constantly-adaptive robotic systems. This shows how important these topics are to the robotics research of the next generation.

Robotics, AI, Computing Algorithms