Foundations of Autonomous Robots: Navigation and Control Algorithms

Autonomous robots are reshaping industries from logistics to self-driving vehicles, but understanding how they make decisions can feel overwhelming. This text-based course demystifies the core algorithms that allow robots to perceive their environment, plan paths, and execute precise movements. You will transition from understanding basic robotic components to designing the logic that drives autonomous decision-making. By studying structured explanations and pseudocode, you will grasp how robots navigate complex environments safely and efficiently. What you'll learn: Understand foundational robotic terminology, coordinate systems, and kinematic models; Apply path planning algorithms such as A* and Dijkstra to find optimal routes; Configure localization and mapping frameworks, including modern SLAM concepts; Analyze sensor fusion techniques to combine data from LIDAR, cameras, and IMUs; Explore modern robotics middleware architectures, including ROS 2 concepts and node communication; Design basic control loops to guide a robot smoothly along a planned path. The course begins with essential terminology and the math behind robot kinematics before progressing to perception, localization, and decision-making algorithms. You will work through detailed text explanations and practical pseudocode examples to solidify your understanding of robotic software architecture. This course is designed for beginners in computer science, engineering, or software development with no prior robotics experience required. Start reading today and build a solid foundation in the software that powers the physical world.