Robotic Mapping, Kinematics, and Trajectory Generation

Designing autonomous robots requires a solid grasp of how they move, perceive their surroundings, and plan their paths. This text-based course guides you through the fundamental mathematics and algorithms behind robotic kinematics, trajectory generation, and spatial mapping. You will transition from understanding basic robotic motion to implementing feedback control loops and processing sensor data to build accurate maps of unknown environments. What you'll learn: - Understand kinematics for both holonomic and non-holonomic robotic systems. - Apply feedback control techniques to solve inverse kinematics problems. - Process multi-dimensional sensor data, including laser range scanners and modern LiDAR inputs. - Generate smooth, collision-free trajectories for robot navigation. - Model and manage uncertainty in robotic mechanisms and sensor measurements. - Explore modern probabilistic mapping concepts used in contemporary autonomous systems. The course begins with core definitions, coordinate systems, and kinematic foundations. From there, you will progress to feedback control, sensor data processing, mapping algorithms, and trajectory generation, building your knowledge systematically through clear written explanations and algorithmic walkthroughs. This course is designed for aspiring robotics engineers, software developers, and students new to robotics navigation, with no advanced prerequisites required. Start reading today to build the foundational skills needed to program intelligent, self-navigating robots.