1.4: Challenges of Mobile Autonomous Robots

Being able to stitch sensor information together to map the environment just by counting your own steps and orienting yourself by using distinct features of the environment is known as Simultaneous Localization and Mapping (SLAM). The key challenge here is that the length of the steps you take are uncertain (a wheeled robot might slip or have slightly differently sized wheels, e.g.) and it is not possible to recognize places with 100% accuracy (not even for a human). In order to be able to implement something like the last algorithm on a real robot, we will therefore need to understand

• How does a robot move? How does rotation of its wheels affect its position and speed in the world?
• How do we have to control the wheel-speed in order to reach a desired position?
• What sensors exist for a robot to perceive its own status and its environment?
• How can we extract structured information from a vast amount of sensor data?
• How can we localize in the world?
• How can error be represented and how can we reason in the face of uncertainty?

In order to answer these questions, we will rely on trigonometry, linear algebra, and probability theory. Specific concepts that will be used throughout this book are basic trigonometry, matrix notation, Bayes’ formula, and the concept of probability distributions. You will see that robotics is actually a great vehicle to add meaning to these concepts!

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