Mobile Robot Programming : Adventures in Python and C

This book emphasizes software design as the most important topic in modern robotics and demonstrates practical code examples in Python and C. The book introduces the free simulation system EyeSim in combination with EyeBot robots, which can be built from inexpensive embedded processors, sensors and motors – or by adapting the control inputs of model cars. EyeSim is a free software for MacOS, Windows and Linux, which uses a realistic physics simulation engine and is source-code compatible to the EyeBot mobile robots. So, each robot program can first be tested on the simulator before running it on a real robot. EyeSim includes modules for driving, walking, swimming and diving robots, as well as for robot manipulators. EyeSim also runs on the Meta/Oculus Quest, providing a fully immersive robotics experience in virtual reality. Beginning with simple driving algorithms and sensor data processing for distance sensors, Lidar and camera, the book progresses to more complex localization and navigation tasks, as well as vision-based navigation and genetic algorithms. It concludes with artificial intelligence applications for mobile robots in traffic scenarios and full-size autonomous vehicles. This book is suitable as a text for undergraduate and graduate courses in Robotics, Automation and Artificial Intelligence, as well as a self-study guide for practitioners and hobbyists. All robot application programs in this book are available as free downloads for MacOS, Windows, Linux, and Raspberry Pi OS. PREFACE CONTENTS 1 ROBOT HARDWARE 1.1 Actuators 1.2 Sensors 1.3 Processor and I/O 1.4 Complete Robot 1.5 Communication 1.6 User Interface 1.7 Simulation 1.8 Tasks 2 ROBOT SOFTWARE 2.1 Software Installation 2.2 First Steps in Python 2.3 First Steps in C 2.4 Driving a Square in Python 2.5 Driving a Square in C or C++ 2.6 SIM Scripts and Environment Files 2.7 Display and Input Buttons 2.8 Distance Sensors 2.9 Camera 2.10 Robot Communication 2.11 Multitasking 2.12 Using an IDE 2.13 Virtual Reality 2.14 Tasks 3 DRIVING ALGORITHMS 3.1 Random Drive 3.2 Driving to a Target Position 3.3 Turn and Drive Straight 3.4 Circle 3.5 Dog Curve 3.6 Splines 3.7 Tasks 4 LIDAR SENSORS 4.1 Lidar Scans 4.2 Corners and Obstacles 4.3 Tasks 5 ROBOT SWARMS 5.1 Setting up a Swarm 5.2 Follow Me 5.3 Multiple Followers 5.4 Tasks 6 WALL FOLLOWING 6.1 Wall Following Algorithm 6.2 Simplified Wall Following Program 6.3 Tasks 7 ALTERNATIVE DRIVE SYSTEMS 7.1 Ackermann Steering 7.2 Omni-directional Drives 7.3 Skid-Steering 7.4 Chain Drives and Terrain 7.5 Tasks 8 BOATS AND SUBMARINES 8.1 Autonomous Boats 8.2 Autonomous Submarines 8.3 Simulating Boats and Submarines 8.4 Submarine Diving 8.5 Submarine Movement 8.6 Tasks 9 MAZES 9.1 Micromouse 9.2 Wall Following 9.3 Robustness and Control 9.4 Maze Driving with Lidar 9.5 Recursive Maze Exploration 9.6 Flood-Fill 9.7 Shortest Path 9.8 Tasks 10 NAVIGATION 10.1 Navigation in Unknown Environments 10.2 DistBug Algorithm 10.3 Navigation in Known Environments 10.4 Quadtrees 10.5 Quadtree Implementation 10.6 Shortest Path Algorithm 10.7 Tasks 11 ROBOT VISION 11.1 Camera and Screen Functions 11.2 Edge Detection 11.3 OpenCV 11.4 Color Detection 11.5 Motion Detection 11.6 Tasks 12 LEARNING ROBOTS 12.1 Starman 12.2 Motion Model 12.3 Genetic Algorithms 12.4 Evolution Run 12.5 Tasks 13 TRAFFIC MODELS 13.1 Autonomous Model Car Competitions 13.2 Carolo-Cup 13.3 Lane Keeping 13.4 Intersections and Zebra Crossings 13.5 Traffic Sign Recognition 13.6 End-to-End Learning 13.7 Tasks 14 AUTONOMOUS CARS 14.1 Electric Drive System 14.2 Drive by Wire 14.3 Sensors and Safety Systems 14.4 Formula-SAE Autonomous 14.5 Formula-SAE Simulation 14.6 Autonomous Road Vehicles 14.7 Tasks 15 OUTLOOK APPENDIX