Techniques and Algorithms for Autonomous Vehicles in Forest Environment

Sammanfattning: This thesis describes an ongoing project of which the purpose is designing and developing techniques and algorithms for autonomous off-road vehicles. The focus is on some of the components necessary to accomplish autonomous navigation, which involves sensing and moving safely along a user-defined path in a dynamic forest environment. The work is part of a long-term vision in the forest industry of developing an unmanned shuttle that transports timber from the felling area to the main roads for further transportation. A new path-tracking algorithm is introduced and demonstrated as superior to standard algorithms, such as Follow the Carrot and Pure Pursuit. This is accomplished by using recorded data from a path-learning phase. By using the recorded steering angle, the curvature of the path is automatically included in the final steering command. Localization is accomplished by a neural network that fuses data from a Real-Time Kinematic Differential GPS/GLONASS, a gyro, and wheel odometry. Test results are presented for path tracking and localization accuracy from runs conducted on a full-sized forest machine. A large part of the work has been design and implementation of a general software platform for research in autonomous vehicles. The developed algorithms and software have been implemented and tested on a full-size forest machine supplied by our industrial partner Komatsu Forest AB. Results from successful field tests with autonomous path tracking, including obstacle avoidance, are presented.