| Autonomous ground vehicles (AGVs) are commonly used for search and rescue, military and forestry purposes. To safely and efficiently perform missions in these environments the AGV must adapt its driving and control strategies based on the traversed terrain. A paradigm for achieving this type of control is to use a terrain classification algorithm to identify the traversed terrain and update the control mode when a new terrain is encountered. Although terrain classification can be performed using vision sensors, the area of terrain classification that has needed the most development is classification using proprioceptive sensors, which detect the internal state of the vehicle. The purpose of this dissertation is to describe at length recent advancements in terrain classification using proprioceptive sensors, including many that are centered around the author's personal research. The discussion starts with the fundamentals and physics behind vibration-based terrain classification, the most proven means of terrain classification using proprioceptive sensors. This physical understanding is then shown to lead to the use of frequency domain features, extracted from measured vehicle vibrations, to distinguish the underlying terrain. By borrowing techniques and concepts from the computer science field of pattern recognition this identification can be automated and performed in real-time. Using comparisons, the author details the benefits of several different pattern recognition classifiers using performance metrics based on accuracy and computational speed. This pattern recognition discussion ultimately leads to a better understanding of how automated terrain classification can be implemented on AGVs.;Perhaps the most difficult problem to address before effectively implementing online terrain classification using proprioceptive sensors is that of speed and load dependency, which is characterized by the need to train classification algorithms based on vehicle speed or load. This means that a large amount of empirical data must be collected in order to ensure the classification algorithm will be accurate. After addressing why these problems occur, it is shown that a vehicle model along with the measured vehicle vibrations can in theory be used to describe the terrain in a way that is independent of both vehicle speed and load. Alternatively, interpolation techniques may reduce the impact of speed and load dependency, while enabling the use of sensor modalities beyond vehicle vibrations. As a terrain classification system is only beneficial to vehicle control when used in coordination with terrain-dependent driving modes, methods are demonstrated to handle both the online and offline cooperation of these systems. Offline, the terrain classification algorithm can be trained to reduce the likelihood of implementing a control mode with settings drastically different from that of the ideal control mode. Online cooperation can be accomplished by using terrain classification to switch between control modes via an update rule that is both sensitive to terrain transitions and robust to misclassifications made by the terrain classification algorithm. |
