This article considers the problem of motion planning in the unknown environment, where terrain features and goal positioning data are used for navigation. The described control algorithm for path finding with the use of terrain-following motion is based on reactive collision avoidance methods, but also involves a strong deliberative component as well as consideration of kinematic and dynamic constraints of the autonomous mobile agent. That way common pitfalls such as generating impossible paths, losing the goal, and getting stuck in the local minima are avoided, whereas the necessary ability to react quickly to changes in the environment is ensured. Terrain-acquiring sensor model constitutes an important part of the described navigation algorithm since processing of sensor data determines agent's behavior, for example, whether it tends to choose low-lying terrain areas vs. passing above the hills, or favors close-to-horizontal motion. The implemented terrain-acquiring sensor model is consistent with the simplified model of rotating laser rangefinder/ LIDAR, where terrain vision process is discrete, and could be viewed as "snapshot-based ray-tracing". Influence of sensor parameters on the character of motion is studied and acceptable parameter ranges are determined. The equations of motion are derived using Udwadia-Kalaba Equation, thus, obtained control force is always minimized. Case studies, illustrating different behavior types and resulting paths, are presented.
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