ROUGH-TERRAIN ROBOT MOTION PLANNING BASED ON TOPOLOGY AND TERRAIN CONSTITUTION

We present a randomised potential-field based motion planning approach for rough-terrain mobile robot navi- gation. The motion planning component is closely integrated with a real-time capable rigid-body kinematics and dynamics simulation module. The terrain is approximated as a polygonal surface generated from height data. The robot interacts with the terrain, modelled as a series of physically accurate collisions between its geometry and the polygons of the terrain. This model includes physical material properties, such as coefficients of restitution, static and dynamic friction of the underground as well as the robot parts. For each polygon of the terrain, a different set of material properties can be specified to represent variations in the underground constitution. In real rough-terrain settings it is typically not possible to accurately define a binary obstacle area, therefore we have included a continuous obstacle model. Each point of the terrain has a degree of "obstacleness" associated with it. This coefficient depends on the local terrain geometry and material properties. When navigating on the terrain, some areas of the workspace might be unreachable for the robot. Importantly, these areas need not a priori be specified as obstacles, but are a consequence of the physics-based robot-terrain interaction model. A motion plan is generated by performing a randomised search on a regular discreet grid potential computed over the workspace of the robot. Since no binary obstacle area exists, each point of the terrain contributes to the repulsive obstacle potential according to its obstacle coefficient. The computed motion plan allows the robot to navigate through the terrain taking into consideration not only the topology but also the physical underground properties. A major difficulty in rough-terrain robot motion planning lies in the uncertainties of the underground constitution. In real-world settings it is hard to determine the spatial distribution of multiple physical properties of the terrain, controlling them is even harder. A further difficulty lies in the repeatability of experiments. It often proves challenging to recreate the same conditions for multiple algorithm test runs without introducing errors due to the terrain characteristics or robot configuration having changed. To lessen the impact of these issues, we use a real-time simulation environment for researching rough-terrain motion planning algorithms. In this simulation, we create a physically accurate model of the terrain. Based on CAD data, robot geometries can be imported and complemented with actuator simulation components. Having direct control over the terrain topology and its physical properties has allowed us to design rough-terrain motion planning algorithms which take full advantage of this additional information. In this publication we present the underlying idea as well as a concrete application. Typically, it is not possible to determine binary obstacle areas in rough- terrain settings. Therefore, we propose to use continuous obstacle definitions based both on terrain topology and physical underground properties. Such definitions are of general interest in rough-terrain motion planning, but due to the affinity of the continuous obstacle function with a potential, they fit naturally into a potential-field approach. The implementation of such a system is also presented in this publication.