Abstract

Exploration, military, and humanitarian efforts call for robots capable of navigating extreme terrain. Lacking the combination of computational efficiency and understanding of robot/terrain dynamics, existing algorithms are unable to answer this call. Future robots that reliably and rapidly move over extreme terrain will generate new scientific discoveries, expand the bounds of field operations and secure a safer world. Existing navigation algorithms do not provide this capability; most lack the ability to reason about the robot/terrain interaction with sufficient fidelity. Algorithms capable of reasoning with sufficient fidelity operate too slowly and require more information about the environment than is generally available. Probabilistic planning approaches address some of the computational problems associated with high degree of freedom manipulators, but are not yet applicable to the domain of mobile robots operating with incomplete knowledge. This work explores randomized planning in conjunction with physical modeling to provide real time, sufficient fidelity plans for traversing extreme terrain. Randomized planning is introduced to address both the real time and high dimensionality aspects of the problem. Physical modeling

Citations