Nonmyopic Informative Path Planning Based on Global Kriging Variance Minimization

Remote, hazardous, and extreme exploration missions require robots to be equipped with on-board sensors for rich and heterogeneous information during deployment. In such tasks, path planning can directly affect the quality and quantity of the observations obtained under temporal and energetic constraints. While most informative path planners can only plan for a short horizon ahead of time (referred as to myopic planners), we propose a novel planner that is capable of planning global paths in which the nonmyopic exploration efficiency is optimized. To achieve this, a novel sampling algorithm named MPE is proposed to adaptively sample landmarks that are associated with high information capacity, in order to minimize the global Kriging variance. The traverse path for the landmarks is then obtained by the IPP-MPE algorithm for minimizing the overall traveling cost. The algorithm is flexible enough to be applied to various information acquisition tasks. The tractable computational cost allows the horizon to be long enough for scene coverage. The algorithm was deployed on a real robot for accomplishing tactile based object searching tasks, which shows superior efficiency compared to the myopic planner baseline. Last, the complexity and other theoretical analysis of the algorithm is provided.

Automated summary

The paper proposes a novel planner that optimizes nonmyopic exploration efficiency in remote, hazardous, and extreme exploration missions. The algorithm incorporates a new sampling algorithm and path optimization algorithm to achieve global path planning and minimize traveling cost. Experimental results demonstrate superior efficiency compared to myopic planners.