A non-deterministic planner for planetary rover autonomy

Ceriotti, Matteo and Vasile, Massimiliano and Giardini, Giovanni and Massari, Mauro (2006) A non-deterministic planner for planetary rover autonomy. In: AIAA/AAS Astrodynamics Specialist Conference and Exhibit, 2006-08-21 - 2006-08-24.

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Autonomy is an important feature for space systems, especially for planetary exploration rovers. Furthermore, for every rover activity, there are intrinsic uncertainties on activity duration, position of the rover, and other environment characteristics that affect each operation, like soil condition, dust on solar panels, temperature, etc.: disregarding them during planning would bring unreliable plans, that are likely to fail. In this paper, a novel, non-deterministic planning approach for autonomous planetary exploration rovers will be presented. Uncertainties in modeling the surrounding environment and in the input from sensors are integrated in the planning process in order to make the rover activity more reliable and to prevent failures. For each plan created by a planner a measure of reliability is computed and used to predict and select the safest one. The evaluation of the plan has been performed with the Dempster-Shafer Theory of Evidence, that allows to deal with both aleatory and epistemic uncertainties. Moreover the rover has been endowed with the capability of reallocating its goals. By data-fusing payload and navigation information, gathered by the rover during its mission, assigns interest values to the existing goals or generates new goals.. The fusion yields an 'interest map,' that quantifies the level of interest of each area around the rover. In this way the planner can choose the most interesting scientific objectives to be analyzed, with limited human intervention, and reallocates its goals autonomously. The novel Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning has been used for information fusion: this theory allows to deal with vague and conflicting data. Finally the paper shows some applications of the proposed approach to the generation of reliable plans. These tests demonstrate how the planner is able to generate plans that maximize at the same time reliability and the level of interest.