An Approach to Model Interest for Planetary Rover through Dezert-Smarandache Theory

TL;DR

This paper presents a novel method for autonomous goal prioritization in planetary rovers by fusing sensor data with Dezert-Smarandache Theory to generate interest maps, enabling adaptive scientific exploration with minimal human input.

Contribution

It introduces a new application of Dezert-Smarandache Theory for interest level estimation and autonomous goal reallocation in planetary rover missions.

Findings

Interest maps effectively guide goal selection.

The approach handles conflicting and vague data.

Autonomous goal reallocation improves exploration efficiency.

Abstract

In this paper, we propose an approach for assigning an interest level to the goals of a planetary rover. Assigning an interest level to goals, allows the rover autonomously to transform and reallocate the goals. The interest level is defined by data-fusing payload and navigation information. 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 Dezert-Smarandache Theory of Plausible and Paradoxical Reasoning was used for information fusion: this theory allows dealing with vague and conflicting data. In particular, it allows us directly to model the behavior of the scientists that have to evaluate the relevance of a particular set of goals. The paper shows an application of the proposed approach to the generation of a reliable interest map.