Abstract
In this research, a novel adaptive frontier-assessment-based environment exploration strategy for search and rescue (SAR) robots is presented. Two neutrosophic WASPAS multi-criteria decision-making (MCDM) method extensions that provide the tools for addressing the inaccurate input data characteristics are applied to measure the utilities of the candidate frontiers. Namely, the WASPAS method built under the interval-valued neutrosophic set environment (WASPAS-IVNS) and the WASPAS method built under the m-generalised q-neutrosophic set environment (WASPAS-mGqNS). The indeterminacy component of the neutrosophic set can be considered as the axis of symmetry, and neutrosophic truth and falsity membership functions are asymmetric. As these three components of the neutrosophic set are independent, one can model the input data characteristics applied in the candidate frontier assessment process, while also taking into consideration uncertain or inaccurate input data obtained by the autonomous robot sensors. The performed experiments indicate that the proposed adaptive environment exploration strategy provides better results when compared to the baseline greedy environment exploration strategies.
Highlights
The application of autonomous robots in search and rescue (SAR) missions, can enable the rescue teams to collect on-scene information about the disaster site, without risking the safety of human personnel [1]
The increased robot travel distance in the 3rd environment is considered insignificant, when the proposed environment exploration strategy is compared to the baseline Standard Information Gain (SIG) strategy, and the decrease is significant in the 1st environment
The proposed adaptive candidate frontier assessment strategy which applies the neutrosophic WASPAS-IVNS and WASPAS-m-generalized q-neutrosophic set (mGqNS) methods is evaluated in the simulated SAR environments
Summary
The application of autonomous robots in search and rescue (SAR) missions, can enable the rescue teams to collect on-scene information about the disaster site, without risking the safety of human personnel [1]. If no a priori information about the exploration environment is known, a common approach to this problem is to apply the greedy next-best-view strategies, that interpret the robot-constructed map to determine a set of candidate locations within the partly explored search space and choose the one that should be visited by the robot [5]. By applying these strategies, the decision on where the robot should move is made on the go and depend only on the current state of the robot and the known environment information
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