Abstract
The application of autonomous robots in search and rescue missions represents a complex task which requires a robot to make robust decisions in unknown and dangerous environments. However, imprecise robot movements and small measurement errors obtained by robot sensors can have an impact on the autonomous environment exploration quality, and therefore, should be addressed while designing search and rescue (SAR) robots. In this paper, a novel frontier evaluation strategy is proposed, that address technical, economic, social, and environmental factors of the sustainable environment exploration process, and a new extension of the weighted aggregated sum product assessment (WASPAS) method, modelled under interval-valued neutrosophic sets (IVNS), is introduced for autonomous mobile robots. The general-purpose Pioneer 3-AT robot platform is applied in simulated search and rescue missions, and the conducted experimental assessment shows the proposed method efficiency in commercial and public-type building exploration. By addressing the estimated measurement errors in the initial data obtained by the robot sensors, the proposed decision-making framework provides additional reliability for comparing and ranking candidate frontiers. The interval-valued multi-criteria decision-making method combined with the proposed frontier evaluation strategy enables the robot to exhaustively explore and map smaller SAR mission environments as well as ensure robot safety and efficient energy consumption in relatively larger public-type building environments.
Highlights
Nowadays, autonomous robot systems, such as industrial robots [1], autonomous cars [2], social [3] and service robots [4], are increasingly applied to solve real-life problems, and represent a constant object of discussion, from the technical, and from social and ethical perspectives [5]
Considering the iterative frontier-based environment exploration approach, and a heavily criteria-based nature of the problem, we argue that multi-criteria decision-making (MCDM) frameworks can be applied to improve robots decision-making module
To address the problem of the small errors produced by imprecise robot movements and imperfect sensor readings, we propose a new formulation of weighted aggregated sum product assessment (WASPAS) method, modelled under interval-valued neutrosophic set environment, namely WASPAS-interval-valued neutrosophic sets (IVNS)
Summary
Autonomous robot systems, such as industrial robots [1], autonomous cars [2], social [3] and service robots [4], are increasingly applied to solve real-life problems, and represent a constant object of discussion, from the technical, and from social and ethical perspectives [5]. Growing autonomous robots decision-making capabilities enable such systems to replace humans in labour-intense and dangerous tasks, such as infrastructure maintenance and inspection [6,7], or environment exploration and data gathering tasks, such as search and rescue missions [8,9]. Search and rescue missions are complex tasks in which autonomous robots must safely explore the disaster sites and provide rescue teams with important information, such as victim locations and status, environmental conditions, and the locations of dangerous objects [8,10,11]. This research is aimed at improving the decision-making module, which is responsible for sensor-obtained data interpretation and conversion to expected environment exploration behaviour in search and rescue missions
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