Currently, the European Union (EU) is focusing on a large-scale campaign dedicated to developing a competitive circular economy and expanding the single digital market. One of the main goals of this campaign is the implementation of the sustainability principles in the development and deployment cycle of the new generation technologies. This paper focuses on the fast-growing field of autonomous mobile robots and the harsh environment exploration problem. Currently, most state-of-the-art navigation methods are utilising the idea of evaluating candidate observation locations by combining different task-related criteria. However, these map building solutions are often designed for operating in near-perfect environments, neglecting such factors as the danger to the robot. In this paper, a new strategy that aims to address the safety and re-usability of the autonomous mobile agent by implementing the economic sustainability principles is proposed. A novel multi-criteria decision-making method of Weighted Aggregated Sum Product Assessment—Single-Valued Neutrosophic Sets, namely WASPAS-SVNS, and the weight selection method of Step-Wise Weights Assessment Ratio Analysis (SWARA) are applied to model a dynamic decision-making system. The experimental evaluation of the proposed strategy shows that increased survivability of the autonomous agent can be observed. Compared to the greedy baseline strategy, the proposed method forms the movement path which orients the autonomous agent away from dangerous obstacles.

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.

Unknown environment exploration by an autonomous robot is a complex problem that requires robustness and reliability of the applied exploration strategy. Currently, a common approach to autonomous exploration is to incrementally increase the robot's knowledge about the environment by directing it to the regions which border currently unexplored areas (frontiers). However, deciding where to move next when multiple alternatives are present introduces an additional layer of complexity. As such, a decision might require balancing the competing high-level objectives (for example, visiting several priority locations while also reducing the robot travelled distance). This research proposes a novel environment exploration strategy and the extension for the WASPAS multi-criteria decision making (MCDM) method, modelled under the m-generalised q-neutrosophic environment, namely, WASPAS-mGqNS. The proposed method is applied to address the problem of selecting the next frontier that the exploring robot should reach. Case study results highlight how the proposed approach could be applied to minimise the robot-travelled distance and maximise the observed environment when the robot is tasked to visit several priority locations set in advance by the robot operator.

Path planning can be considered the most vital task of the autonomous robot. In this task, selecting an optimal route from the starting to the target position becomes an important problem that must be addressed when multiple competing optimization priorities are considered. Thus, a novel route assessment strategy based on a multi-criteria decision-making approach is proposed. The m-generalized q-neutrosophic PROMETHEE (PROMETHEE-mGqNS) method is applied to aggregate the competing route assessment requirements and choose an optimal route. A case study is investigated to explain the proposed strategy for path planning in a typical environment and indicates the method stability when incomplete input data characteristics are present.

Serious games together with the gamified and the game-based surveys (GBS), offer an engaging way to increase citizens’ participation in urban planning projects. However, there is always the risk of untrustworthy participants, which can decrease the overall reliability of the game-based research. Trustworthiness analysis is a highly challenging task since the neuropsychology of the GBS respondents and the infinite amount of their possible in-game actions causes many uncertainties in the data analysis. The novel MCDM approach PROMETHEE-mGqNN (PROMETHEE under m-generalised q-neutrosophic numbers) is proposed in this paper as the solution to the described problem. Five criteria that might be automatically calculated from the in-game data are proposed to construct the decision matrix to identify the untrustworthy respondents. The game-based survey “Parkis” developed to assess the safety and attractiveness of the urban public park “Missionary Garden” (Vilnius, Lithuania) is proposed as the case study of this research. By applying the proposed methodology, we calculated the trustworthy index value and noticed that it is capable of detecting the behavioural tendencies of the GBS players.