## 1 Introduction
* Classification based on the approach applied on the data: The WSM (Weighted Sum Model) or SAW (Simple Additive Weighting), WPM (Weighted Product Model), AHP (Analytic Hierarchy Process), revised AHP, ANP (Analytic Network Process), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), ELECTRE (Elimination and Choice Translating Reality), PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations); VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje), and LINMAP (Linear Programming for Multidimensional Analysis of Preference) are the methods which are of more use in practice [17; 18; 22]. In addition, MCDM plays a significant role in many intelligent systems and is widely utilized in many fields such as operation research and soft computing [21; 23]. Moreover, regarding the links between MCDM and robotics, it is mostly utilized for the allocation of resources and task scheduling in robotic swarms [24], robot selection in automated industry operations [25], and defining exploration strategies for search and rescue robots [26]. Similarly, in this paper, MCDM will be applied to choose between navigation strategies defined by given tiling sets. Here, we will be focused on single decision-making deterministic MCDM applying concepts of the SAW (Simple Additive Weighting) method [22]. In summary, all aspects of this research are quite novel. In better words, all aspects of it have recently emerged: The mechanical design of the Tetris-inspired cleaning robot is proposed in 2017 [11; 12] and the path-planning idea is raised in 2018 in terms of a tiling theory approach [13; 14]. However, the previously raised tiling-path planning approach only considers area coverage as a single performance metric with absolutely no consideration of energy consumption issues. The main contributions of this study in comparison to our previous works are as follows:  
* In this study, energy consumption is considered as a second significant performance metric in tiling path planning. * This paper proposes an energy estimation scheme for the novel self-reconfigurable robotic platform. * The current work suggests the optimal path-planning approach by means of MCDM, i.e., creating an optimal balance between maximum area coverage and minimum energy. In other words, however the tiling-theoretic path planning approach is introduced in [13; 14], this paper goes beyond previous works to some degree by taking into account the energy factor and aims at adding some optimality utilizing MCDM. * In addition, in this study, a more recent tiling theorem has been utilized that is not investigated in previous studies [13; 14] and surprisingly, based on simulation results, this specific theorem (here illustrated as Theorem 1 [27]) turns out to be the most-promising tiling theorem that can best serve all the areas targeted for optimal cleaning. On the whole, _tiling-path-planning_ is a novel concept only related to the novel hTetro, and it is our belief that expanding a novel idea (by considering more parameters) and adding optimality to a novel concept is invaluable. The rest of the paper is organized as follows. Section 2 introduces the experimental environment and explains the utilized Tetris-inspired robot structure. Section 3 casts light on the concepts of Polyomino Tiling Theory applied to our robotic platform and elaborates the four utilized tiling theorems. Moreover, this section is also concerned with the reason (and also technique) for the area decomposition process and finally presents five distinguished tiling sets under each theorem applied on each sub-area. Section 4 is concerned with explaining the method of extracting the data for energy and area coverage as the two decision criteria. Section 5 clarifies the general concepts of a MCDM method called Simple Additive Weighing (SAW) and then applies concepts of this method on the tiling-based path planning for the robot by means of defining a special fitness function.