###### Abstract  
To effectively enhance the adaptability of earthquake rescue robots in dynamic environments and complex tasks, there is an urgent need for an evaluation method that quantifies their performance and facilitates the selection of rescue robots with optimal overall capabilities. In this paper, twenty-two evaluation criteria are proposed based on a comprehensive review of existing evaluation criteria for rescue robots across various domains. The evaluation criteria are tested using the test modules developed by the National Earthquake Response support service, obtaining the corresponding values for each criterion. Then, the weights of the criterion layer and comprehensive evaluation index are determined based on the analytical hierarch process and trapezoidal fuzzy number complementary judgment matrix, and a new consistency test method is proposed. The qualitative evaluation and quantitative analysis are effectively combined to overcome the subjective influence of expert decision-making. Additionally, the performance of three earthquake search and rescue robots is comprehensively evaluated and ranked using the improved radar chart method as an empirical example. Finally, the robustness of the ranking results is examined using a weight sensitivity analysis. The results of the sensitivity analysis demonstrate the effectiveness and feasibility of the proposed method, thereby providing valuable insights for developing multi-objective optimization control strategies and structural designs for earthquake search and rescue robots. Keywords:improve fuzzy analytic hierarchy process (IFAHP); evaluation criteria; comprehensive evaluation; improved radar chart; earthquake search and rescue robots; sensitivity analysis +
Footnote †: journal: Medical  
Additionally, there is limited literature available on exploring the hierarchical structure of performance evaluation indexes specifically designed for rescue robots. Therefore, further research is required to develop an evaluation approach for rescue robots. The selection of an appropriate evaluation method for performance indexes is beneficial for analysts and evaluators in efficiently evaluating alternatives and determining the optimal alternative [6]. When selecting multiple earthquake rescue robots, it is essential to consider the conflicting performance indexes. For instance, during the evaluation of various indexes, certain robots may opt to enhance the mass-volume ratio in order to improve the motion stability of the rescue robot. However, this increase in ratio might have a detrimental impact on obstacle clearance performance when confronted with narrow or low-height rescue environments. Therefore, it is necessary to address this issue by employing a multi-criteria decision-making (MCDM) approach that incorporates various factors [7]. The process of multi-criteria decision-making consists of several stages, including (1) defining objectives, (2) selecting the criteria for measuring those objectives, (3) identifying alternatives, (4) assigning weights to the selected criteria, (5) utilizing a suitable mathematical algorithm to rank the alternatives [8]. Numerous methods have been suggested in existing literature and employed to tackle various MCDM issues [9]. The technique for order preference by similarity to ideal solution (TOPSIS) is a multi-attribute decision-making method based on distance measurement, which calculates the distance between each attribute and the ideal solution, as well as the distance between each attribute and the anti-ideal solution. The relative preference of each attribute is determined by calculating the weighted sum of these two distances, and the optimal alternative can be determined [10]. However, when the information in the environment is incomplete or inaccurate, TOPSIS technology may not fully consider these uncertain factors, resulting in inaccurate decision results. Cluster analysis, a robust method widely employed in MCDM, efficiently uncovers the underlying rules and structures within datasets.