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A novel palpation-based incision detection strategy in the laryngeal region,potentially for robotic tracheotomy, is proposed in this letter. A tactilesensor is introduced to measure tissue hardness in the specific laryngealregion by gentle contact. The kernel fusion method is proposed to combine theSquared Exponential (SE) kernel with Ornstein-Uhlenbeck (OU) kernel to figureout the drawbacks that the existing kernel functions are not sufficientlyoptimal in this scenario. Moreover, we further regularize exploration factorand greed factor, and the tactile sensor's moving distance and the robotic baselink's rotation angle during the incision localization process are consideredas new factors in the acquisition strategy. We conducted simulation andphysical experiments to compare the newly proposed algorithm - RescalingAcquisition Strategy with Energy Constraints (RASEC) in trachea detection withcurrent palpation-based acquisition strategies. The result indicates that theproposed acquisition strategy with fusion kernel can successfully localize theincision with the highest algorithm performance (Average Precision 0.932,Average Recall 0.973, Average F1 score 0.952). During the robotic palpationprocess, the cumulative moving distance is reduced by 50%, and the cumulativerotation angle is reduced by 71.4% with no sacrifice in the comprehensiveperformance capabilities. Therefore, it proves that RASEC can efficientlysuggest the incision zone in the laryngeal region and greatly reduced theenergy loss.

RASEC: Rescaling Acquisition Strategy with Energy Constraints under  SE-OU Fusion Kernel for Active Trachea Palpation and Incision Recommendation  in Laryngeal Region