The prevalence of autonomous robotic solutions has increased interest in multi-robot system connection and collaboration. Industry 4.0-driven manufacturing innovation will streamline production, improve quality, and sustain growth with robotic sensors. This paper presents a decision-making framework for optimizing robotic sensors in dynamic Industry 4.0 environments by introducing a Complex Hesitant Fuzzy Soft Set (CHFSS), as an extension of hesitant fuzzy sets and soft sets. The research aims to improve uncertainty and ambiguity management by increasing flexibility and authenticity with the complex plane. By assigning hesitating membership values in a multi-parameter complex unit plane, CHFSS is better equipped to demonstrate and handle challenging scenarios that occur throughout the decision-making process. A score function to sort the comparability issues between CHFSSs is introduced. Schweizer–Sklar’s operations and prioritized arithmetic and geometric aggregation operators are proposed. Their theorems and properties are developed and verified. The classical TODIM (acronym of the Portuguese expression TOmada de Decisão Interativa e Multicritério) approach is not able to deal with prioritized weight vectors. Thus, a novel CHFS-TODIM approach based on prioritized weight vectors is presented. The geometrical interpretation of the CHFS-TODIM approach under Schweizer–Sklar aggregation operators and prioritized weight vectors is depicted. A case study that focuses on the incorporation of robotic sensors is presented. Another goal of this case study is to show the approach’s precision, resilience, and flexibility. Additionally, it tests the viability and efficacy of the generated operators. The advantages and merits of the CHFS-TODIM approach are elaborated on.