<![CDATA[The current assistive robotics platforms tend to be unable to keep up with the unpredictable nature of renewable sources of energy, the lack of processing capacity, and the growing security risks. In this work, the integrated control architecture is introduced where the aspects of energy management, computation efficiency, and interaction reliability are considered in one and lightweight framework. The study paper contribution is the creation of a single architecture that integrates adaptive fuzzy energy control with multi-channel eye-gaze communication and IoT-based security, tailored to compute economically and resource-limited microcontroller processor designs. The approach combines the Adaptive Fuzzy Energy Manager (AFEM) of real-time power operation, Multi-Modal Control Interface (MMCI) based on electrooculography (EOG) in addition to the depth estimation of vision, and Lightweight Secure Communication Layer (LSCL) that employs 128-bit permutation-based encryption. The experimental test of the system was performed with a robotic arm of the 4-DOF with 150W PV-battery unit powered by an Arduino Mega. Experimental results demonstrate that the overall accuracy of EOG command classification was high at 97.8% and the location of the end-effector was positioned precisely with an error of less than 2 mm. AFEM values were sufficient to ensure state-of-charge of the battery was not less than 88 percent under varying sun irradiance and security layer was maintained to give low processing latency of less than 5 ms. The suggested framework is highly operational efficient and also interaction reliable rendering it applicable to deploy in the resource constrained resources in assistive and telemedicine settings.]]>
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