Design of an Efficient Bioinspired Model for Optimizing Robotic Arm Movements via Ensemble Learning Operations

Robotic arm movements are highly dependent on design and deployment of sensors & actuation devices & their duty cycles. Optimizing current-level duty cycles for these devices can reduce the power consumption, and maximize the efficiency of control for different device operations. Existing duty cycle control models for robotic arms are highly complex, or have lower efficiency levels. To overcome these issues, this text proposes design of an efficient bioinspired model for optimizing robotic arm movements via ensemble learning operations. The arm is built using Arduino controller along with stepper motors, which assist in controlled movements for different arm operations. The proposed model uses Mayfly Optimization (MO) in order to identify duty cycles of different arm components for different movement types. The MO Model uses delay, energy and jitter parameters in order to estimate a fitness function that is optimized in order to identify arm movement sets. These movement sets are classified into performance-aware movements via a combination of Naïve Bayes (NB), k Nearest Neighbours (kNN), Support Vector Machine (SVM), Logistic Regression (LR), and Multilayer Perceptron (MLP) classifiers. Due to which the model is able to reduce the delay needed for control the arms by 8.3%, reduce the energy needed for control operations by 2.9%, and reduce the control jitter by 4.5% under real-time scenarios.