Mathematical Modelling and Hybrid Optimization of Robotic Arm Trajectories for Minimally Invasive Carcinogenic Interventions

Abstract

Carcinogenesis, the process by which normal cells undergo genetic and molecular alterations to form malignant tumors, necessitates precise, minimally invasive interventions for research and therapeutic purposes. In this study, we introduce the HEVC-OTPA framework—a novel hybrid optimization and mathematical modeling paradigm designed to refine robotic arm trajectory planning with heightened precision, robustness, and operational safety, particularly in biologically sensitive and high-risk environments associated with carcinogenic research applications. By synergistically combining hybrid expansion algorithms, Δ-clearance enforcement protocols, and real-time trajectory smoothing within the constraints of advanced kinematic and dynamic modeling, the proposed methodology significantly mitigates mechanical stress while enhancing navigational accuracy. Comparative analyses reveal a 17% decrease in path length and a 32% acceleration in computation time relative to conventional A* strategies, alongside a 33.7% reduction in node expansions and a 41% decline in curvature variance—factors critical to minimizing wear and optimizing instrument longevity. Importantly, the enforced safety margins are increased by over 100%, thereby drastically lowering the risk of inadvertent collisions during delicate manipulations, such as tissue sampling or targeted therapeutic interventions. The system achieves a remarkable 96.8% success rate, surpassing previous benchmarks by more than 10%, with tracking accuracy improvements of up to 42%. These enhancements foster real-time adaptability, reducing energy expenditure by 22% and extending device lifespan—parameters that are crucial in sustained biomedical operations. The mathematical rigor underlying HEVC-OTPA is corroborated through extensive simulations and experimental trials, positioning it as a transformative tool bridging computational robotics and precision-driven medical applications. The framework offers a robust foundation for next-generation robotic systems, empowering safer, more efficient interventions in carcinogenesis research and therapeutic domains.